qiskit_machine_learning.algorithms)", "Circuit library for machine learning applications (qiskit_machine_learning.circuit.library)", "Connectors (qiskit_machine_learning.connectors)", "Datasets (qiskit_machine_learning.datasets)", "Quantum kernels (qiskit_machine_learning.kernels)", "Quantum Kernel Algorithms", "Quantum neural networks (qiskit_machine_learning.neural_networks)", "Utility functions and classes (qiskit_machine_learning.utils)", "Loss Functions (qiskit_machine_learning.utils.loss_functions)", "Getting started", "Qiskit Machine Learning overview", "Qiskit Machine Learning v0.5 Migration Guide", "Qiskit Machine Learning Migration Guide", "Release Notes", "QiskitMachineLearningError", "BinaryObjectiveFunction", "MultiClassObjectiveFunction", "NeuralNetworkClassifier", "NeuralNetworkRegressor", "ObjectiveFunction", "OneHotObjectiveFunction", "PegasosQSVC", "QBayesian", "QSVC", "QSVR", "SerializableModelMixin", "TrainableModel", "VQC", "VQR", "QNNCircuit", "RawFeatureVector", 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+Search.setIndex({"alltitles": {"0.2.0": [[14, "release-notes-0-2-0"]], "0.3.0": [[14, "release-notes-0-3-0"]], "0.4.0": [[14, "release-notes-0-4-0"]], "0.5.0": [[14, "release-notes-0-5-0"]], "0.6.0": [[14, "release-notes-0-6-0"]], "0.7.0": [[14, "release-notes-0-7-0"]], "0.8.0": [[14, "release-notes-0-8-0"]], "1. Classification": [[58, "1.-Classification"]], "1. Differences between a QCNN and CCNN": [[63, "1.-Differences-between-a-QCNN-and-CCNN"]], "1. Exploratory Data Analysis": [[55, "1.-Exploratory-Data-Analysis"]], "1. Global vs. Local Effective Dimension": [[62, "1.-Global-vs.-Local-Effective-Dimension"]], "1. Introduction": [[53, "1.-Introduction"], [56, "1.-Introduction"], [57, "1.-Introduction"], [63, "1.-Introduction"], [65, "1.-Introduction"]], "1. Prepare a dataset": [[61, "1.-Prepare-a-dataset"]], "1. What is an Autoencoder?": [[64, "1.-What-is-an-Autoencoder?"]], "1.1 Classical Convolutional Neural Networks": [[63, "1.1-Classical-Convolutional-Neural-Networks"]], "1.1. Kernel Methods for Machine Learning": [[56, "1.1.-Kernel-Methods-for-Machine-Learning"]], "1.1. Quantum vs. Classical Bayesian Inference": [[65, "1.1.-Quantum-vs.-Classical-Bayesian-Inference"]], "1.1. Quantum vs. Classical Neural Networks": [[53, "1.1.-Quantum-vs.-Classical-Neural-Networks"]], "1.1. qGANs for Loading Random Distributions": [[57, "1.1.-qGANs-for-Loading-Random-Distributions"]], "1.2 Quantum Convolutional Neural Networks": [[63, "1.2-Quantum-Convolutional-Neural-Networks"]], "1.2. Implementation in qiskit-machine-learning": [[53, "1.2.-Implementation-in-qiskit-machine-learning"], [65, "1.2.-Implementation-in-qiskit-machine-learning"]], "1.2. Kernel Functions": [[56, "1.2.-Kernel-Functions"]], "1.3. Quantum Kernels": [[56, "1.3.-Quantum-Kernels"]], "2. Classification": [[56, "2.-Classification"]], "2. Components of a QCNN": [[63, "2.-Components-of-a-QCNN"]], "2. Data and Representation": [[57, "2.-Data-and-Representation"]], "2. How to Instantiate QBI": [[65, "2.-How-to-Instantiate-QBI"]], "2. How to Instantiate QNNs": [[53, "2.-How-to-Instantiate-QNNs"]], "2. Regression": [[58, "2.-Regression"]], "2. The Effective Dimension Algorithm": [[62, "2.-The-Effective-Dimension-Algorithm"]], "2. The Quantum Autoencoder": [[64, "2.-The-Quantum-Autoencoder"]], "2. Train a model and save it": [[61, "2.-Train-a-model-and-save-it"]], "2. Training a Classical Machine Learning Model": [[55, "2.-Training-a-Classical-Machine-Learning-Model"]], "2.1 Convolutional Layer": [[63, "2.1-Convolutional-Layer"]], "2.1. Create Rotations for the Bayesian Networks": [[65, "2.1.-Create-Rotations-for-the-Bayesian-Networks"]], "2.1. Defining the dataset": [[56, "2.1.-Defining-the-dataset"]], "2.1. EstimatorQNN": [[53, "2.1.-EstimatorQNN"]], "2.1.1. Two Node Bayesian Network Example": [[65, "2.1.1.-Two-Node-Bayesian-Network-Example"]], "2.1.2. Burglary Alarm Example": [[65, "2.1.2.-Burglary-Alarm-Example"]], "2.2 Pooling Layer": [[63, "2.2-Pooling-Layer"]], "2.2. Create a Quantum Circuit for the Bayesian Networks": [[65, "2.2.-Create-a-Quantum-Circuit-for-the-Bayesian-Networks"]], "2.2. Defining the quantum kernel": [[56, "2.2.-Defining-the-quantum-kernel"]], "2.2. SamplerQNN": [[53, "2.2.-SamplerQNN"]], "2.2.1 Two Node Bayesian Network Example": [[65, "2.2.1-Two-Node-Bayesian-Network-Example"]], "2.2.2. Burglary Alarm Example": [[65, "2.2.2.-Burglary-Alarm-Example"]], "2.3. Classification with SVC": [[56, "2.3.-Classification-with-SVC"]], "2.4. Classification with QSVC": [[56, "2.4.-Classification-with-QSVC"]], "2.5. Evaluation of models used for classification": [[56, "2.5.-Evaluation-of-models-used-for-classification"]], "3. Basic Example (SamplerQNN)": [[62, "3.-Basic-Example-(SamplerQNN)"]], "3. Clustering": [[56, "3.-Clustering"]], "3. Components of a Quantum Autoencoder": [[64, "3.-Components-of-a-Quantum-Autoencoder"]], "3. Data Generation": [[63, "3.-Data-Generation"]], "3. Definitions of the Neural Networks": [[57, "3.-Definitions-of-the-Neural-Networks"]], "3. How to Run Rejection Sampling": [[65, "3.-How-to-Run-Rejection-Sampling"]], "3. How to Run a Forward Pass": [[53, "3.-How-to-Run-a-Forward-Pass"]], "3. Load a model and continue training": [[61, "3.-Load-a-model-and-continue-training"]], "3. Training a Quantum Machine Learning Model": [[55, "3.-Training-a-Quantum-Machine-Learning-Model"]], "3.1 Define QNN": [[62, "3.1-Define-QNN"]], "3.1. Defining the dataset": [[56, "3.1.-Defining-the-dataset"]], "3.1. Definition of the quantum neural network ansatz": [[57, "3.1.-Definition-of-the-quantum-neural-network-ansatz"]], "3.1. Set up": [[65, "3.1.-Set-up"]], "3.1. Set-Up": [[53, "3.1.-Set-Up"]], "3.1.1 Two Node Bayesian Network Example": [[65, "3.1.1-Two-Node-Bayesian-Network-Example"]], "3.1.1. EstimatorQNN Example": [[53, "3.1.1.-EstimatorQNN-Example"]], "3.1.2. Burglary Alarm Example": [[65, "3.1.2.-Burglary-Alarm-Example"]], "3.1.2. SamplerQNN Example": [[53, "3.1.2.-SamplerQNN-Example"]], "3.2 Set up Effective Dimension calculation": [[62, "3.2-Set-up-Effective-Dimension-calculation"]], "3.2. Defining the Quantum Kernel": [[56, "3.2.-Defining-the-Quantum-Kernel"]], "3.2. Definition of the quantum generator": [[57, "3.2.-Definition-of-the-quantum-generator"]], "3.2. Non-batched Forward Pass": [[53, "3.2.-Non-batched-Forward-Pass"]], "3.2.1. EstimatorQNN Example": [[53, "3.2.1.-EstimatorQNN-Example"]], "3.2.2. SamplerQNN Example": [[53, "3.2.2.-SamplerQNN-Example"]], "3.3 Compute Global Effective Dimension": [[62, "3.3-Compute-Global-Effective-Dimension"]], "3.3. Batched Forward Pass": [[53, "3.3.-Batched-Forward-Pass"]], "3.3. Clustering with the Spectral Clustering Model": [[56, "3.3.-Clustering-with-the-Spectral-Clustering-Model"]], "3.3. Definition of the classical discriminator": [[57, "3.3.-Definition-of-the-classical-discriminator"]], "3.3.1. EstimatorQNN Example": [[53, "3.3.1.-EstimatorQNN-Example"]], "3.3.2. SamplerQNN Example": [[53, "3.3.2.-SamplerQNN-Example"]], "3.4. Create a generator and a discriminator": [[57, "3.4.-Create-a-generator-and-a-discriminator"]], "4. Choosing a Loss Function": [[64, "4.-Choosing-a-Loss-Function"]], "4. How to Run a Backward Pass": [[53, "4.-How-to-Run-a-Backward-Pass"]], "4. How to Run an Inference": [[65, "4.-How-to-Run-an-Inference"]], "4. Kernel Principal Component Analysis": [[56, "4.-Kernel-Principal-Component-Analysis"]], "4. Local Effective Dimension Example": [[62, "4.-Local-Effective-Dimension-Example"]], "4. Modeling our QCNN": [[63, "4.-Modeling-our-QCNN"]], "4. PyTorch hybrid models": [[61, "4.-PyTorch-hybrid-models"]], "4. Reducing the Number of Features": [[55, "4.-Reducing-the-Number-of-Features"]], "4. Setting up the Training Loop": [[57, "4.-Setting-up-the-Training-Loop"]], "4.1 Define Dataset and QNN": [[62, "4.1-Define-Dataset-and-QNN"]], "4.1 Set Up": [[65, "4.1-Set-Up"]], "4.1. Backward Pass without Input Gradients": [[53, "4.1.-Backward-Pass-without-Input-Gradients"]], "4.1. Defining the dataset": [[56, "4.1.-Defining-the-dataset"]], "4.1. Definition of the loss functions": [[57, "4.1.-Definition-of-the-loss-functions"]], "4.1. Two Node Bayesian Network Example": [[65, "4.1.-Two-Node-Bayesian-Network-Example"]], "4.1.1. EstimatorQNN Example": [[53, "4.1.1.-EstimatorQNN-Example"]], "4.1.2. SamplerQNN Example": [[53, "4.1.2.-SamplerQNN-Example"]], "4.2 Train QNN": [[62, "4.2-Train-QNN"]], "4.2. Backward Pass with Input Gradients": [[53, "4.2.-Backward-Pass-with-Input-Gradients"]], "4.2. Burglary Alarm Example": [[65, "4.2.-Burglary-Alarm-Example"]], "4.2. Defining the Quantum Kernel": [[56, "4.2.-Defining-the-Quantum-Kernel"]], "4.2. Definition of the optimizers": [[57, "4.2.-Definition-of-the-optimizers"]], "4.2.1. EstimatorQNN Example": [[53, "4.2.1.-EstimatorQNN-Example"]], "4.2.2. SamplerQNN Example": [[53, "4.2.2.-SamplerQNN-Example"]], "4.3 Compute Local Effective Dimension of trained QNN": [[62, "4.3-Compute-Local-Effective-Dimension-of-trained-QNN"]], "4.3. Comparison of Kernel PCA on gaussian and quantum kernel": [[56, "4.3.-Comparison-of-Kernel-PCA-on-gaussian-and-quantum-kernel"]], "4.3. Visualization of the training process": [[57, "4.3.-Visualization-of-the-training-process"]], "4.4 Compute Local Effective Dimension of untrained QNN": [[62, "4.4-Compute-Local-Effective-Dimension-of-untrained-QNN"]], "4.5 Plot and analyze results": [[62, "4.5-Plot-and-analyze-results"]], "5. Advanced Functionality": [[53, "5.-Advanced-Functionality"]], "5. Building the Quantum Autoencoder Ansatz": [[64, "5.-Building-the-Quantum-Autoencoder-Ansatz"]], "5. Conclusion": [[55, "5.-Conclusion"], [56, "5.-Conclusion"]], "5. Model Training": [[57, "5.-Model-Training"]], "5. Training our QCNN": [[63, "5.-Training-our-QCNN"]], "5.1. EstimatorQNN with Multiple Observables": [[53, "5.1.-EstimatorQNN-with-Multiple-Observables"]], "5.2. SamplerQNN with custom interpret": [[53, "5.2.-SamplerQNN-with-custom-interpret"]], "6. A Simple Example: The Domain Wall Autoencoder": [[64, "6.-A-Simple-Example:-The-Domain-Wall-Autoencoder"]], "6. Conclusion": [[53, "6.-Conclusion"]], "6. Results: Cumulative Density Functions": [[57, "6.-Results:-Cumulative-Density-Functions"]], "6. Testing our QCNN": [[63, "6.-Testing-our-QCNN"]], "7. A Quantum Autoencoder for Digital Compression": [[64, "7.-A-Quantum-Autoencoder-for-Digital-Compression"]], "7. Conclusion": [[57, "7.-Conclusion"]], "7. References": [[63, "7.-References"]], "8. Applications of a Quantum Autoencoder": [[64, "8.-Applications-of-a-Quantum-Autoencoder"]], "9. References": [[64, "9.-References"]], "A. Classification with PyTorch and EstimatorQNN": [[58, "A.-Classification-with-PyTorch-and-EstimatorQNN"]], "A. Regression with PyTorch and EstimatorQNN": [[58, "A.-Regression-with-PyTorch-and-EstimatorQNN"]], "Algorithms": [[1, "algorithms"]], "B. 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learning applications (qiskit_machine_learning.circuit.library)", "Connectors (qiskit_machine_learning.connectors)", "Datasets (qiskit_machine_learning.datasets)", "Quantum kernels (qiskit_machine_learning.kernels)", "Quantum Kernel Algorithms", "Quantum neural networks (qiskit_machine_learning.neural_networks)", "Utility functions and classes (qiskit_machine_learning.utils)", "Loss Functions (qiskit_machine_learning.utils.loss_functions)", "Getting started", "Qiskit Machine Learning overview", "Qiskit Machine Learning v0.5 Migration Guide", "Qiskit Machine Learning Migration Guide", "Release Notes", "QiskitMachineLearningError", "BinaryObjectiveFunction", "MultiClassObjectiveFunction", "NeuralNetworkClassifier", "NeuralNetworkRegressor", "ObjectiveFunction", "OneHotObjectiveFunction", "PegasosQSVC", "QBayesian", "QSVC", "QSVR", "SerializableModelMixin", "TrainableModel", "VQC", "VQR", "QNNCircuit", "RawFeatureVector", "TorchConnector", "ad_hoc_data", "BaseKernel", 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\ No newline at end of file
diff --git a/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html b/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html
index 4705663ae..0a21056d1 100644
--- a/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html
+++ b/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html
@@ -396,7 +396,7 @@
Bases: NeuralNetwork
A neural network implementation based on the Estimator primitive.
The EstimatorQNN is a neural network that takes in a parametrized quantum circuit
@@ -501,12 +501,16 @@
True for a proper gradient computation when using
TorchConnector.
-num_virtual_qubits (int | None) – Number of virtual qubits.
default_precision (float) – The default precision for the estimator if not specified during run.
pass_manager (BasePassManager | None) – The pass manager to transpile the circuits, if necessary.
+Defaults to None, as some primitives do not need transpiled circuits.
QiskitMachineLearningError – Invalid parameter values.
+QiskitMachineLearningError – Invalid parameter values.
QiskitMachineLearningError – Gradient is required if
Attributes
diff --git a/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html b/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html index 87f26f631..19ca1a403 100644 --- a/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html +++ b/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html @@ -396,7 +396,7 @@Bases: NeuralNetwork
A neural network implementation based on the Sampler primitive.
The SamplerQNN is a neural network that takes in a parametrized quantum circuit
@@ -508,7 +508,10 @@
True for a
proper gradient computation when using
-TorchConnector. Raises:
+TorchConnector.
+pass_manager: The pass manager to transpile the circuits, if necessary.
+Defaults to None, as some primitives do not need transpiled circuits.
+Raises:
QiskitMachineLearningError: Invalid parameter values.
Attributes
EstimatorQ
-/tmp/ipykernel_2155/381094295.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- estimator_qnn = EstimatorQNN(
-/tmp/ipykernel_2155/381094295.py:3: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_2176/381094295.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
estimator_qnn = EstimatorQNN(
EstimatorQ
-<qiskit_machine_learning.neural_networks.estimator_qnn.EstimatorQNN at 0x7febcbbc4bb0>
+<qiskit_machine_learning.neural_networks.estimator_qnn.EstimatorQNN at 0x7fd3a3de3880>
We’ll see how to use the QNN in the following sections, but before that, let’s check out the SamplerQNN class.
SamplerQNN
-/tmp/ipykernel_2155/1714778621.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_2176/1714778621.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
sampler_qnn = SamplerQNN(circuit=qc2, input_params=inputs2, weight_params=weights2)
SamplerQNN
-<qiskit_machine_learning.neural_networks.sampler_qnn.SamplerQNN at 0x7febb6097310>
+<qiskit_machine_learning.neural_networks.sampler_qnn.SamplerQNN at 0x7fd3aa053a90>
In addition to the basic arguments shown above, the SamplerQNN accepts three more settings: input_gradients, interpret, and output_shape. These will be introduced in sections 4 and 5.
EstimatorQ
-/tmp/ipykernel_2155/1817078375.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- estimator_qnn2 = EstimatorQNN(
-/tmp/ipykernel_2155/1817078375.py:3: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_2176/1817078375.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
estimator_qnn2 = EstimatorQNN(
@@ -1059,7 +1055,7 @@ 5.2. SamplerQNN
-/tmp/ipykernel_2155/276109081.py:4: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_2176/276109081.py:4: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
sampler_qnn2 = SamplerQNN(
@@ -1111,7 +1107,7 @@ 6. Conclusion
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:48:14 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 16:59:29 2024 UTC
diff --git a/tutorials/01_neural_networks.ipynb b/tutorials/01_neural_networks.ipynb
index e233d87a3..05e47be5d 100644
--- a/tutorials/01_neural_networks.ipynb
+++ b/tutorials/01_neural_networks.ipynb
@@ -91,10 +91,10 @@
"id": "annual-engine",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:12.141567Z",
- "iopub.status.busy": "2024-11-15T18:48:12.141376Z",
- "iopub.status.idle": "2024-11-15T18:48:12.468387Z",
- "shell.execute_reply": "2024-11-15T18:48:12.467771Z"
+ "iopub.execute_input": "2024-11-18T16:59:27.527962Z",
+ "iopub.status.busy": "2024-11-18T16:59:27.527760Z",
+ "iopub.status.idle": "2024-11-18T16:59:27.847047Z",
+ "shell.execute_reply": "2024-11-18T16:59:27.846336Z"
}
},
"outputs": [],
@@ -124,10 +124,10 @@
"id": "popular-artwork",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:12.471415Z",
- "iopub.status.busy": "2024-11-15T18:48:12.470791Z",
- "iopub.status.idle": "2024-11-15T18:48:13.532328Z",
- "shell.execute_reply": "2024-11-15T18:48:13.531636Z"
+ "iopub.execute_input": "2024-11-18T16:59:27.849766Z",
+ "iopub.status.busy": "2024-11-18T16:59:27.849169Z",
+ "iopub.status.idle": "2024-11-18T16:59:29.087868Z",
+ "shell.execute_reply": "2024-11-18T16:59:29.087182Z"
}
},
"outputs": [
@@ -171,10 +171,10 @@
"id": "encouraging-magnitude",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:13.534801Z",
- "iopub.status.busy": "2024-11-15T18:48:13.534275Z",
- "iopub.status.idle": "2024-11-15T18:48:13.537626Z",
- "shell.execute_reply": "2024-11-15T18:48:13.537115Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.090427Z",
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+ "shell.execute_reply": "2024-11-18T16:59:29.092796Z"
}
},
"outputs": [],
@@ -204,10 +204,10 @@
"id": "italian-clear",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:13.539639Z",
- "iopub.status.busy": "2024-11-15T18:48:13.539227Z",
- "iopub.status.idle": "2024-11-15T18:48:13.877728Z",
- "shell.execute_reply": "2024-11-15T18:48:13.877060Z"
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+ "iopub.status.idle": "2024-11-18T16:59:29.436579Z",
+ "shell.execute_reply": "2024-11-18T16:59:29.435862Z"
}
},
"outputs": [
@@ -215,16 +215,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2155/381094295.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " estimator_qnn = EstimatorQNN(\n",
- "/tmp/ipykernel_2155/381094295.py:3: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_2176/381094295.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" estimator_qnn = EstimatorQNN(\n"
]
},
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 4,
@@ -279,10 +277,10 @@
"id": "acceptable-standing",
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"execution": {
- "iopub.execute_input": "2024-11-15T18:48:13.880090Z",
- "iopub.status.busy": "2024-11-15T18:48:13.879616Z",
- "iopub.status.idle": "2024-11-15T18:48:13.984630Z",
- "shell.execute_reply": "2024-11-15T18:48:13.984088Z"
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+ "shell.execute_reply": "2024-11-18T16:59:29.543669Z"
}
},
"outputs": [
@@ -346,10 +344,10 @@
"id": "5c007d10",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:13.986730Z",
- "iopub.status.busy": "2024-11-15T18:48:13.986236Z",
- "iopub.status.idle": "2024-11-15T18:48:13.992252Z",
- "shell.execute_reply": "2024-11-15T18:48:13.991637Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.546448Z",
+ "iopub.status.busy": "2024-11-18T16:59:29.545979Z",
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}
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"outputs": [
@@ -357,14 +355,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2155/1714778621.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_2176/1714778621.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" sampler_qnn = SamplerQNN(circuit=qc2, input_params=inputs2, weight_params=weights2)\n"
]
},
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 6,
@@ -418,10 +416,10 @@
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- "iopub.execute_input": "2024-11-15T18:48:13.994275Z",
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- "iopub.status.idle": "2024-11-15T18:48:13.997381Z",
- "shell.execute_reply": "2024-11-15T18:48:13.996767Z"
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}
},
"outputs": [],
@@ -436,10 +434,10 @@
"id": "4d5c27e2",
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- "shell.execute_reply": "2024-11-15T18:48:14.001973Z"
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}
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"outputs": [
@@ -477,10 +475,10 @@
"id": "a0fd6253",
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- "shell.execute_reply": "2024-11-15T18:48:14.006912Z"
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},
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@@ -496,10 +494,10 @@
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- "iopub.status.idle": "2024-11-15T18:48:14.012318Z",
- "shell.execute_reply": "2024-11-15T18:48:14.011777Z"
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}
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"outputs": [
@@ -561,10 +559,10 @@
"id": "54bed89e",
"metadata": {
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- "shell.execute_reply": "2024-11-15T18:48:14.018930Z"
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}
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"outputs": [
@@ -607,10 +605,10 @@
"id": "cb847a75",
"metadata": {
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- "shell.execute_reply": "2024-11-15T18:48:14.026968Z"
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}
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@@ -661,10 +659,10 @@
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- "shell.execute_reply": "2024-11-15T18:48:14.033997Z"
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}
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@@ -710,10 +708,10 @@
"id": "29eb2151",
"metadata": {
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- "shell.execute_reply": "2024-11-15T18:48:14.043103Z"
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+ "shell.execute_reply": "2024-11-18T16:59:29.603537Z"
}
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"outputs": [
@@ -785,10 +783,10 @@
"id": "entitled-reaction",
"metadata": {
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- "shell.execute_reply": "2024-11-15T18:48:14.052426Z"
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+ "iopub.status.busy": "2024-11-18T16:59:29.605686Z",
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},
"scrolled": false
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@@ -839,10 +837,10 @@
"id": "eefacefe",
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- "iopub.status.idle": "2024-11-15T18:48:14.067725Z",
- "shell.execute_reply": "2024-11-15T18:48:14.067153Z"
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+ "shell.execute_reply": "2024-11-18T16:59:29.626702Z"
}
},
"outputs": [
@@ -889,10 +887,10 @@
"id": "9ccc4641",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:14.069610Z",
- "iopub.status.busy": "2024-11-15T18:48:14.069242Z",
- "iopub.status.idle": "2024-11-15T18:48:14.072150Z",
- "shell.execute_reply": "2024-11-15T18:48:14.071624Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.629106Z",
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}
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"outputs": [],
@@ -923,10 +921,10 @@
"id": "4332f42b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:14.074098Z",
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- "shell.execute_reply": "2024-11-15T18:48:14.079834Z"
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}
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"outputs": [
@@ -976,10 +974,10 @@
"id": "3339f869",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:48:14.096968Z",
- "shell.execute_reply": "2024-11-15T18:48:14.096451Z"
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+ "shell.execute_reply": "2024-11-18T16:59:29.658583Z"
}
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"outputs": [
@@ -1043,10 +1041,10 @@
"id": "34e1e2f0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:14.098960Z",
- "iopub.status.busy": "2024-11-15T18:48:14.098603Z",
- "iopub.status.idle": "2024-11-15T18:48:14.102694Z",
- "shell.execute_reply": "2024-11-15T18:48:14.102072Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.661225Z",
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+ "shell.execute_reply": "2024-11-18T16:59:29.664405Z"
}
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"outputs": [
@@ -1054,9 +1052,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2155/1817078375.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " estimator_qnn2 = EstimatorQNN(\n",
- "/tmp/ipykernel_2155/1817078375.py:3: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_2176/1817078375.py:3: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" estimator_qnn2 = EstimatorQNN(\n"
]
}
@@ -1078,10 +1074,10 @@
"id": "e801632d",
"metadata": {
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- "iopub.status.idle": "2024-11-15T18:48:14.112973Z",
- "shell.execute_reply": "2024-11-15T18:48:14.112463Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.666955Z",
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+ "shell.execute_reply": "2024-11-18T16:59:29.675237Z"
}
},
"outputs": [
@@ -1130,10 +1126,10 @@
"id": "eed68d1a",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:48:14.118586Z",
- "shell.execute_reply": "2024-11-15T18:48:14.118062Z"
+ "iopub.execute_input": "2024-11-18T16:59:29.677733Z",
+ "iopub.status.busy": "2024-11-18T16:59:29.677350Z",
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+ "shell.execute_reply": "2024-11-18T16:59:29.681046Z"
}
},
"outputs": [
@@ -1141,7 +1137,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2155/276109081.py:4: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_2176/276109081.py:4: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" sampler_qnn2 = SamplerQNN(\n"
]
}
@@ -1165,10 +1161,10 @@
"id": "c2888195",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:14.120630Z",
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- "iopub.status.idle": "2024-11-15T18:48:14.137154Z",
- "shell.execute_reply": "2024-11-15T18:48:14.136597Z"
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+ "shell.execute_reply": "2024-11-18T16:59:29.698422Z"
}
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"outputs": [
@@ -1214,10 +1210,10 @@
"id": "appointed-shirt",
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- "shell.execute_reply": "2024-11-15T18:48:14.146428Z"
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},
"pycharm": {
"name": "#%%\n"
@@ -1227,7 +1223,7 @@
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:48:14 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 16:59:29 2024 UTC
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diff --git a/tutorials/02_neural_network_classifier_and_regressor.html b/tutorials/02_neural_network_classifier_and_regressor.html
index 36290ca1e..ac71911e9 100644
--- a/tutorials/02_neural_network_classifier_and_regressor.html
+++ b/tutorials/02_neural_network_classifier_and_regressor.html
@@ -505,9 +505,7 @@ Classification with an
-/tmp/ipykernel_2647/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- estimator_qnn = EstimatorQNN(circuit=qc)
-/tmp/ipykernel_2647/1658004975.py:1: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_2667/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
estimator_qnn = EstimatorQNN(circuit=qc)
@@ -694,7 +692,7 @@ Classification with a
-/tmp/ipykernel_2647/3691530435.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_2667/3691530435.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
sampler_qnn = SamplerQNN(
@@ -917,7 +915,7 @@ Multiple classes with VQC
-<matplotlib.collections.PathCollection at 0x7ff1d493b610>
+<matplotlib.collections.PathCollection at 0x7f026a60afb0>
@@ -1078,9 +1076,7 @@ Regression with an
-/tmp/ipykernel_2647/3237334137.py:15: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- regression_estimator_qnn = EstimatorQNN(circuit=qc)
-/tmp/ipykernel_2647/3237334137.py:15: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_2667/3237334137.py:15: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
regression_estimator_qnn = EstimatorQNN(circuit=qc)
@@ -1179,7 +1175,7 @@ Regression with an
Regression with the Variational Quantum Regressor (VQR)¶
Similar to the VQC for classification, the VQR is a special variant of the NeuralNetworkRegressor with a EstimatorQNN. By default it considers the L2Loss function to minimize the mean squared error between predictions and targets.
-
+
[33]:
@@ -1192,15 +1188,6 @@ Regression with the Variational Quantum Regressor (
-
-
-
-
-/home/runner/work/qiskit-machine-learning/qiskit-machine-learning/qiskit_machine_learning/algorithms/regressors/vqr.py:106: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.
- neural_network = EstimatorQNN(
-
-
[34]:
@@ -1275,7 +1262,7 @@ Regression with the Variational Quantum Regressor (
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:48:45 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:00:01 2024 UTC
diff --git a/tutorials/02_neural_network_classifier_and_regressor.ipynb b/tutorials/02_neural_network_classifier_and_regressor.ipynb
index 58af9ae72..c9cff0753 100644
--- a/tutorials/02_neural_network_classifier_and_regressor.ipynb
+++ b/tutorials/02_neural_network_classifier_and_regressor.ipynb
@@ -29,10 +29,10 @@
"id": "functioning-sword",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:17.491682Z",
- "iopub.status.busy": "2024-11-15T18:48:17.491180Z",
- "iopub.status.idle": "2024-11-15T18:48:18.932500Z",
- "shell.execute_reply": "2024-11-15T18:48:18.931786Z"
+ "iopub.execute_input": "2024-11-18T16:59:33.289477Z",
+ "iopub.status.busy": "2024-11-18T16:59:33.289281Z",
+ "iopub.status.idle": "2024-11-18T16:59:34.782601Z",
+ "shell.execute_reply": "2024-11-18T16:59:34.781815Z"
}
},
"outputs": [],
@@ -70,10 +70,10 @@
"id": "short-pierre",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:18.934939Z",
- "iopub.status.busy": "2024-11-15T18:48:18.934644Z",
- "iopub.status.idle": "2024-11-15T18:48:19.052376Z",
- "shell.execute_reply": "2024-11-15T18:48:19.051808Z"
+ "iopub.execute_input": "2024-11-18T16:59:34.785165Z",
+ "iopub.status.busy": "2024-11-18T16:59:34.784872Z",
+ "iopub.status.idle": "2024-11-18T16:59:34.900693Z",
+ "shell.execute_reply": "2024-11-18T16:59:34.900024Z"
},
"tags": [
"nbsphinx-thumbnail"
@@ -126,10 +126,10 @@
"id": "ceed90df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.054313Z",
- "iopub.status.busy": "2024-11-15T18:48:19.054103Z",
- "iopub.status.idle": "2024-11-15T18:48:19.532756Z",
- "shell.execute_reply": "2024-11-15T18:48:19.532136Z"
+ "iopub.execute_input": "2024-11-18T16:59:34.902806Z",
+ "iopub.status.busy": "2024-11-18T16:59:34.902594Z",
+ "iopub.status.idle": "2024-11-18T16:59:35.385952Z",
+ "shell.execute_reply": "2024-11-18T16:59:35.385303Z"
}
},
"outputs": [
@@ -165,10 +165,10 @@
"id": "determined-hands",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.535194Z",
- "iopub.status.busy": "2024-11-15T18:48:19.534526Z",
- "iopub.status.idle": "2024-11-15T18:48:19.538752Z",
- "shell.execute_reply": "2024-11-15T18:48:19.538187Z"
+ "iopub.execute_input": "2024-11-18T16:59:35.387939Z",
+ "iopub.status.busy": "2024-11-18T16:59:35.387648Z",
+ "iopub.status.idle": "2024-11-18T16:59:35.391836Z",
+ "shell.execute_reply": "2024-11-18T16:59:35.391252Z"
}
},
"outputs": [
@@ -176,9 +176,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2647/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " estimator_qnn = EstimatorQNN(circuit=qc)\n",
- "/tmp/ipykernel_2647/1658004975.py:1: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_2667/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" estimator_qnn = EstimatorQNN(circuit=qc)\n"
]
}
@@ -193,10 +191,10 @@
"id": "acute-casting",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.540817Z",
- "iopub.status.busy": "2024-11-15T18:48:19.540444Z",
- "iopub.status.idle": "2024-11-15T18:48:19.549022Z",
- "shell.execute_reply": "2024-11-15T18:48:19.548506Z"
+ "iopub.execute_input": "2024-11-18T16:59:35.393558Z",
+ "iopub.status.busy": "2024-11-18T16:59:35.393360Z",
+ "iopub.status.idle": "2024-11-18T16:59:35.402058Z",
+ "shell.execute_reply": "2024-11-18T16:59:35.401379Z"
}
},
"outputs": [
@@ -230,10 +228,10 @@
"id": "similar-controversy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.550772Z",
- "iopub.status.busy": "2024-11-15T18:48:19.550578Z",
- "iopub.status.idle": "2024-11-15T18:48:19.554065Z",
- "shell.execute_reply": "2024-11-15T18:48:19.553555Z"
+ "iopub.execute_input": "2024-11-18T16:59:35.404109Z",
+ "iopub.status.busy": "2024-11-18T16:59:35.403750Z",
+ "iopub.status.idle": "2024-11-18T16:59:35.407412Z",
+ "shell.execute_reply": "2024-11-18T16:59:35.406882Z"
}
},
"outputs": [],
@@ -255,10 +253,10 @@
"id": "lesser-receiver",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.555863Z",
- "iopub.status.busy": "2024-11-15T18:48:19.555664Z",
- "iopub.status.idle": "2024-11-15T18:48:19.558753Z",
- "shell.execute_reply": "2024-11-15T18:48:19.558031Z"
+ "iopub.execute_input": "2024-11-18T16:59:35.409280Z",
+ "iopub.status.busy": "2024-11-18T16:59:35.408902Z",
+ "iopub.status.idle": "2024-11-18T16:59:35.412046Z",
+ "shell.execute_reply": "2024-11-18T16:59:35.411491Z"
}
},
"outputs": [],
@@ -275,10 +273,10 @@
"id": "adopted-editor",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:19.560892Z",
- "iopub.status.busy": "2024-11-15T18:48:19.560458Z",
- "iopub.status.idle": "2024-11-15T18:48:29.374184Z",
- "shell.execute_reply": "2024-11-15T18:48:29.373556Z"
+ "iopub.execute_input": "2024-11-18T16:59:35.413969Z",
+ "iopub.status.busy": "2024-11-18T16:59:35.413596Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.046213Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.045512Z"
}
},
"outputs": [
@@ -324,10 +322,10 @@
"id": "civilian-analysis",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.376275Z",
- "iopub.status.busy": "2024-11-15T18:48:29.376050Z",
- "iopub.status.idle": "2024-11-15T18:48:29.533567Z",
- "shell.execute_reply": "2024-11-15T18:48:29.532876Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.048140Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.047932Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.203915Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.203351Z"
}
},
"outputs": [
@@ -373,10 +371,10 @@
"id": "offshore-basket",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.535950Z",
- "iopub.status.busy": "2024-11-15T18:48:29.535555Z",
- "iopub.status.idle": "2024-11-15T18:48:29.540095Z",
- "shell.execute_reply": "2024-11-15T18:48:29.539444Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.205974Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.205743Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.210013Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.209470Z"
}
},
"outputs": [
@@ -413,10 +411,10 @@
"id": "d1ff56f4",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.542213Z",
- "iopub.status.busy": "2024-11-15T18:48:29.541823Z",
- "iopub.status.idle": "2024-11-15T18:48:29.622447Z",
- "shell.execute_reply": "2024-11-15T18:48:29.621782Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.212202Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.211688Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.291236Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.290617Z"
}
},
"outputs": [
@@ -444,10 +442,10 @@
"id": "young-sensitivity",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.624563Z",
- "iopub.status.busy": "2024-11-15T18:48:29.624198Z",
- "iopub.status.idle": "2024-11-15T18:48:29.627517Z",
- "shell.execute_reply": "2024-11-15T18:48:29.626869Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.293259Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.292883Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.296226Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.295593Z"
}
},
"outputs": [],
@@ -466,10 +464,10 @@
"id": "statutory-mercury",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.629529Z",
- "iopub.status.busy": "2024-11-15T18:48:29.629185Z",
- "iopub.status.idle": "2024-11-15T18:48:29.633649Z",
- "shell.execute_reply": "2024-11-15T18:48:29.633098Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.298423Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.297977Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.302521Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.301897Z"
}
},
"outputs": [
@@ -477,7 +475,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2647/3691530435.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_2667/3691530435.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" sampler_qnn = SamplerQNN(\n"
]
}
@@ -497,10 +495,10 @@
"id": "hybrid-orlando",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.635528Z",
- "iopub.status.busy": "2024-11-15T18:48:29.635323Z",
- "iopub.status.idle": "2024-11-15T18:48:29.638269Z",
- "shell.execute_reply": "2024-11-15T18:48:29.637759Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.304382Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.304184Z",
+ "iopub.status.idle": "2024-11-18T16:59:45.307137Z",
+ "shell.execute_reply": "2024-11-18T16:59:45.306626Z"
}
},
"outputs": [],
@@ -517,10 +515,10 @@
"id": "adult-newman",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:29.640147Z",
- "iopub.status.busy": "2024-11-15T18:48:29.639950Z",
- "iopub.status.idle": "2024-11-15T18:48:34.224452Z",
- "shell.execute_reply": "2024-11-15T18:48:34.223746Z"
+ "iopub.execute_input": "2024-11-18T16:59:45.308991Z",
+ "iopub.status.busy": "2024-11-18T16:59:45.308793Z",
+ "iopub.status.idle": "2024-11-18T16:59:49.949976Z",
+ "shell.execute_reply": "2024-11-18T16:59:49.949389Z"
}
},
"outputs": [
@@ -566,10 +564,10 @@
"id": "angry-bulgarian",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:34.226656Z",
- "iopub.status.busy": "2024-11-15T18:48:34.226230Z",
- "iopub.status.idle": "2024-11-15T18:48:34.371124Z",
- "shell.execute_reply": "2024-11-15T18:48:34.370436Z"
+ "iopub.execute_input": "2024-11-18T16:59:49.952137Z",
+ "iopub.status.busy": "2024-11-18T16:59:49.951669Z",
+ "iopub.status.idle": "2024-11-18T16:59:50.096027Z",
+ "shell.execute_reply": "2024-11-18T16:59:50.095348Z"
}
},
"outputs": [
@@ -615,10 +613,10 @@
"id": "indonesian-bulletin",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:34.373543Z",
- "iopub.status.busy": "2024-11-15T18:48:34.373050Z",
- "iopub.status.idle": "2024-11-15T18:48:34.377513Z",
- "shell.execute_reply": "2024-11-15T18:48:34.376972Z"
+ "iopub.execute_input": "2024-11-18T16:59:50.098200Z",
+ "iopub.status.busy": "2024-11-18T16:59:50.097716Z",
+ "iopub.status.idle": "2024-11-18T16:59:50.101971Z",
+ "shell.execute_reply": "2024-11-18T16:59:50.101412Z"
}
},
"outputs": [
@@ -653,10 +651,10 @@
"id": "legislative-dublin",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:34.379497Z",
- "iopub.status.busy": "2024-11-15T18:48:34.379113Z",
- "iopub.status.idle": "2024-11-15T18:48:34.388790Z",
- "shell.execute_reply": "2024-11-15T18:48:34.388263Z"
+ "iopub.execute_input": "2024-11-18T16:59:50.103929Z",
+ "iopub.status.busy": "2024-11-18T16:59:50.103567Z",
+ "iopub.status.idle": "2024-11-18T16:59:50.113073Z",
+ "shell.execute_reply": "2024-11-18T16:59:50.112416Z"
}
},
"outputs": [],
@@ -681,10 +679,10 @@
"id": "geographic-adjustment",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:34.390758Z",
- "iopub.status.busy": "2024-11-15T18:48:34.390391Z",
- "iopub.status.idle": "2024-11-15T18:48:38.948758Z",
- "shell.execute_reply": "2024-11-15T18:48:38.948094Z"
+ "iopub.execute_input": "2024-11-18T16:59:50.114907Z",
+ "iopub.status.busy": "2024-11-18T16:59:50.114706Z",
+ "iopub.status.idle": "2024-11-18T16:59:54.764655Z",
+ "shell.execute_reply": "2024-11-18T16:59:54.763979Z"
}
},
"outputs": [
@@ -730,10 +728,10 @@
"id": "stopped-heavy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:38.950940Z",
- "iopub.status.busy": "2024-11-15T18:48:38.950722Z",
- "iopub.status.idle": "2024-11-15T18:48:39.096826Z",
- "shell.execute_reply": "2024-11-15T18:48:39.096255Z"
+ "iopub.execute_input": "2024-11-18T16:59:54.767002Z",
+ "iopub.status.busy": "2024-11-18T16:59:54.766605Z",
+ "iopub.status.idle": "2024-11-18T16:59:54.906120Z",
+ "shell.execute_reply": "2024-11-18T16:59:54.905517Z"
}
},
"outputs": [
@@ -782,10 +780,10 @@
"id": "plastic-dividend",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:39.099011Z",
- "iopub.status.busy": "2024-11-15T18:48:39.098602Z",
- "iopub.status.idle": "2024-11-15T18:48:39.293412Z",
- "shell.execute_reply": "2024-11-15T18:48:39.292786Z"
+ "iopub.execute_input": "2024-11-18T16:59:54.908385Z",
+ "iopub.status.busy": "2024-11-18T16:59:54.907918Z",
+ "iopub.status.idle": "2024-11-18T16:59:54.939132Z",
+ "shell.execute_reply": "2024-11-18T16:59:54.938586Z"
}
},
"outputs": [],
@@ -819,17 +817,17 @@
"id": "premier-drill",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:39.295894Z",
- "iopub.status.busy": "2024-11-15T18:48:39.295400Z",
- "iopub.status.idle": "2024-11-15T18:48:39.383894Z",
- "shell.execute_reply": "2024-11-15T18:48:39.383269Z"
+ "iopub.execute_input": "2024-11-18T16:59:54.941112Z",
+ "iopub.status.busy": "2024-11-18T16:59:54.940803Z",
+ "iopub.status.idle": "2024-11-18T16:59:55.027328Z",
+ "shell.execute_reply": "2024-11-18T16:59:55.026657Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 22,
@@ -865,10 +863,10 @@
"id": "exposed-bailey",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:39.386078Z",
- "iopub.status.busy": "2024-11-15T18:48:39.385696Z",
- "iopub.status.idle": "2024-11-15T18:48:39.389379Z",
- "shell.execute_reply": "2024-11-15T18:48:39.388862Z"
+ "iopub.execute_input": "2024-11-18T16:59:55.029552Z",
+ "iopub.status.busy": "2024-11-18T16:59:55.029079Z",
+ "iopub.status.idle": "2024-11-18T16:59:55.032958Z",
+ "shell.execute_reply": "2024-11-18T16:59:55.032398Z"
}
},
"outputs": [
@@ -902,10 +900,10 @@
"id": "latin-result",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:39.391351Z",
- "iopub.status.busy": "2024-11-15T18:48:39.390968Z",
- "iopub.status.idle": "2024-11-15T18:48:39.401147Z",
- "shell.execute_reply": "2024-11-15T18:48:39.400403Z"
+ "iopub.execute_input": "2024-11-18T16:59:55.034749Z",
+ "iopub.status.busy": "2024-11-18T16:59:55.034551Z",
+ "iopub.status.idle": "2024-11-18T16:59:55.044300Z",
+ "shell.execute_reply": "2024-11-18T16:59:55.043774Z"
}
},
"outputs": [],
@@ -931,10 +929,10 @@
"id": "reported-pioneer",
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- "shell.execute_reply": "2024-11-15T18:48:43.687466Z"
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+ "shell.execute_reply": "2024-11-18T16:59:59.359299Z"
}
},
"outputs": [
@@ -988,10 +986,10 @@
"id": "employed-patient",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:43.714941Z"
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+ "shell.execute_reply": "2024-11-18T16:59:59.389395Z"
}
},
"outputs": [
@@ -1026,10 +1024,10 @@
"id": "iraqi-flavor",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:43.792180Z"
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}
},
"outputs": [
@@ -1075,10 +1073,10 @@
"id": "perfect-kelly",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:43.799870Z"
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}
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"outputs": [
@@ -1086,9 +1084,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2647/3237334137.py:15: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " regression_estimator_qnn = EstimatorQNN(circuit=qc)\n",
- "/tmp/ipykernel_2647/3237334137.py:15: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_2667/3237334137.py:15: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" regression_estimator_qnn = EstimatorQNN(circuit=qc)\n"
]
}
@@ -1117,10 +1113,10 @@
"id": "velvet-marks",
"metadata": {
"execution": {
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}
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"outputs": [],
@@ -1140,10 +1136,10 @@
"id": "working-mongolia",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:44.609228Z"
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+ "shell.execute_reply": "2024-11-18T17:00:00.438514Z"
}
},
"outputs": [
@@ -1189,10 +1185,10 @@
"id": "diverse-conservative",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:44.706579Z"
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}
},
"outputs": [
@@ -1234,10 +1230,10 @@
"id": "terminal-turner",
"metadata": {
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- "shell.execute_reply": "2024-11-15T18:48:44.713285Z"
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+ "shell.execute_reply": "2024-11-18T17:00:00.536185Z"
}
},
"outputs": [
@@ -1272,22 +1268,13 @@
"id": "offensive-entry",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:44.715854Z",
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- "iopub.status.idle": "2024-11-15T18:48:44.719882Z",
- "shell.execute_reply": "2024-11-15T18:48:44.719344Z"
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+ "shell.execute_reply": "2024-11-18T17:00:00.541695Z"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/runner/work/qiskit-machine-learning/qiskit-machine-learning/qiskit_machine_learning/algorithms/regressors/vqr.py:106: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.\n",
- " neural_network = EstimatorQNN(\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"vqr = VQR(\n",
" feature_map=feature_map,\n",
@@ -1303,10 +1290,10 @@
"id": "cooperative-helmet",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:44.721822Z",
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- "shell.execute_reply": "2024-11-15T18:48:45.540141Z"
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+ "shell.execute_reply": "2024-11-18T17:00:01.343526Z"
}
},
"outputs": [
@@ -1352,10 +1339,10 @@
"id": "genetic-cambridge",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:45.542896Z",
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- "shell.execute_reply": "2024-11-15T18:48:45.632135Z"
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+ "shell.execute_reply": "2024-11-18T17:00:01.434790Z"
}
},
"outputs": [
@@ -1389,17 +1376,17 @@
"id": "backed-visit",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:45.635060Z",
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- "shell.execute_reply": "2024-11-15T18:48:45.641731Z"
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+ "shell.execute_reply": "2024-11-18T17:00:01.444226Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:48:45 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:00:01 2024 UTC
"
],
"text/plain": [
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diff --git a/tutorials/02a_training_a_quantum_model_on_a_real_dataset.html b/tutorials/02a_training_a_quantum_model_on_a_real_dataset.html
index 4564fdc52..692ab9aff 100644
--- a/tutorials/02a_training_a_quantum_model_on_a_real_dataset.html
+++ b/tutorials/02a_training_a_quantum_model_on_a_real_dataset.html
@@ -553,7 +553,7 @@ 1. Exploratory Data Analysis
-<seaborn.axisgrid.PairGrid at 0x7fb35df77970>
+<seaborn.axisgrid.PairGrid at 0x7f83d00a3670>
@@ -1069,7 +1069,7 @@ 5. Conclusion
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:50:12 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:01:28 2024 UTC
diff --git a/tutorials/02a_training_a_quantum_model_on_a_real_dataset.ipynb b/tutorials/02a_training_a_quantum_model_on_a_real_dataset.ipynb
index 9d30ee392..9c1241a9a 100644
--- a/tutorials/02a_training_a_quantum_model_on_a_real_dataset.ipynb
+++ b/tutorials/02a_training_a_quantum_model_on_a_real_dataset.ipynb
@@ -27,10 +27,10 @@
"id": "valued-leeds",
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- "shell.execute_reply": "2024-11-15T18:48:49.092040Z"
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+ "shell.execute_reply": "2024-11-18T17:00:04.892477Z"
}
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"outputs": [],
@@ -54,10 +54,10 @@
"id": "everyday-commission",
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"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:49.097928Z"
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}
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"outputs": [
@@ -159,10 +159,10 @@
"id": "mobile-dictionary",
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+ "shell.execute_reply": "2024-11-18T17:00:04.902710Z"
}
},
"outputs": [],
@@ -187,10 +187,10 @@
"id": "alternative-preliminary",
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- "shell.execute_reply": "2024-11-15T18:48:49.107686Z"
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+ "shell.execute_reply": "2024-11-18T17:00:04.908054Z"
}
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@@ -214,10 +214,10 @@
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},
"tags": [
"nbsphinx-thumbnail"
@@ -227,7 +227,7 @@
{
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"text/plain": [
- ""
+ ""
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@@ -277,10 +277,10 @@
"id": "pursuant-survival",
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"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:52.852606Z"
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}
},
"outputs": [],
@@ -308,10 +308,10 @@
"id": "proved-reviewer",
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+ "shell.execute_reply": "2024-11-18T17:00:08.681542Z"
}
},
"outputs": [],
@@ -336,10 +336,10 @@
"id": "veterinary-proxy",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:52.890162Z"
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+ "shell.execute_reply": "2024-11-18T17:00:08.688614Z"
}
},
"outputs": [
@@ -390,10 +390,10 @@
"id": "optional-pocket",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:53.625796Z"
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+ "shell.execute_reply": "2024-11-18T17:00:09.416224Z"
}
},
"outputs": [
@@ -434,10 +434,10 @@
"id": "elder-interaction",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:48:53.834971Z",
- "shell.execute_reply": "2024-11-15T18:48:53.834357Z"
+ "iopub.execute_input": "2024-11-18T17:00:09.419129Z",
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+ "shell.execute_reply": "2024-11-18T17:00:09.630141Z"
}
},
"outputs": [
@@ -476,10 +476,10 @@
"id": "intimate-doubt",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:53.837079Z",
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- "shell.execute_reply": "2024-11-15T18:48:53.860335Z"
+ "iopub.execute_input": "2024-11-18T17:00:09.633610Z",
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+ "shell.execute_reply": "2024-11-18T17:00:09.657137Z"
}
},
"outputs": [],
@@ -503,10 +503,10 @@
"id": "unauthorized-footwear",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:48:53.865540Z"
+ "iopub.execute_input": "2024-11-18T17:00:09.659994Z",
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+ "shell.execute_reply": "2024-11-18T17:00:09.662822Z"
}
},
"outputs": [
@@ -514,7 +514,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_3072/2087805081.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
+ "/tmp/ipykernel_3090/2087805081.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
" sampler = Sampler()\n"
]
}
@@ -539,10 +539,10 @@
"id": "connected-reach",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:53.868315Z",
- "iopub.status.busy": "2024-11-15T18:48:53.867860Z",
- "iopub.status.idle": "2024-11-15T18:48:53.871911Z",
- "shell.execute_reply": "2024-11-15T18:48:53.871379Z"
+ "iopub.execute_input": "2024-11-18T17:00:09.665405Z",
+ "iopub.status.busy": "2024-11-18T17:00:09.665203Z",
+ "iopub.status.idle": "2024-11-18T17:00:09.669346Z",
+ "shell.execute_reply": "2024-11-18T17:00:09.668700Z"
}
},
"outputs": [],
@@ -582,10 +582,10 @@
"id": "multiple-garbage",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:48:53.873651Z",
- "iopub.status.busy": "2024-11-15T18:48:53.873452Z",
- "iopub.status.idle": "2024-11-15T18:49:45.923135Z",
- "shell.execute_reply": "2024-11-15T18:49:45.922461Z"
+ "iopub.execute_input": "2024-11-18T17:00:09.671680Z",
+ "iopub.status.busy": "2024-11-18T17:00:09.671129Z",
+ "iopub.status.idle": "2024-11-18T17:01:01.715854Z",
+ "shell.execute_reply": "2024-11-18T17:01:01.715150Z"
}
},
"outputs": [
@@ -643,10 +643,10 @@
"id": "formed-mineral",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:45.925258Z",
- "iopub.status.busy": "2024-11-15T18:49:45.925050Z",
- "iopub.status.idle": "2024-11-15T18:49:46.419493Z",
- "shell.execute_reply": "2024-11-15T18:49:46.418768Z"
+ "iopub.execute_input": "2024-11-18T17:01:01.718058Z",
+ "iopub.status.busy": "2024-11-18T17:01:01.717616Z",
+ "iopub.status.idle": "2024-11-18T17:01:02.240328Z",
+ "shell.execute_reply": "2024-11-18T17:01:02.239722Z"
}
},
"outputs": [
@@ -697,10 +697,10 @@
"id": "painted-montreal",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:46.421682Z",
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- "iopub.status.idle": "2024-11-15T18:49:46.567136Z",
- "shell.execute_reply": "2024-11-15T18:49:46.566588Z"
+ "iopub.execute_input": "2024-11-18T17:01:02.242619Z",
+ "iopub.status.busy": "2024-11-18T17:01:02.242236Z",
+ "iopub.status.idle": "2024-11-18T17:01:02.390467Z",
+ "shell.execute_reply": "2024-11-18T17:01:02.389891Z"
}
},
"outputs": [
@@ -748,10 +748,10 @@
"id": "naval-agriculture",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:46.569455Z",
- "iopub.status.busy": "2024-11-15T18:49:46.568979Z",
- "iopub.status.idle": "2024-11-15T18:49:46.576460Z",
- "shell.execute_reply": "2024-11-15T18:49:46.575943Z"
+ "iopub.execute_input": "2024-11-18T17:01:02.392744Z",
+ "iopub.status.busy": "2024-11-18T17:01:02.392222Z",
+ "iopub.status.idle": "2024-11-18T17:01:02.399912Z",
+ "shell.execute_reply": "2024-11-18T17:01:02.399388Z"
}
},
"outputs": [
@@ -792,10 +792,10 @@
"id": "electric-novel",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:46.578494Z",
- "iopub.status.busy": "2024-11-15T18:49:46.578118Z",
- "iopub.status.idle": "2024-11-15T18:49:46.583096Z",
- "shell.execute_reply": "2024-11-15T18:49:46.582593Z"
+ "iopub.execute_input": "2024-11-18T17:01:02.401939Z",
+ "iopub.status.busy": "2024-11-18T17:01:02.401548Z",
+ "iopub.status.idle": "2024-11-18T17:01:02.406707Z",
+ "shell.execute_reply": "2024-11-18T17:01:02.406050Z"
}
},
"outputs": [],
@@ -820,10 +820,10 @@
"id": "younger-louisiana",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:46.585043Z",
- "iopub.status.busy": "2024-11-15T18:49:46.584659Z",
- "iopub.status.idle": "2024-11-15T18:49:46.587474Z",
- "shell.execute_reply": "2024-11-15T18:49:46.586944Z"
+ "iopub.execute_input": "2024-11-18T17:01:02.408655Z",
+ "iopub.status.busy": "2024-11-18T17:01:02.408285Z",
+ "iopub.status.idle": "2024-11-18T17:01:02.411347Z",
+ "shell.execute_reply": "2024-11-18T17:01:02.410709Z"
}
},
"outputs": [],
@@ -845,10 +845,10 @@
"id": "varied-capital",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:46.589404Z",
- "iopub.status.busy": "2024-11-15T18:49:46.589023Z",
- "iopub.status.idle": "2024-11-15T18:49:58.410176Z",
- "shell.execute_reply": "2024-11-15T18:49:58.409465Z"
+ "iopub.execute_input": "2024-11-18T17:01:02.413160Z",
+ "iopub.status.busy": "2024-11-18T17:01:02.412959Z",
+ "iopub.status.idle": "2024-11-18T17:01:14.342011Z",
+ "shell.execute_reply": "2024-11-18T17:01:14.341279Z"
}
},
"outputs": [
@@ -899,10 +899,10 @@
"id": "developmental-crazy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:58.412416Z",
- "iopub.status.busy": "2024-11-15T18:49:58.412010Z",
- "iopub.status.idle": "2024-11-15T18:49:58.630549Z",
- "shell.execute_reply": "2024-11-15T18:49:58.629929Z"
+ "iopub.execute_input": "2024-11-18T17:01:14.344037Z",
+ "iopub.status.busy": "2024-11-18T17:01:14.343821Z",
+ "iopub.status.idle": "2024-11-18T17:01:14.567524Z",
+ "shell.execute_reply": "2024-11-18T17:01:14.566954Z"
}
},
"outputs": [
@@ -937,10 +937,10 @@
"id": "convinced-seven",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:49:58.632809Z",
- "iopub.status.busy": "2024-11-15T18:49:58.632338Z",
- "iopub.status.idle": "2024-11-15T18:50:12.691378Z",
- "shell.execute_reply": "2024-11-15T18:50:12.690648Z"
+ "iopub.execute_input": "2024-11-18T17:01:14.569468Z",
+ "iopub.status.busy": "2024-11-18T17:01:14.569266Z",
+ "iopub.status.idle": "2024-11-18T17:01:28.693701Z",
+ "shell.execute_reply": "2024-11-18T17:01:28.693009Z"
}
},
"outputs": [
@@ -992,10 +992,10 @@
"id": "painted-reverse",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:12.693432Z",
- "iopub.status.busy": "2024-11-15T18:50:12.693222Z",
- "iopub.status.idle": "2024-11-15T18:50:12.985071Z",
- "shell.execute_reply": "2024-11-15T18:50:12.984504Z"
+ "iopub.execute_input": "2024-11-18T17:01:28.695915Z",
+ "iopub.status.busy": "2024-11-18T17:01:28.695506Z",
+ "iopub.status.idle": "2024-11-18T17:01:28.993462Z",
+ "shell.execute_reply": "2024-11-18T17:01:28.992903Z"
}
},
"outputs": [
@@ -1040,10 +1040,10 @@
"id": "educated-snake",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:12.987057Z",
- "iopub.status.busy": "2024-11-15T18:50:12.986848Z",
- "iopub.status.idle": "2024-11-15T18:50:12.990757Z",
- "shell.execute_reply": "2024-11-15T18:50:12.990213Z"
+ "iopub.execute_input": "2024-11-18T17:01:28.995830Z",
+ "iopub.status.busy": "2024-11-18T17:01:28.995438Z",
+ "iopub.status.idle": "2024-11-18T17:01:28.999859Z",
+ "shell.execute_reply": "2024-11-18T17:01:28.999295Z"
}
},
"outputs": [
@@ -1089,17 +1089,17 @@
"id": "median-psychology",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:12.992790Z",
- "iopub.status.busy": "2024-11-15T18:50:12.992411Z",
- "iopub.status.idle": "2024-11-15T18:50:12.999717Z",
- "shell.execute_reply": "2024-11-15T18:50:12.999089Z"
+ "iopub.execute_input": "2024-11-18T17:01:29.001906Z",
+ "iopub.status.busy": "2024-11-18T17:01:29.001538Z",
+ "iopub.status.idle": "2024-11-18T17:01:29.009170Z",
+ "shell.execute_reply": "2024-11-18T17:01:29.008565Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:50:12 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:01:28 2024 UTC
"
],
"text/plain": [
""
diff --git a/tutorials/03_quantum_kernel.html b/tutorials/03_quantum_kernel.html
index 226919a26..a11fcab6c 100644
--- a/tutorials/03_quantum_kernel.html
+++ b/tutorials/03_quantum_kernel.html
@@ -582,9 +582,9 @@ 2.2. Defining the quantum kernel
-/tmp/ipykernel_3546/3912191764.py:8: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
+/tmp/ipykernel_3564/3912191764.py:8: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
sampler = Sampler()
-/tmp/ipykernel_3546/3912191764.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_3564/3912191764.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
fidelity = ComputeUncompute(sampler=sampler)
@@ -1065,7 +1065,7 @@ 5. Conclusion
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:50:33 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:01:49 2024 UTC
diff --git a/tutorials/03_quantum_kernel.ipynb b/tutorials/03_quantum_kernel.ipynb
index 40e4c0a59..27265bf73 100644
--- a/tutorials/03_quantum_kernel.ipynb
+++ b/tutorials/03_quantum_kernel.ipynb
@@ -77,10 +77,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:50:16.405564Z",
- "shell.execute_reply": "2024-11-15T18:50:16.404979Z"
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+ "shell.execute_reply": "2024-11-18T17:01:32.484656Z"
}
},
"outputs": [],
@@ -120,10 +120,10 @@
"execution_count": 2,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:50:17.259686Z"
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+ "shell.execute_reply": "2024-11-18T17:01:33.361514Z"
}
},
"outputs": [],
@@ -154,10 +154,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:17.262640Z",
- "iopub.status.busy": "2024-11-15T18:50:17.262348Z",
- "iopub.status.idle": "2024-11-15T18:50:17.588678Z",
- "shell.execute_reply": "2024-11-15T18:50:17.588011Z"
+ "iopub.execute_input": "2024-11-18T17:01:33.365175Z",
+ "iopub.status.busy": "2024-11-18T17:01:33.364647Z",
+ "iopub.status.idle": "2024-11-18T17:01:33.701327Z",
+ "shell.execute_reply": "2024-11-18T17:01:33.700601Z"
}
},
"outputs": [],
@@ -223,10 +223,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:17.591058Z",
- "iopub.status.busy": "2024-11-15T18:50:17.590770Z",
- "iopub.status.idle": "2024-11-15T18:50:17.749879Z",
- "shell.execute_reply": "2024-11-15T18:50:17.749189Z"
+ "iopub.execute_input": "2024-11-18T17:01:33.703808Z",
+ "iopub.status.busy": "2024-11-18T17:01:33.703507Z",
+ "iopub.status.idle": "2024-11-18T17:01:33.885122Z",
+ "shell.execute_reply": "2024-11-18T17:01:33.884530Z"
},
"tags": [
"nbsphinx-thumbnail"
@@ -270,10 +270,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:17.752252Z",
- "iopub.status.busy": "2024-11-15T18:50:17.751823Z",
- "iopub.status.idle": "2024-11-15T18:50:17.774305Z",
- "shell.execute_reply": "2024-11-15T18:50:17.773722Z"
+ "iopub.execute_input": "2024-11-18T17:01:33.887346Z",
+ "iopub.status.busy": "2024-11-18T17:01:33.886928Z",
+ "iopub.status.idle": "2024-11-18T17:01:33.909301Z",
+ "shell.execute_reply": "2024-11-18T17:01:33.908744Z"
}
},
"outputs": [
@@ -281,9 +281,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_3546/3912191764.py:8: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
+ "/tmp/ipykernel_3564/3912191764.py:8: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
" sampler = Sampler()\n",
- "/tmp/ipykernel_3546/3912191764.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_3564/3912191764.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" fidelity = ComputeUncompute(sampler=sampler)\n"
]
}
@@ -328,10 +328,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:17.776430Z",
- "iopub.status.busy": "2024-11-15T18:50:17.776110Z",
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- "shell.execute_reply": "2024-11-15T18:50:20.122517Z"
+ "iopub.execute_input": "2024-11-18T17:01:33.911346Z",
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+ "shell.execute_reply": "2024-11-18T17:01:36.228716Z"
}
},
"outputs": [
@@ -371,10 +371,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:20.125571Z",
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- "iopub.status.idle": "2024-11-15T18:50:22.681634Z",
- "shell.execute_reply": "2024-11-15T18:50:22.681025Z"
+ "iopub.execute_input": "2024-11-18T17:01:36.231708Z",
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+ "shell.execute_reply": "2024-11-18T17:01:38.780864Z"
},
"scrolled": false
},
@@ -419,10 +419,10 @@
"execution_count": 8,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:50:22.688322Z"
+ "iopub.execute_input": "2024-11-18T17:01:38.784033Z",
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+ "shell.execute_reply": "2024-11-18T17:01:38.788381Z"
}
},
"outputs": [
@@ -458,10 +458,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:22.690785Z",
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- "shell.execute_reply": "2024-11-15T18:50:25.174169Z"
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}
},
"outputs": [
@@ -497,10 +497,10 @@
"execution_count": 10,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-11-15T18:50:25.180236Z"
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+ "shell.execute_reply": "2024-11-18T17:01:41.286004Z"
}
},
"outputs": [
@@ -556,10 +556,10 @@
"execution_count": 11,
"metadata": {
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@@ -919,10 +919,10 @@
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@@ -956,10 +956,10 @@
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@@ -1053,17 +1053,17 @@
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{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:50:33 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:01:49 2024 UTC
"
],
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""
diff --git a/tutorials/04_torch_qgan.html b/tutorials/04_torch_qgan.html
index 87190b9c4..499abe2a7 100644
--- a/tutorials/04_torch_qgan.html
+++ b/tutorials/04_torch_qgan.html
@@ -591,7 +591,7 @@ 3.2. Definition of the quantum generator
-/tmp/ipykernel_3919/235628602.py:4: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
+/tmp/ipykernel_3935/235628602.py:4: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
sampler = Sampler(options={"shots": shots, "seed": algorithm_globals.random_seed})
@@ -665,7 +665,7 @@ 3.4. Create a generator and a discriminator
-/tmp/ipykernel_3919/3201077825.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_3935/3201077825.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = SamplerQNN(
@@ -843,7 +843,7 @@ 5. Model Training
-Fit in 71.56 sec
+Fit in 71.45 sec
@@ -921,7 +921,7 @@ 7. Conclusion
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:51:54 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:03:10 2024 UTC
diff --git a/tutorials/04_torch_qgan.ipynb b/tutorials/04_torch_qgan.ipynb
index 9b5d104b7..82dd3b9c3 100644
--- a/tutorials/04_torch_qgan.ipynb
+++ b/tutorials/04_torch_qgan.ipynb
@@ -79,10 +79,10 @@
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@@ -227,10 +227,10 @@
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@@ -264,10 +264,10 @@
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@@ -342,10 +342,10 @@
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@@ -353,7 +353,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_3919/235628602.py:4: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
+ "/tmp/ipykernel_3935/235628602.py:4: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
" sampler = Sampler(options={\"shots\": shots, \"seed\": algorithm_globals.random_seed})\n"
]
}
@@ -377,10 +377,10 @@
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@@ -423,10 +423,10 @@
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@@ -468,10 +468,10 @@
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@@ -479,7 +479,7 @@
"name": "stderr",
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"text": [
- "/tmp/ipykernel_3919/3201077825.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_3935/3201077825.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = SamplerQNN(\n"
]
}
@@ -517,10 +517,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:40.707124Z",
- "iopub.status.busy": "2024-11-15T18:50:40.706739Z",
- "iopub.status.idle": "2024-11-15T18:50:40.710057Z",
- "shell.execute_reply": "2024-11-15T18:50:40.709542Z"
+ "iopub.execute_input": "2024-11-18T17:01:57.244502Z",
+ "iopub.status.busy": "2024-11-18T17:01:57.244201Z",
+ "iopub.status.idle": "2024-11-18T17:01:57.247439Z",
+ "shell.execute_reply": "2024-11-18T17:01:57.246930Z"
},
"pycharm": {
"name": "#%%\n"
@@ -552,10 +552,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:40.711978Z",
- "iopub.status.busy": "2024-11-15T18:50:40.711590Z",
- "iopub.status.idle": "2024-11-15T18:50:42.294844Z",
- "shell.execute_reply": "2024-11-15T18:50:42.294170Z"
+ "iopub.execute_input": "2024-11-18T17:01:57.249456Z",
+ "iopub.status.busy": "2024-11-18T17:01:57.249094Z",
+ "iopub.status.idle": "2024-11-18T17:01:58.984413Z",
+ "shell.execute_reply": "2024-11-18T17:01:58.983769Z"
},
"pycharm": {
"name": "#%%\n"
@@ -594,10 +594,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:42.297449Z",
- "iopub.status.busy": "2024-11-15T18:50:42.296934Z",
- "iopub.status.idle": "2024-11-15T18:50:42.302042Z",
- "shell.execute_reply": "2024-11-15T18:50:42.301488Z"
+ "iopub.execute_input": "2024-11-18T17:01:58.987052Z",
+ "iopub.status.busy": "2024-11-18T17:01:58.986554Z",
+ "iopub.status.idle": "2024-11-18T17:01:58.991505Z",
+ "shell.execute_reply": "2024-11-18T17:01:58.990991Z"
},
"pycharm": {
"name": "#%%\n"
@@ -654,10 +654,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:50:42.304023Z",
- "iopub.status.busy": "2024-11-15T18:50:42.303641Z",
- "iopub.status.idle": "2024-11-15T18:51:53.871988Z",
- "shell.execute_reply": "2024-11-15T18:51:53.871290Z"
+ "iopub.execute_input": "2024-11-18T17:01:58.993467Z",
+ "iopub.status.busy": "2024-11-18T17:01:58.993086Z",
+ "iopub.status.idle": "2024-11-18T17:03:10.453293Z",
+ "shell.execute_reply": "2024-11-18T17:03:10.452651Z"
},
"pycharm": {
"name": "#%%\n"
@@ -678,7 +678,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fit in 71.56 sec\n"
+ "Fit in 71.45 sec\n"
]
}
],
@@ -761,10 +761,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:53.874075Z",
- "iopub.status.busy": "2024-11-15T18:51:53.873705Z",
- "iopub.status.idle": "2024-11-15T18:51:53.886490Z",
- "shell.execute_reply": "2024-11-15T18:51:53.885821Z"
+ "iopub.execute_input": "2024-11-18T17:03:10.455477Z",
+ "iopub.status.busy": "2024-11-18T17:03:10.455096Z",
+ "iopub.status.idle": "2024-11-18T17:03:10.468700Z",
+ "shell.execute_reply": "2024-11-18T17:03:10.468045Z"
}
},
"outputs": [],
@@ -785,10 +785,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:53.888622Z",
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- "iopub.status.idle": "2024-11-15T18:51:54.218828Z",
- "shell.execute_reply": "2024-11-15T18:51:54.218108Z"
+ "iopub.execute_input": "2024-11-18T17:03:10.470935Z",
+ "iopub.status.busy": "2024-11-18T17:03:10.470567Z",
+ "iopub.status.idle": "2024-11-18T17:03:10.798683Z",
+ "shell.execute_reply": "2024-11-18T17:03:10.797968Z"
},
"pycharm": {
"name": "#%%\n"
@@ -856,10 +856,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:54.221032Z",
- "iopub.status.busy": "2024-11-15T18:51:54.220623Z",
- "iopub.status.idle": "2024-11-15T18:51:54.228451Z",
- "shell.execute_reply": "2024-11-15T18:51:54.227804Z"
+ "iopub.execute_input": "2024-11-18T17:03:10.800865Z",
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+ "iopub.status.idle": "2024-11-18T17:03:10.808362Z",
+ "shell.execute_reply": "2024-11-18T17:03:10.807707Z"
},
"pycharm": {
"name": "#%%\n"
@@ -869,7 +869,7 @@
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:51:54 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:03:10 2024 UTC
"
],
"text/plain": [
""
diff --git a/tutorials/05_torch_connector.html b/tutorials/05_torch_connector.html
index 297f3d609..02b04fe82 100644
--- a/tutorials/05_torch_connector.html
+++ b/tutorials/05_torch_connector.html
@@ -547,9 +547,7 @@ A. Classification with PyTorch and
-/tmp/ipykernel_4291/3557703904.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- qnn1 = EstimatorQNN(
-/tmp/ipykernel_4291/3557703904.py:2: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_4307/3557703904.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn1 = EstimatorQNN(
@@ -744,7 +742,7 @@ B. Classification with PyTorch and
-/tmp/ipykernel_4291/998709632.py:14: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_4307/998709632.py:14: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn2 = SamplerQNN(
@@ -914,9 +912,7 @@ A. Regression with PyTorch and
-/tmp/ipykernel_4291/3970866756.py:16: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- qnn3 = EstimatorQNN(circuit=qc, input_params=[param_x], weight_params=[param_y])
-/tmp/ipykernel_4291/3970866756.py:16: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_4307/3970866756.py:16: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn3 = EstimatorQNN(circuit=qc, input_params=[param_x], weight_params=[param_y])
@@ -1093,11 +1089,29 @@ Step 1: Defining Data-loaders for train and test
+
+
+
+
+100.0%
+
+
+
+
+
+
+
Failed to download (trying next):
HTTP Error 403: Forbidden
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz
+Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
+
+Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
@@ -1105,7 +1119,7 @@ Step 1: Defining Data-loaders for train and test
-100.0%
+
@@ -1113,9 +1127,6 @@ Step 1: Defining Data-loaders for train and test
-Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
-
-Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 403: Forbidden
@@ -1139,11 +1150,6 @@ Step 1: Defining Data-loaders for train and test
@@ -1159,6 +1165,11 @@ Step 1: Defining Data-loaders for train and test
+Failed to download (trying next):
+HTTP Error 403: Forbidden
+
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
+Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw
@@ -1265,9 +1276,7 @@ Step 2: Defining the QNN and Hybrid Model
-/tmp/ipykernel_4291/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- qnn = EstimatorQNN(
-/tmp/ipykernel_4291/2402256359.py:10: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_4307/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = EstimatorQNN(
@@ -1403,11 +1412,9 @@ Step 4: Evaluation
-/tmp/ipykernel_4291/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- qnn = EstimatorQNN(
-/tmp/ipykernel_4291/2402256359.py:10: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_4307/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = EstimatorQNN(
-/tmp/ipykernel_4291/2057927024.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+/tmp/ipykernel_4307/2057927024.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
model5.load_state_dict(torch.load("model4.pt"))
@@ -1511,7 +1518,7 @@ Step 4: Evaluation
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:53:43 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:04:57 2024 UTC
diff --git a/tutorials/05_torch_connector.ipynb b/tutorials/05_torch_connector.ipynb
index 5c0690346..b44c28e63 100644
--- a/tutorials/05_torch_connector.ipynb
+++ b/tutorials/05_torch_connector.ipynb
@@ -34,10 +34,10 @@
"id": "banned-helicopter",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:57.021280Z",
- "iopub.status.busy": "2024-11-15T18:51:57.021087Z",
- "iopub.status.idle": "2024-11-15T18:51:59.145753Z",
- "shell.execute_reply": "2024-11-15T18:51:59.145156Z"
+ "iopub.execute_input": "2024-11-18T17:03:13.891435Z",
+ "iopub.status.busy": "2024-11-18T17:03:13.891233Z",
+ "iopub.status.idle": "2024-11-18T17:03:16.016196Z",
+ "shell.execute_reply": "2024-11-18T17:03:16.015611Z"
}
},
"outputs": [],
@@ -86,10 +86,10 @@
"id": "secure-tragedy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:59.148398Z",
- "iopub.status.busy": "2024-11-15T18:51:59.147894Z",
- "iopub.status.idle": "2024-11-15T18:51:59.265304Z",
- "shell.execute_reply": "2024-11-15T18:51:59.264603Z"
+ "iopub.execute_input": "2024-11-18T17:03:16.019064Z",
+ "iopub.status.busy": "2024-11-18T17:03:16.018441Z",
+ "iopub.status.idle": "2024-11-18T17:03:16.135406Z",
+ "shell.execute_reply": "2024-11-18T17:03:16.134793Z"
}
},
"outputs": [
@@ -147,10 +147,10 @@
"id": "fewer-desperate",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:59.267576Z",
- "iopub.status.busy": "2024-11-15T18:51:59.267043Z",
- "iopub.status.idle": "2024-11-15T18:51:59.725801Z",
- "shell.execute_reply": "2024-11-15T18:51:59.725165Z"
+ "iopub.execute_input": "2024-11-18T17:03:16.137355Z",
+ "iopub.status.busy": "2024-11-18T17:03:16.137142Z",
+ "iopub.status.idle": "2024-11-18T17:03:16.597269Z",
+ "shell.execute_reply": "2024-11-18T17:03:16.596593Z"
}
},
"outputs": [
@@ -182,10 +182,10 @@
"id": "humanitarian-flavor",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:59.727872Z",
- "iopub.status.busy": "2024-11-15T18:51:59.727589Z",
- "iopub.status.idle": "2024-11-15T18:51:59.734171Z",
- "shell.execute_reply": "2024-11-15T18:51:59.733599Z"
+ "iopub.execute_input": "2024-11-18T17:03:16.599445Z",
+ "iopub.status.busy": "2024-11-18T17:03:16.599169Z",
+ "iopub.status.idle": "2024-11-18T17:03:16.605700Z",
+ "shell.execute_reply": "2024-11-18T17:03:16.605056Z"
}
},
"outputs": [
@@ -201,9 +201,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_4291/3557703904.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " qnn1 = EstimatorQNN(\n",
- "/tmp/ipykernel_4291/3557703904.py:2: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_4307/3557703904.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn1 = EstimatorQNN(\n"
]
}
@@ -228,10 +226,10 @@
"id": "likely-grace",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:59.736171Z",
- "iopub.status.busy": "2024-11-15T18:51:59.735805Z",
- "iopub.status.idle": "2024-11-15T18:51:59.745468Z",
- "shell.execute_reply": "2024-11-15T18:51:59.744913Z"
+ "iopub.execute_input": "2024-11-18T17:03:16.607550Z",
+ "iopub.status.busy": "2024-11-18T17:03:16.607344Z",
+ "iopub.status.idle": "2024-11-18T17:03:16.617030Z",
+ "shell.execute_reply": "2024-11-18T17:03:16.616396Z"
}
},
"outputs": [
@@ -273,10 +271,10 @@
"id": "following-extension",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:51:59.747574Z",
- "iopub.status.busy": "2024-11-15T18:51:59.747072Z",
- "iopub.status.idle": "2024-11-15T18:52:11.411529Z",
- "shell.execute_reply": "2024-11-15T18:52:11.410780Z"
+ "iopub.execute_input": "2024-11-18T17:03:16.619007Z",
+ "iopub.status.busy": "2024-11-18T17:03:16.618805Z",
+ "iopub.status.idle": "2024-11-18T17:03:28.171693Z",
+ "shell.execute_reply": "2024-11-18T17:03:28.171136Z"
}
},
"outputs": [
@@ -457,10 +455,10 @@
"id": "efficient-bangkok",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:11.413886Z",
- "iopub.status.busy": "2024-11-15T18:52:11.413312Z",
- "iopub.status.idle": "2024-11-15T18:52:11.586856Z",
- "shell.execute_reply": "2024-11-15T18:52:11.586261Z"
+ "iopub.execute_input": "2024-11-18T17:03:28.173952Z",
+ "iopub.status.busy": "2024-11-18T17:03:28.173420Z",
+ "iopub.status.idle": "2024-11-18T17:03:28.342443Z",
+ "shell.execute_reply": "2024-11-18T17:03:28.341831Z"
}
},
"outputs": [
@@ -534,10 +532,10 @@
"id": "present-operator",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:11.589369Z",
- "iopub.status.busy": "2024-11-15T18:52:11.588921Z",
- "iopub.status.idle": "2024-11-15T18:52:11.602376Z",
- "shell.execute_reply": "2024-11-15T18:52:11.601699Z"
+ "iopub.execute_input": "2024-11-18T17:03:28.344619Z",
+ "iopub.status.busy": "2024-11-18T17:03:28.344243Z",
+ "iopub.status.idle": "2024-11-18T17:03:28.358002Z",
+ "shell.execute_reply": "2024-11-18T17:03:28.357318Z"
}
},
"outputs": [
@@ -552,7 +550,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_4291/998709632.py:14: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_4307/998709632.py:14: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn2 = SamplerQNN(\n"
]
}
@@ -601,10 +599,10 @@
"id": "marked-harvest",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:11.604644Z",
- "iopub.status.busy": "2024-11-15T18:52:11.604157Z",
- "iopub.status.idle": "2024-11-15T18:52:16.311676Z",
- "shell.execute_reply": "2024-11-15T18:52:16.311067Z"
+ "iopub.execute_input": "2024-11-18T17:03:28.359867Z",
+ "iopub.status.busy": "2024-11-18T17:03:28.359658Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.081103Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.080411Z"
}
},
"outputs": [
@@ -778,10 +776,10 @@
"id": "falling-electronics",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.313896Z",
- "iopub.status.busy": "2024-11-15T18:52:16.313488Z",
- "iopub.status.idle": "2024-11-15T18:52:16.467995Z",
- "shell.execute_reply": "2024-11-15T18:52:16.467326Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.083399Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.083039Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.237064Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.236377Z"
}
},
"outputs": [
@@ -850,10 +848,10 @@
"id": "amateur-dubai",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.470222Z",
- "iopub.status.busy": "2024-11-15T18:52:16.469843Z",
- "iopub.status.idle": "2024-11-15T18:52:16.550313Z",
- "shell.execute_reply": "2024-11-15T18:52:16.549643Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.239522Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.239039Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.315354Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.314821Z"
}
},
"outputs": [
@@ -905,10 +903,10 @@
"id": "brazilian-adapter",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.552429Z",
- "iopub.status.busy": "2024-11-15T18:52:16.552064Z",
- "iopub.status.idle": "2024-11-15T18:52:16.558412Z",
- "shell.execute_reply": "2024-11-15T18:52:16.557755Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.317510Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.317147Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.323382Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.322817Z"
}
},
"outputs": [
@@ -916,9 +914,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_4291/3970866756.py:16: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " qnn3 = EstimatorQNN(circuit=qc, input_params=[param_x], weight_params=[param_y])\n",
- "/tmp/ipykernel_4291/3970866756.py:16: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (1). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_4307/3970866756.py:16: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn3 = EstimatorQNN(circuit=qc, input_params=[param_x], weight_params=[param_y])\n"
]
}
@@ -962,10 +958,10 @@
"id": "bibliographic-consciousness",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.560356Z",
- "iopub.status.busy": "2024-11-15T18:52:16.559994Z",
- "iopub.status.idle": "2024-11-15T18:52:16.713221Z",
- "shell.execute_reply": "2024-11-15T18:52:16.712662Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.325295Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.324921Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.477310Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.476717Z"
}
},
"outputs": [
@@ -1021,10 +1017,10 @@
"id": "timely-happiness",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.715090Z",
- "iopub.status.busy": "2024-11-15T18:52:16.714881Z",
- "iopub.status.idle": "2024-11-15T18:52:16.834993Z",
- "shell.execute_reply": "2024-11-15T18:52:16.834434Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.479429Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.479045Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.599890Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.599170Z"
}
},
"outputs": [
@@ -1076,10 +1072,10 @@
"id": "otherwise-military",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.837153Z",
- "iopub.status.busy": "2024-11-15T18:52:16.836764Z",
- "iopub.status.idle": "2024-11-15T18:52:16.963857Z",
- "shell.execute_reply": "2024-11-15T18:52:16.963125Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.602028Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.601794Z",
+ "iopub.status.idle": "2024-11-18T17:03:33.746775Z",
+ "shell.execute_reply": "2024-11-18T17:03:33.746189Z"
}
},
"outputs": [],
@@ -1126,10 +1122,10 @@
"id": "worthy-charlotte",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:16.966411Z",
- "iopub.status.busy": "2024-11-15T18:52:16.966019Z",
- "iopub.status.idle": "2024-11-15T18:52:22.748978Z",
- "shell.execute_reply": "2024-11-15T18:52:22.748197Z"
+ "iopub.execute_input": "2024-11-18T17:03:33.749176Z",
+ "iopub.status.busy": "2024-11-18T17:03:33.748814Z",
+ "iopub.status.idle": "2024-11-18T17:03:36.343912Z",
+ "shell.execute_reply": "2024-11-18T17:03:36.343082Z"
}
},
"outputs": [
@@ -1147,13 +1143,7 @@
"Failed to download (trying next):\n",
"HTTP Error 403: Forbidden\n",
"\n",
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
]
},
@@ -3603,23 +3593,6 @@
"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n"
]
},
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Failed to download (trying next):\n",
- "HTTP Error 403: Forbidden\n",
- "\n",
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
@@ -3628,36 +3601,35 @@
"100.0%"
]
},
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Failed to download (trying next):\n",
+ "HTTP Error 403: Forbidden\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n",
"Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
"\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Failed to download (trying next):\n",
- "HTTP Error 403: Forbidden\n",
- "\n",
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n"
+ "\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Failed to download (trying next):\n",
+ "HTTP Error 403: Forbidden\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
]
},
@@ -4085,23 +4057,6 @@
"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n"
]
},
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Failed to download (trying next):\n",
- "HTTP Error 403: Forbidden\n",
- "\n",
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
@@ -4114,6 +4069,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "Failed to download (trying next):\n",
+ "HTTP Error 403: Forbidden\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
"Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
"\n"
]
@@ -4166,10 +4126,10 @@
"id": "medieval-bibliography",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:22.751366Z",
- "iopub.status.busy": "2024-11-15T18:52:22.750879Z",
- "iopub.status.idle": "2024-11-15T18:52:22.903703Z",
- "shell.execute_reply": "2024-11-15T18:52:22.902993Z"
+ "iopub.execute_input": "2024-11-18T17:03:36.346297Z",
+ "iopub.status.busy": "2024-11-18T17:03:36.345843Z",
+ "iopub.status.idle": "2024-11-18T17:03:36.497380Z",
+ "shell.execute_reply": "2024-11-18T17:03:36.496805Z"
}
},
"outputs": [
@@ -4207,10 +4167,10 @@
"id": "structural-chuck",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:22.905992Z",
- "iopub.status.busy": "2024-11-15T18:52:22.905603Z",
- "iopub.status.idle": "2024-11-15T18:52:22.916154Z",
- "shell.execute_reply": "2024-11-15T18:52:22.915578Z"
+ "iopub.execute_input": "2024-11-18T17:03:36.499581Z",
+ "iopub.status.busy": "2024-11-18T17:03:36.499188Z",
+ "iopub.status.idle": "2024-11-18T17:03:36.509689Z",
+ "shell.execute_reply": "2024-11-18T17:03:36.509148Z"
}
},
"outputs": [],
@@ -4264,10 +4224,10 @@
"id": "urban-purse",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:22.918496Z",
- "iopub.status.busy": "2024-11-15T18:52:22.918049Z",
- "iopub.status.idle": "2024-11-15T18:52:22.928681Z",
- "shell.execute_reply": "2024-11-15T18:52:22.928023Z"
+ "iopub.execute_input": "2024-11-18T17:03:36.511961Z",
+ "iopub.status.busy": "2024-11-18T17:03:36.511588Z",
+ "iopub.status.idle": "2024-11-18T17:03:36.521931Z",
+ "shell.execute_reply": "2024-11-18T17:03:36.521227Z"
}
},
"outputs": [
@@ -4275,9 +4235,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_4291/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " qnn = EstimatorQNN(\n",
- "/tmp/ipykernel_4291/2402256359.py:10: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_4307/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = EstimatorQNN(\n"
]
}
@@ -4310,10 +4268,10 @@
"id": "exclusive-productivity",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:22.930622Z",
- "iopub.status.busy": "2024-11-15T18:52:22.930238Z",
- "iopub.status.idle": "2024-11-15T18:52:22.936997Z",
- "shell.execute_reply": "2024-11-15T18:52:22.936508Z"
+ "iopub.execute_input": "2024-11-18T17:03:36.523969Z",
+ "iopub.status.busy": "2024-11-18T17:03:36.523614Z",
+ "iopub.status.idle": "2024-11-18T17:03:36.530586Z",
+ "shell.execute_reply": "2024-11-18T17:03:36.529984Z"
}
},
"outputs": [],
@@ -4364,10 +4322,10 @@
"id": "precious-career",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:52:22.938828Z",
- "iopub.status.busy": "2024-11-15T18:52:22.938520Z",
- "iopub.status.idle": "2024-11-15T18:53:42.626147Z",
- "shell.execute_reply": "2024-11-15T18:53:42.625494Z"
+ "iopub.execute_input": "2024-11-18T17:03:36.532444Z",
+ "iopub.status.busy": "2024-11-18T17:03:36.532244Z",
+ "iopub.status.idle": "2024-11-18T17:04:56.434595Z",
+ "shell.execute_reply": "2024-11-18T17:04:56.433900Z"
}
},
"outputs": [
@@ -4471,10 +4429,10 @@
"id": "spoken-stationery",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:42.628452Z",
- "iopub.status.busy": "2024-11-15T18:53:42.628061Z",
- "iopub.status.idle": "2024-11-15T18:53:42.715924Z",
- "shell.execute_reply": "2024-11-15T18:53:42.715233Z"
+ "iopub.execute_input": "2024-11-18T17:04:56.436991Z",
+ "iopub.status.busy": "2024-11-18T17:04:56.436531Z",
+ "iopub.status.idle": "2024-11-18T17:04:56.523582Z",
+ "shell.execute_reply": "2024-11-18T17:04:56.522899Z"
},
"scrolled": true
},
@@ -4513,10 +4471,10 @@
"id": "regulation-bread",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:42.717954Z",
- "iopub.status.busy": "2024-11-15T18:53:42.717733Z",
- "iopub.status.idle": "2024-11-15T18:53:42.722234Z",
- "shell.execute_reply": "2024-11-15T18:53:42.721685Z"
+ "iopub.execute_input": "2024-11-18T17:04:56.525716Z",
+ "iopub.status.busy": "2024-11-18T17:04:56.525313Z",
+ "iopub.status.idle": "2024-11-18T17:04:56.529556Z",
+ "shell.execute_reply": "2024-11-18T17:04:56.529045Z"
}
},
"outputs": [],
@@ -4546,10 +4504,10 @@
"id": "prospective-flooring",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:42.724330Z",
- "iopub.status.busy": "2024-11-15T18:53:42.723938Z",
- "iopub.status.idle": "2024-11-15T18:53:42.736870Z",
- "shell.execute_reply": "2024-11-15T18:53:42.736308Z"
+ "iopub.execute_input": "2024-11-18T17:04:56.531426Z",
+ "iopub.status.busy": "2024-11-18T17:04:56.531228Z",
+ "iopub.status.idle": "2024-11-18T17:04:56.543847Z",
+ "shell.execute_reply": "2024-11-18T17:04:56.543296Z"
}
},
"outputs": [
@@ -4557,11 +4515,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_4291/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " qnn = EstimatorQNN(\n",
- "/tmp/ipykernel_4291/2402256359.py:10: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (2). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_4307/2402256359.py:10: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = EstimatorQNN(\n",
- "/tmp/ipykernel_4291/2057927024.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ "/tmp/ipykernel_4307/2057927024.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" model5.load_state_dict(torch.load(\"model4.pt\"))\n"
]
},
@@ -4588,10 +4544,10 @@
"id": "spectacular-conservative",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:42.738742Z",
- "iopub.status.busy": "2024-11-15T18:53:42.738416Z",
- "iopub.status.idle": "2024-11-15T18:53:43.059871Z",
- "shell.execute_reply": "2024-11-15T18:53:43.059141Z"
+ "iopub.execute_input": "2024-11-18T17:04:56.545627Z",
+ "iopub.status.busy": "2024-11-18T17:04:56.545429Z",
+ "iopub.status.idle": "2024-11-18T17:04:56.875543Z",
+ "shell.execute_reply": "2024-11-18T17:04:56.874900Z"
}
},
"outputs": [
@@ -4634,10 +4590,10 @@
"id": "color-brave",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:43.062211Z",
- "iopub.status.busy": "2024-11-15T18:53:43.061828Z",
- "iopub.status.idle": "2024-11-15T18:53:43.234608Z",
- "shell.execute_reply": "2024-11-15T18:53:43.234051Z"
+ "iopub.execute_input": "2024-11-18T17:04:56.877813Z",
+ "iopub.status.busy": "2024-11-18T17:04:56.877377Z",
+ "iopub.status.idle": "2024-11-18T17:04:57.234514Z",
+ "shell.execute_reply": "2024-11-18T17:04:57.233883Z"
},
"tags": [
"nbsphinx-thumbnail"
@@ -4698,17 +4654,17 @@
"id": "related-wheat",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:43.236648Z",
- "iopub.status.busy": "2024-11-15T18:53:43.236431Z",
- "iopub.status.idle": "2024-11-15T18:53:43.244380Z",
- "shell.execute_reply": "2024-11-15T18:53:43.243753Z"
+ "iopub.execute_input": "2024-11-18T17:04:57.236829Z",
+ "iopub.status.busy": "2024-11-18T17:04:57.236418Z",
+ "iopub.status.idle": "2024-11-18T17:04:57.244159Z",
+ "shell.execute_reply": "2024-11-18T17:04:57.243661Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:53:43 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:04:57 2024 UTC
"
],
"text/plain": [
""
diff --git a/tutorials/07_pegasos_qsvc.html b/tutorials/07_pegasos_qsvc.html
index 8089184a4..dc322ee11 100644
--- a/tutorials/07_pegasos_qsvc.html
+++ b/tutorials/07_pegasos_qsvc.html
@@ -598,7 +598,7 @@ Pegasos Quantum Support Vector Classifier
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:53:49 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:05:03 2024 UTC
diff --git a/tutorials/07_pegasos_qsvc.ipynb b/tutorials/07_pegasos_qsvc.ipynb
index 1d75ace41..7dd030ddb 100644
--- a/tutorials/07_pegasos_qsvc.ipynb
+++ b/tutorials/07_pegasos_qsvc.ipynb
@@ -28,10 +28,10 @@
"id": "impressed-laser",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:46.356530Z",
- "iopub.status.busy": "2024-11-15T18:53:46.356330Z",
- "iopub.status.idle": "2024-11-15T18:53:46.997639Z",
- "shell.execute_reply": "2024-11-15T18:53:46.997010Z"
+ "iopub.execute_input": "2024-11-18T17:05:00.391019Z",
+ "iopub.status.busy": "2024-11-18T17:05:00.390640Z",
+ "iopub.status.idle": "2024-11-18T17:05:01.039172Z",
+ "shell.execute_reply": "2024-11-18T17:05:01.038475Z"
}
},
"outputs": [],
@@ -56,10 +56,10 @@
"id": "adolescent-composer",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:46.999868Z",
- "iopub.status.busy": "2024-11-15T18:53:46.999606Z",
- "iopub.status.idle": "2024-11-15T18:53:47.025971Z",
- "shell.execute_reply": "2024-11-15T18:53:47.025467Z"
+ "iopub.execute_input": "2024-11-18T17:05:01.041525Z",
+ "iopub.status.busy": "2024-11-18T17:05:01.041276Z",
+ "iopub.status.idle": "2024-11-18T17:05:01.068029Z",
+ "shell.execute_reply": "2024-11-18T17:05:01.067348Z"
}
},
"outputs": [],
@@ -94,10 +94,10 @@
"id": "dying-dispatch",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:47.028139Z",
- "iopub.status.busy": "2024-11-15T18:53:47.027683Z",
- "iopub.status.idle": "2024-11-15T18:53:47.030618Z",
- "shell.execute_reply": "2024-11-15T18:53:47.030115Z"
+ "iopub.execute_input": "2024-11-18T17:05:01.070266Z",
+ "iopub.status.busy": "2024-11-18T17:05:01.069870Z",
+ "iopub.status.idle": "2024-11-18T17:05:01.072755Z",
+ "shell.execute_reply": "2024-11-18T17:05:01.072233Z"
}
},
"outputs": [],
@@ -129,10 +129,10 @@
"id": "automated-allergy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:47.032699Z",
- "iopub.status.busy": "2024-11-15T18:53:47.032307Z",
- "iopub.status.idle": "2024-11-15T18:53:47.265384Z",
- "shell.execute_reply": "2024-11-15T18:53:47.264734Z"
+ "iopub.execute_input": "2024-11-18T17:05:01.074751Z",
+ "iopub.status.busy": "2024-11-18T17:05:01.074393Z",
+ "iopub.status.idle": "2024-11-18T17:05:01.306107Z",
+ "shell.execute_reply": "2024-11-18T17:05:01.305355Z"
}
},
"outputs": [],
@@ -167,10 +167,10 @@
"id": "representative-thumb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:47.267655Z",
- "iopub.status.busy": "2024-11-15T18:53:47.267370Z",
- "iopub.status.idle": "2024-11-15T18:53:47.996685Z",
- "shell.execute_reply": "2024-11-15T18:53:47.996072Z"
+ "iopub.execute_input": "2024-11-18T17:05:01.308504Z",
+ "iopub.status.busy": "2024-11-18T17:05:01.308224Z",
+ "iopub.status.idle": "2024-11-18T17:05:02.037676Z",
+ "shell.execute_reply": "2024-11-18T17:05:02.037075Z"
}
},
"outputs": [
@@ -209,10 +209,10 @@
"id": "judicial-pottery",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:47.999054Z",
- "iopub.status.busy": "2024-11-15T18:53:47.998522Z",
- "iopub.status.idle": "2024-11-15T18:53:48.002203Z",
- "shell.execute_reply": "2024-11-15T18:53:48.001662Z"
+ "iopub.execute_input": "2024-11-18T17:05:02.039929Z",
+ "iopub.status.busy": "2024-11-18T17:05:02.039477Z",
+ "iopub.status.idle": "2024-11-18T17:05:02.043031Z",
+ "shell.execute_reply": "2024-11-18T17:05:02.042397Z"
}
},
"outputs": [],
@@ -239,10 +239,10 @@
"id": "competitive-outdoors",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:48.004322Z",
- "iopub.status.busy": "2024-11-15T18:53:48.003880Z",
- "iopub.status.idle": "2024-11-15T18:53:49.317814Z",
- "shell.execute_reply": "2024-11-15T18:53:49.317104Z"
+ "iopub.execute_input": "2024-11-18T17:05:02.045024Z",
+ "iopub.status.busy": "2024-11-18T17:05:02.044563Z",
+ "iopub.status.idle": "2024-11-18T17:05:03.341548Z",
+ "shell.execute_reply": "2024-11-18T17:05:03.340844Z"
}
},
"outputs": [],
@@ -265,10 +265,10 @@
"id": "monetary-knife",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:49.320432Z",
- "iopub.status.busy": "2024-11-15T18:53:49.320068Z",
- "iopub.status.idle": "2024-11-15T18:53:49.827742Z",
- "shell.execute_reply": "2024-11-15T18:53:49.827051Z"
+ "iopub.execute_input": "2024-11-18T17:05:03.344149Z",
+ "iopub.status.busy": "2024-11-18T17:05:03.343764Z",
+ "iopub.status.idle": "2024-11-18T17:05:03.848109Z",
+ "shell.execute_reply": "2024-11-18T17:05:03.847461Z"
},
"tags": [
"nbsphinx-thumbnail"
@@ -338,17 +338,17 @@
"id": "imperial-promise",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:49.830130Z",
- "iopub.status.busy": "2024-11-15T18:53:49.829636Z",
- "iopub.status.idle": "2024-11-15T18:53:49.837103Z",
- "shell.execute_reply": "2024-11-15T18:53:49.836460Z"
+ "iopub.execute_input": "2024-11-18T17:05:03.850143Z",
+ "iopub.status.busy": "2024-11-18T17:05:03.849835Z",
+ "iopub.status.idle": "2024-11-18T17:05:03.857683Z",
+ "shell.execute_reply": "2024-11-18T17:05:03.857164Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:53:49 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:05:03 2024 UTC
"
],
"text/plain": [
""
diff --git a/tutorials/08_quantum_kernel_trainer.html b/tutorials/08_quantum_kernel_trainer.html
index fd5c3defb..7d4cd5fb5 100644
--- a/tutorials/08_quantum_kernel_trainer.html
+++ b/tutorials/08_quantum_kernel_trainer.html
@@ -631,13 +631,13 @@ Train the Quantum Kernel
{ 'optimal_circuit': None,
- 'optimal_parameters': { ParameterVectorElement(θ[0]): np.float64(2.1332457528054807)},
- 'optimal_point': array([2.13324575]),
- 'optimal_value': np.float64(12.177977810377222),
+ 'optimal_parameters': { ParameterVectorElement(θ[0]): np.float64(1.4599427101510223)},
+ 'optimal_point': array([1.45994271]),
+ 'optimal_value': np.float64(12.976733420932453),
'optimizer_evals': 30,
'optimizer_result': None,
'optimizer_time': None,
- 'quantum_kernel': <qiskit_machine_learning.kernels.trainable_fidelity_quantum_kernel.TrainableFidelityQuantumKernel object at 0x7f5fd1e52ef0>}
+ 'quantum_kernel': <qiskit_machine_learning.kernels.trainable_fidelity_quantum_kernel.TrainableFidelityQuantumKernel object at 0x7f9df5776500>}
@@ -668,7 +668,7 @@ Fit and Test the Model
-accuracy test: 0.9
+accuracy test: 1.0
@@ -716,7 +716,7 @@ Visualize the Kernel Training Process
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:54:52 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:06 2024 UTC
diff --git a/tutorials/08_quantum_kernel_trainer.ipynb b/tutorials/08_quantum_kernel_trainer.ipynb
index 3b5e2830b..21050b45b 100644
--- a/tutorials/08_quantum_kernel_trainer.ipynb
+++ b/tutorials/08_quantum_kernel_trainer.ipynb
@@ -33,10 +33,10 @@
"id": "1a646351",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:51.608469Z",
- "iopub.status.busy": "2024-11-15T18:53:51.608267Z",
- "iopub.status.idle": "2024-11-15T18:53:53.073724Z",
- "shell.execute_reply": "2024-11-15T18:53:53.072999Z"
+ "iopub.execute_input": "2024-11-18T17:05:05.924740Z",
+ "iopub.status.busy": "2024-11-18T17:05:05.924547Z",
+ "iopub.status.idle": "2024-11-18T17:05:07.385655Z",
+ "shell.execute_reply": "2024-11-18T17:05:07.384988Z"
}
},
"outputs": [],
@@ -104,16 +104,16 @@
"id": "2311cff1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:53.076224Z",
- "iopub.status.busy": "2024-11-15T18:53:53.075926Z",
- "iopub.status.idle": "2024-11-15T18:53:53.587429Z",
- "shell.execute_reply": "2024-11-15T18:53:53.586735Z"
+ "iopub.execute_input": "2024-11-18T17:05:07.388423Z",
+ "iopub.status.busy": "2024-11-18T17:05:07.387835Z",
+ "iopub.status.idle": "2024-11-18T17:05:07.914464Z",
+ "shell.execute_reply": "2024-11-18T17:05:07.913767Z"
}
},
"outputs": [
{
"data": {
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",
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",
"text/plain": [
""
]
@@ -200,10 +200,10 @@
"id": "60b58ede",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:53.589533Z",
- "iopub.status.busy": "2024-11-15T18:53:53.589136Z",
- "iopub.status.idle": "2024-11-15T18:53:53.792398Z",
- "shell.execute_reply": "2024-11-15T18:53:53.791762Z"
+ "iopub.execute_input": "2024-11-18T17:05:07.916466Z",
+ "iopub.status.busy": "2024-11-18T17:05:07.916246Z",
+ "iopub.status.idle": "2024-11-18T17:05:08.120096Z",
+ "shell.execute_reply": "2024-11-18T17:05:08.119424Z"
}
},
"outputs": [
@@ -257,10 +257,10 @@
"id": "a190efef",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:53.794687Z",
- "iopub.status.busy": "2024-11-15T18:53:53.794149Z",
- "iopub.status.idle": "2024-11-15T18:53:53.798449Z",
- "shell.execute_reply": "2024-11-15T18:53:53.797883Z"
+ "iopub.execute_input": "2024-11-18T17:05:08.122348Z",
+ "iopub.status.busy": "2024-11-18T17:05:08.122044Z",
+ "iopub.status.idle": "2024-11-18T17:05:08.126030Z",
+ "shell.execute_reply": "2024-11-18T17:05:08.125434Z"
}
},
"outputs": [],
@@ -303,10 +303,10 @@
"id": "9d26212c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:53:53.800514Z",
- "iopub.status.busy": "2024-11-15T18:53:53.800118Z",
- "iopub.status.idle": "2024-11-15T18:54:48.356327Z",
- "shell.execute_reply": "2024-11-15T18:54:48.355628Z"
+ "iopub.execute_input": "2024-11-18T17:05:08.128084Z",
+ "iopub.status.busy": "2024-11-18T17:05:08.127753Z",
+ "iopub.status.idle": "2024-11-18T17:06:01.896701Z",
+ "shell.execute_reply": "2024-11-18T17:06:01.896065Z"
}
},
"outputs": [
@@ -315,13 +315,13 @@
"output_type": "stream",
"text": [
"{ 'optimal_circuit': None,\n",
- " 'optimal_parameters': { ParameterVectorElement(θ[0]): np.float64(2.1332457528054807)},\n",
- " 'optimal_point': array([2.13324575]),\n",
- " 'optimal_value': np.float64(12.177977810377222),\n",
+ " 'optimal_parameters': { ParameterVectorElement(θ[0]): np.float64(1.4599427101510223)},\n",
+ " 'optimal_point': array([1.45994271]),\n",
+ " 'optimal_value': np.float64(12.976733420932453),\n",
" 'optimizer_evals': 30,\n",
" 'optimizer_result': None,\n",
" 'optimizer_time': None,\n",
- " 'quantum_kernel': }\n"
+ " 'quantum_kernel': }\n"
]
}
],
@@ -348,10 +348,10 @@
"id": "e716655f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:48.358469Z",
- "iopub.status.busy": "2024-11-15T18:54:48.358048Z",
- "iopub.status.idle": "2024-11-15T18:54:50.842388Z",
- "shell.execute_reply": "2024-11-15T18:54:50.841749Z"
+ "iopub.execute_input": "2024-11-18T17:06:01.898693Z",
+ "iopub.status.busy": "2024-11-18T17:06:01.898487Z",
+ "iopub.status.idle": "2024-11-18T17:06:04.337729Z",
+ "shell.execute_reply": "2024-11-18T17:06:04.337131Z"
}
},
"outputs": [
@@ -359,7 +359,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "accuracy test: 0.9\n"
+ "accuracy test: 1.0\n"
]
}
],
@@ -396,16 +396,16 @@
"id": "0cb85c46",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:50.844668Z",
- "iopub.status.busy": "2024-11-15T18:54:50.844274Z",
- "iopub.status.idle": "2024-11-15T18:54:52.952560Z",
- "shell.execute_reply": "2024-11-15T18:54:52.951802Z"
+ "iopub.execute_input": "2024-11-18T17:06:04.339964Z",
+ "iopub.status.busy": "2024-11-18T17:06:04.339576Z",
+ "iopub.status.idle": "2024-11-18T17:06:06.373298Z",
+ "shell.execute_reply": "2024-11-18T17:06:06.372576Z"
}
},
"outputs": [
{
"data": {
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ssr2hQ4fa09PTC+7BAHa7fcGCBfbAwMBcX4cNGza079u3z9PdLLL4nlA43Pk+h+fateXLl1t6rkNCQuz/+9//XLY1btw4u5eXV65t9OjRw56cnFxIj6zkY0QbUABiY2MlSTVq1NDo0aM1d+5cRUdHa926dZoyZYquvfZaSdLnn3+ep7+woWjKyMjQsGHDlJaWpqpVq3q6O3CTV155RU888YSSkpLUsGFDTZ48WStXrtTmzZv1448/avLkyWrTpo28vPiRWtxcvHhRPXr00O+//y5Jat68uWbOnKl169Zp2bJlGjt2rAICAiRJ7777rl577TVPdhcu1KpVS126dMnTff/xj3/ok08+kSS1aNFCs2bNUnR0tGbNmqUWLVpIkqZPn66XXnrJbf0FrrR582bdd999SkhIUPny5fXqq69q7dq1WrFihR555BFJ0t69e9WjRw8lJiZ6uLdFH98TCo473+fwXJvVrFlTAwYM0Ntvv6158+Zp3bp1WrNmjebMmaN7771X3t7eOnXqlO666y5t3bo1xzamTp2qCRMmKCMjQ/Xr19cnn3yi6OhozZ8/X507d5YkLV68WEOGDCnMh1ayebrSB5REPXr0sM+ZM8eelpaW4/mTJ0/aGzZs6PwLwqpVqwq5hygIb775pl2SvXHjxvYxY8Ywoq0E+PHHH7P8ZTY1NTXXLKNTi59vvvnG+fVt3bp1jt+zN27caC9Tpoxdkr1ixYr2ixcveqCnyMnYsWPtCxcutB8/ftxut9vtf/zxx1WPXtmzZ49zFHKrVq3s58+fz3I+KSnJ3qpVK7sku4+PD6OJUGAco4R8fHzsa9euzXb+9ddfd76+x40bV/gdLAb4nlA43PU+h+faLLfnOLPvvvvO+VxHRkZmO//XX3/Zg4KC7JLstWrVsp88eTLbNXr16sX7Fjej0AZ4yMKFC7NMSULxdujQIXv58uXtkuwrV660jxs3jh9YxVx6err9uuuus0uy33jjjRRYSqCnnnrK+f90wYIFueYiIyOduW3bthViD3E18vKm+rHHHnPeZ926dTlm1q1b58yMGDHCjT0GLvn111+dr7FHH300x0x6err9+uuvdxb9Xf3hB5fwPcFzrLzP4bl2n0aNGjmnkF7ptddecz6Hs2bNyvH+R44csXt7e9sl2bt3717Q3S0VmOcCeIhjmK4kHThwwIM9gTs8/vjjOnfunAYOHKiOHTt6ujtwg2XLlmnfvn2SLm1w4ePj4+Eewd1SU1Odx/Xq1cs1V79+/Rzvg+LNbrfr+++/lyQ1btxYt956a465W2+9VY0aNZIkff/997Lb7YXWR5QO8+fPdx4PHjw4x4yXl5cGDBggSTp79qx+/vnnwuhaqcL3BPcxvc/huXavChUqSJIuXLiQ7Zzj+0tgYKB69+6d4/1DQ0N1xx13SJJWrFjB9HQ3oNAGeEhKSorz2Nvb24M9QX59/fXXWrRokYKDg/XGG294ujtwk2+++UaSZLPZ1LNnT+ftp0+f1r59+3T69GlPdQ1u4vjlXZIOHjyYa87xJsFms+m6664r8H6hcPzxxx/OtYZMfyBxnD927JhiYmIKumsoZRw7NAYEBKhly5a55jK/TtesWVPg/Spt+J7gPqb3OTzX7rNnzx5t2bJF0qWiZWapqamKjo6WJLVu3Vq+vr65tuN4nlNSUrRx48aC6WwpQqEN8JBVq1Y5j6+//noP9gT5cfbsWY0ePVqS9NprrykkJMTDPYK7rF+/XpJUp04dVahQQV999ZWaNWumypUrq2HDhqpcubIaNWqkN954I8svlCg+7r//fgUGBkq69P83PT09W2bz5s1avHixJKl///7OPIq/nf/X3r0HRXGlbQB/RhCGixcuoiKEW5aIopKI0QSiqEB2pQwChg0Ygy7IaumWZqPGrK5gGUWzq1Ax5mIhoDFqYpUokd2VoIgEJawoEVBERU1YxAh4QYaLw/T3Bx8dCMxwsYdReH5VVPXMOX367UPTTL9z+vSlS+Lyby9Ofqt1+eXLl7UWE/VPLcfU888/r3H0NI9D7eI5QTqdXeewr5+MQqHA1atXsX37dkybNg1KpRIAsGLFijb1SkpKxM827OfexftgiHRApVJhy5Yt4uvg4GAdRkNPYvXq1aioqICHhwfCw8N1HQ5JRKVSobi4GABgaWmJ5cuX4+OPP25Xr6SkBKtWrUJycjJSU1MxdOjQXo6UnoSlpSW+/PJLhISEIDs7G5MmTcKKFSvg7OyMR48eITs7G9u2bUNjYyNeeuklbNu2Tdchk4TKysrEZRsbG411bW1txeWff/5ZazFR/1NfX4/KykoAnR+HZmZmMDExQW1tLY9DLeA5QRpduc5hX3dfUlKS2lvLAWDNmjUIDQ1t8x77WXc4oo1IB2JjY8VhvIGBgRpvE6CnV1ZWFuLj46Gvr4/PP/8cMplM1yGRRB48eACVSgUAKCgowMcff4yRI0di3759qK6uhkKhQGZmpjinyJkzZ/hI9GfUG2+8gby8PERERCA/Px9hYWF45ZVX4OPjg+joaBgbGyMuLg5ZWVkYPny4rsMlCbWeg8bU1FRjXRMTE3H50aNHWouJ+p/uHIfAr8cij0Pp8Zwgja5c57CvpePm5obc3FzExMS0uxZhP+sOE21EvSwzMxNr1qwBAFhZWeGzzz7TcUTUE42NjYiMjIQgCHj33Xfh6uqq65BIQrW1teJyfX09jI2NkZGRgXnz5sHMzAxGRkaYOnUqTp48iQkTJgAAkpOT8cMPP+gqZOqhxsZG7N27V+0ky3fu3MG+ffuQnp6ug+hIm1pPGq1p3hoAMDQ0FJfr6uq0FhP1P905DoFfj0Ueh9LjOeHJdfU6h33dfXPmzEFBQQEKCgqQm5uLAwcOICAgAPn5+QgJCcGxY8farcN+1h0m2oh6UVFREQICAqBUKiGXy3Ho0CFYWVnpOizqgc2bN6O4uBjPPfccoqKidB0OSUwul7d5HRER0Wbi/BZGRkbYtGmT+Prrr7/WemwkndraWnh7eyMmJgbV1dVYvXo1Ll++jIaGBjx48ABpaWnw9PTEuXPnMGfOHGzfvl3XIZOEWv+dd/Y02dbzMBoZGWktJup/unMcAr8eizwOpcdzwpPpznUO+7r7hg4dCldXV7i6umLSpEl46623cPjwYezduxelpaXw9/dHUlJSm3XYz7rDRBtRL7lx4wZ8fX1x79496Onp4eDBg5g6daquw6IeKC4uRkxMDABgx44dbYZaU9/Q8pj0Fr6+vmrrzpw5U5y8+r///a9W4yJpRUdHIysrCwCwe/dubN26FaNHj4aBgQEGDx4MHx8fZGRkYPr06RAEAatWrcKPP/6o46hJKq3/zju7Tab1KNeu3N5H1FXdOQ6BX49FHofS4zmh57p7ncO+ls78+fPx5ptvQqVSYdmyZaiurhbL2M+6w4chEPWC8vJyeHt7o7y8HDKZDAkJCfD399d1WNRDsbGxaGxshKOjIxQKBQ4ePNiuTmFhobh88uRJVFRUAABmz57NxNwzwNDQEMOGDcPdu3cBtJ0g9rfkcjksLS1RUVEh1qennyAISEhIAAA4OzsjLCysw3r6+vrYuHEjPD09oVKpkJSUhNjY2N4MlbSk9cTQrSeM7kjriaE1nQ+Iuksul8PCwgJVVVWdHof37t0TL4Z5HEqP54Se6cl1DvtaWv7+/vjmm29QW1uL//znP+JDEdjPusNEG5GWVVZWwsfHB6WlpQCaR0C98847Oo6KnkTL0OrS0lKEhIR0Wn/jxo3i8o0bN5hoe0aMHTsWp06dAgDx0ejqtJS3jGyjp9+dO3fEb31ffPFFjXVbT+Tc8jRaevaNGTNGXO7s99q63MXFRWsxUf80ZswYZGVl4dq1a1AqlWr/l/A41C6eE7qvp9c57GtpDRs2TFy+deuWuOzs7Aw9PT00NTWxn3sZbx0l0qIHDx7g9ddfx6VLlwAAW7ZswdKlS3UcFRF1RetbHlo+QHbk4cOHqKysBACMGjVK63GRNFpfyCqVSo11Hz9+3OF69GxzcHCAtbU1gOYJvDU5ffo0gOa/cXt7e22HRv2Mp6cngOZbt/Ly8tTWa32cenh4aD2u/obnhO55kusc9rW0/ve//4nLrW/7NDAwwMsvvwwAOHv2rMZ52lp+D4aGhnB3d9dSpP0HE21EWqJQKODn54fz588DANauXYv3339fx1GRFJKSkiAIgsaf1g9IyMjIEN/nB4RnR1BQkLicnJystl5ycrL4tMrXXntN63GRNMzNzTF48GAAzR8+NSXbWl8EODg4aD026h0ymUy8vam4uBg5OTkd1svJyRG/6ff394dMJuu1GKl/mDNnjricmJjYYR2VSoW9e/cCaJ4Uffr06b0RWr/Cc0LXPel1DvtaWocOHRKXx40b16as5fzy8OFDHD58uMP1y8rKxKerz5w5s91cxdQDAhFJrqGhQfD19RUACACE5cuX6zok6mVRUVHi7z8jI0PX4VAP/eEPfxAACAMGDBDS09Pbld++fVuwsbERAAgGBgZCWVmZDqKkngoJCRH/TqOjozusU11dLYwZM0asd/z48V6Okrrqxo0b4u8pLCysS+tcuXJF0NPTEwAI7u7ugkKhaFOuUCgEd3d3AYCgr68vlJSUaCFyIkF47bXXxOPszJkz7co/+ugj8fiOiorq/QCfQTwnaIdU1zns684lJiYKdXV1Guts375d/F04ODgISqWyTXlVVZUwZMgQAYBgZ2cnVFZWtilXKpXC7Nmzed0iMZkg/P/X8EQkmaCgIPEbgxkzZiAuLk7jNzAGBgZwdnburfCoF0RHR2PDhg0Amke0eXl56TYg6pGSkhJMnjwZ9+/fh1wux4oVKzBr1iwYGRkhNzcXMTEx4uSyW7duxerVq3UcMXVHcXExJk6cCIVCAaD5YSVhYWFwdHREfX09cnJyEBcXh59++glA87e8Ld/4ku59//33uHbtmvi6srISq1atAtB8W11ERESb+gsWLOiwnQ8++ABbtmwB0Dxf3/vvvw8nJydcv34dW7duxYULF8R6mzdv1sKeEAEXLlyAh4cH6urqYGpqir/97W+YPn066urqcPDgQezatQtA85xL586d44iTDvCc0DukvM5hX2tmb2+PmpoaBAUFwdPTE05OTjA1NUVNTQ0KCgrw1VdfITs7G0BzP6empsLb27tdO1988QUWL14MAHBycsLatWsxbtw4lJeXIy4uDhkZGQCAkJAQ7N+/v/d2sC/TdaaPqC/C/38j0NUfOzs7XYdMEuOItr4jKytLGD58uNq/X5lMJqxbt07XYVIPfffdd4KlpWWn5+kZM2YI1dXVug6XWgkLC+vW/1p1mpqahD/96U8a1w0PDxeampp6ce+oP0pJSREGDx6s9jh0dnYWrl69quswn1o8J/QOKa9z2Nea2dnZdamPbWxshLS0NI1trV+/XpDJZGrbmDVrVqej56jrOEcbERGRBp6enigqKkJUVBQmTJiAwYMHQy6Xw8HBAQsXLkReXl6bJ8vSs8Xb2xvFxcXYunUrvLy8MGzYMAwcOBBGRkZwcHBAcHAwjhw5gvT0dJiZmek6XNKCAQMGYPfu3UhNTYW/vz+sra1hYGAAa2tr+Pv741//+hfi4+MxYAA/NpN2zZ49GxcvXsS7774LZ2dnGBsbY+jQoXB3dxdH9zz//PO6DrPP4zmh97CvNTt+/Di2bduGwMBAjB8/HsOHD4e+vj4GDRoEJycnBAUFITExEVeuXIGPj4/GtjZs2IDvv/8eoaGhsLW1hYGBAaysrODj44P9+/cjNTUVcrm8l/as7+Oto0RERERERERERBLon6lhIiIiIiIiIiIiiTHRRkREREREREREJAEm2oiIiIiIiIiIiCTARBsREREREREREZEEmGgjIiIiIiIiIiKSABNtREREREREREREEmCijYiIiIiIiIiISAJMtBEREREREREREUmAiTYiIiIiIiIiIiIJMNFGREREREREREQkASbaiIioX7C3t4dMJsOCBQt0HQoREREREfVRTLQRET3DTp06BZlMBplMhujoaF2HQ0RERERE1K8x0UZE1EfdvHlTTMIlJSXpOhyt8PLygkwmg5eXl65DISIiIiIigr6uAyAiIuoNN2/e1HUIRERERETUx3FEGxERERERERERkQSYaCMiIiIiIiIiIpIAE21ERH2QTCaDg4OD+HrhwoXifG2dPTzh/PnzWLx4MV544QWYmprCxMQEL7zwApYsWYKSkhK120xKShLbvnnzJhoaGhAXF4cpU6bA0tKy3TYbGxvx7bffYtmyZZg0aRLMzMwwcOBAWFhYYPLkyYiOjkZlZWWH21qwYAFkMhkyMzMBAJmZme32z97evs06XX3q6Lfffou5c+fCxsYGhoaGsLCwwCuvvIItW7bg0aNHXd5/lUqFXbt24dVXX4WZmRlMTEwwfvx4bNq0CQqFQmMMeXl5CA8Ph7OzM0xMTCCXy2Fra4uJEydi6dKlSElJgSAIGtsgIiIiIqLexznaiIgIAKBSqbBy5UrExcW1S+KUlJSgpKQE8fHx2LlzJyIjIzW2VVlZiYCAAOTn56utExkZiT179rR7v7q6Grm5ucjNzcUnn3yCo0ePwsPDo0f71B319fUIDQ1FcnJyu3hycnKQk5ODHTt2IDU1FW5ubhrbUigU8PX1xYkTJ9q8X1BQgIKCAqSkpODkyZMwMTFpt25sbCxWrlwJlUrV5v2ysjKUlZXh/Pnz+PTTT1FTUwNTU9Oe7SwREREREWkFE21ERH1QQUEBysvL8frrrwMAPvzwQ/j7+7epY2Vl1eb1X/7yF3z66acAgKlTp2LBggVwdHSEsbExfvzxR8TFxaGoqAh//vOfMWLECLzxxhtqtx8eHo6CggK88847+OMf/4gRI0bgp59+gqGhoVhHqVTC0dERAQEBePnll/Hcc89BX18ft27dQnp6OhISElBVVYWAgAAUFha2iXfTpk1YuXIlFi5ciHPnzsHd3R2JiYltYjAwMOhWn4WFhYlJtgkTJuC9996Di4sLqqurcfDgQSQlJaG8vBwzZ87ExYsXMWrUKLVtLVq0CDk5OQgLC0NwcLC4/x999BHOnj2L3NxcfPjhh4iJiWmz3sWLF8Ukm4ODA5YtWwY3NzeYm5ujpqYGV65cQUZGBo4ePdqtfSMiIiIiol4iEBHRMysjI0MAIAAQoqKi2pTduHFDLEtMTNTYTlpamlg3Pj6+wzp1dXXCjBkzBACCnZ2d8Pjx4zbliYmJYhua2mlx7do1QaVSqS2/ePGiYGpqKgAQ1q1b12GdadOmCQCEadOmadyWIAiCnZ2dAEAICwtrV3bs2DEx7pkzZwoNDQ3t6uzatUusExwc3K78t/v/5ZdftqtTX18vuLq6CgAECwuLdn3497//XQAgmJiYCBUVFWr35f79+0JTU1On+0xERERERL2Lc7QRERG2bNkCAAgKCkJ4eHiHdeRyOT755BMAwK1bt5CRkaG2vRkzZqhtp4WTkxNkMpna8nHjxiEiIgIAcOTIEY1tPamdO3cCAAYOHIjExMQOR8MtWrQI3t7eAIDDhw/j9u3batsLDAzE22+/3e59Q0NDLFu2DABQVVWFS5cutSmvqKgAADg7O2P48OFq2x8yZAgGDOC/cCIiIiKipw0/pRMR9XMPHz7EqVOnAABz587VWNfFxQWWlpYAgLNnz6qtN2/evG7Hce/ePVy/fh1FRUUoLCxEYWEhhg4dCgC4dOkSHj9+3O02u0KpVIoPVfD19YWtra3auosWLRLXaemzjmja/4kTJ4rLpaWlbcpGjhwJoHl/c3NzO42diIiIiIieLpyjjYion7tw4YI48X5ISAhCQkK6tF7L6KuOjB8/vkttFBQUIDY2Fv/+9781tqdSqXDv3r1288pJobS0VHwK6OTJkzXWbV1eWFiott7o0aPVlpmbm4vLNTU1bcpCQkIQExODhoYGeHh44Pe//z38/Pzg6emJsWPHahwBSEREREREuscRbURE/dwvv/zSo/VaklMdMTMz63T93bt346WXXkJiYqLGJFuLurq6bsXXVdXV1eJyZ4m8ESNGdLjebxkbG6sta33LZ1NTU5uy0aNH48CBAzAzM4NSqcSxY8ewZMkSjBs3DlZWVpg/fz6ysrI0xkhERERERLrDEW1ERP1c62TPF198gVdffbVL62lKpunp6Wlct7i4GIsXL4ZSqYSVlRVWrVqFGTNmwN7eHoMGDcLAgQMBAAkJCeJcb4IgdCmuJ/E0jBgLCgqCt7c3vv76axw/fhxZWVm4e/cuKisrsW/fPuzbtw9hYWFISEjgPG1ERERERE8ZJtqIiPo5CwsLcdnY2Biurq5a32ZSUhKUSiX09PSQmZmp9lZLTaPGpNL6Vs47d+5orNt65F3r9aQ2ZMgQREZGIjIyEgBw+fJlHD16FDt27EB5eTn27NmDF198EcuXL9daDERERERE1H38KpyIqI/q6ugsNzc3sW52drY2QxIVFRUBACZMmKBxPrNz585pbEeKEWiOjo7irZ4//PCDxrqtH1DQGwnJFi4uLlizZg1ycnJgYmICAPjmm296bftERERERNQ1TLQREfVRcrlcXG5oaFBbb9iwYZgyZQoAYP/+/bh7967WY1MqlQCA2tpatXVu376NlJQUje207KOm/euMvr4+pk2bBgD47rvvUFZWprZufHy8uI6Xl1ePt9lTtra2cHZ2BgBUVlb2+vaJiIiIiEgzJtqIiPooCwsLGBgYAACuX7+use66desAAA8fPsTcuXNx//59tXUbGhqwc+dO1NfX9zi23/3udwCAq1ev4syZM+3KFQoFQkNDO30AwsiRIwE0Pzn0SeZwW7p0KQCgsbER4eHhePz4cbs6CQkJSEtLAwAEBgaK25bSkSNHNPb9zz//jOLiYgCAg4OD5NsnIiIiIqInw0QbEVEfpa+vj0mTJgFoThIdOHAAly9fxrVr13Dt2rU285/NmjVLnO/r9OnTcHFxwYYNG3DixAnk5+cjOzsbe/bsQUREBEaOHIlly5aJo9J6Yv78+QAAlUoFPz8/bN68GadPn0Zubi4+++wzuLm54dSpU/Dw8NDYTsuDG3755Rf89a9/RV5enrh/t27d6nI8fn5+ePPNNwEAaWlpmDJlCr766ivk5eUhPT0dERERiIiIANA8N9v27dt7studiouLw6hRoxAcHIzPP/8cmZmZyM/PR0ZGBv7xj3/Aw8NDTD4uXrxYKzEQEREREVHP8WEIRER92AcffIDZs2ejqqoKoaGhbcqioqIQHR0tvo6NjYW5uTk2btyIioqKNmW/ZWJi0umTRTWZNGkSNmzYgKioKNy/fx9r165tV+e9996Dq6urxnnj3nrrLcTExKC0tBRxcXGIi4sTy+zs7HDz5s0ux7R3714olUokJyfj/PnzePvtt9vVsba2RmpqKkaNGtXldrtLoVDg0KFDOHToUIflAwYMwIYNGzBnzhytxUBERERERD3DEW1ERH2Yn58fTpw4AX9/f1hbW2PgwIFq68pkMqxfvx4lJSVYvXo13N3dYW5uDj09PQwaNAhjxozBvHnzsGfPHty+fRtGRkZPFNv69euRmpoKX19fmJmZwcDAADY2NggMDERaWhr++c9/dtqGqakpzpw5g+XLl8PFxUV8qEFPyOVyHD58GCkpKQgMDIS1tTUMDAxgZmaGyZMnIyYmBleuXIGbm1uPt9GZAwcOYNeuXQgNDYWbmxtGjBgBfX19mJqaYuzYsViyZAkuXLgg3upLRERERERPF5nwJJPaEBEREREREREREQCOaCMiIiIiIiIiIpIEE21EREREREREREQSYKKNiIiIiIiIiIhIAky0ERERERERERERSYCJNiIiIiIiIiIiIgkw0UZERERERERERCQBJtqIiIiIiIiIiIgkwEQbERERERERERGRBJhoIyIiIiIiIiIikgATbURERERERERERBJgoo2IiIiIiIiIiEgCTLQRERERERERERFJgIk2IiIiIiIiIiIiCTDRRkREREREREREJAEm2oiIiIiIiIiIiCTwf3Ar62rmI1XzAAAAAElFTkSuQmCC",
+ "image/png": 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",
"text/plain": [
""
]
@@ -434,17 +434,17 @@
"id": "aa6e50bc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:52.954754Z",
- "iopub.status.busy": "2024-11-15T18:54:52.954559Z",
- "iopub.status.idle": "2024-11-15T18:54:52.962385Z",
- "shell.execute_reply": "2024-11-15T18:54:52.961850Z"
+ "iopub.execute_input": "2024-11-18T17:06:06.375495Z",
+ "iopub.status.busy": "2024-11-18T17:06:06.375095Z",
+ "iopub.status.idle": "2024-11-18T17:06:06.382643Z",
+ "shell.execute_reply": "2024-11-18T17:06:06.382010Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:54:52 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:06 2024 UTC
"
],
"text/plain": [
""
diff --git a/tutorials/09_saving_and_loading_models.html b/tutorials/09_saving_and_loading_models.html
index 189a9e9af..3ceedc815 100644
--- a/tutorials/09_saving_and_loading_models.html
+++ b/tutorials/09_saving_and_loading_models.html
@@ -446,9 +446,9 @@ Saving, Loading Qiskit Machine Learning Models and Continuous Training
-/tmp/ipykernel_12069/2924877470.py:1: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
+/tmp/ipykernel_12085/2924877470.py:1: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
sampler1 = Sampler()
-/tmp/ipykernel_12069/2924877470.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
+/tmp/ipykernel_12085/2924877470.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.
sampler2 = Sampler()
@@ -730,7 +730,7 @@ 2. Train a model and save it
-<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x7f9e32826a70>
+<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x7f4feb66dcc0>
Let’s see how well our model performs after the first step of training.
@@ -807,7 +807,7 @@ 3. Load a model and continue training
-<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x7f9e329ca4a0>
+<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x7f4feb6afd00>
@@ -874,7 +874,7 @@ 3. Load a model and continue training
-<matplotlib.collections.PathCollection at 0x7f9e316b4940>
+<matplotlib.collections.PathCollection at 0x7f4fea155e10>
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:55:15 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:28 2024 UTC
diff --git a/tutorials/09_saving_and_loading_models.ipynb b/tutorials/09_saving_and_loading_models.ipynb
index 42888363e..876349a6c 100644
--- a/tutorials/09_saving_and_loading_models.ipynb
+++ b/tutorials/09_saving_and_loading_models.ipynb
@@ -32,10 +32,10 @@
"id": "exposed-cholesterol",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:54.988383Z",
- "iopub.status.busy": "2024-11-15T18:54:54.988178Z",
- "iopub.status.idle": "2024-11-15T18:54:56.442775Z",
- "shell.execute_reply": "2024-11-15T18:54:56.442021Z"
+ "iopub.execute_input": "2024-11-18T17:06:08.605970Z",
+ "iopub.status.busy": "2024-11-18T17:06:08.605749Z",
+ "iopub.status.idle": "2024-11-18T17:06:10.042907Z",
+ "shell.execute_reply": "2024-11-18T17:06:10.042274Z"
}
},
"outputs": [],
@@ -70,10 +70,10 @@
"id": "charming-seating",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:56.445365Z",
- "iopub.status.busy": "2024-11-15T18:54:56.445054Z",
- "iopub.status.idle": "2024-11-15T18:54:56.448739Z",
- "shell.execute_reply": "2024-11-15T18:54:56.448112Z"
+ "iopub.execute_input": "2024-11-18T17:06:10.045433Z",
+ "iopub.status.busy": "2024-11-18T17:06:10.044925Z",
+ "iopub.status.idle": "2024-11-18T17:06:10.048660Z",
+ "shell.execute_reply": "2024-11-18T17:06:10.048020Z"
}
},
"outputs": [
@@ -81,9 +81,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_12069/2924877470.py:1: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
+ "/tmp/ipykernel_12085/2924877470.py:1: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
" sampler1 = Sampler()\n",
- "/tmp/ipykernel_12069/2924877470.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
+ "/tmp/ipykernel_12085/2924877470.py:3: DeprecationWarning: The class ``qiskit.primitives.sampler.Sampler`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseSamplerV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Sampler` class is `StatevectorSampler`.\n",
" sampler2 = Sampler()\n"
]
}
@@ -110,10 +110,10 @@
"id": "ceramic-florida",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:56.450792Z",
- "iopub.status.busy": "2024-11-15T18:54:56.450346Z",
- "iopub.status.idle": "2024-11-15T18:54:56.453996Z",
- "shell.execute_reply": "2024-11-15T18:54:56.453472Z"
+ "iopub.execute_input": "2024-11-18T17:06:10.050761Z",
+ "iopub.status.busy": "2024-11-18T17:06:10.050412Z",
+ "iopub.status.idle": "2024-11-18T17:06:10.054121Z",
+ "shell.execute_reply": "2024-11-18T17:06:10.053483Z"
}
},
"outputs": [],
@@ -138,10 +138,10 @@
"id": "dirty-director",
"metadata": {
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- "iopub.execute_input": "2024-11-15T18:54:56.455768Z",
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- "iopub.status.idle": "2024-11-15T18:54:56.462253Z",
- "shell.execute_reply": "2024-11-15T18:54:56.461612Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.062081Z"
}
},
"outputs": [
@@ -175,10 +175,10 @@
"id": "thorough-script",
"metadata": {
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- "iopub.status.idle": "2024-11-15T18:54:56.468270Z",
- "shell.execute_reply": "2024-11-15T18:54:56.467649Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.068029Z"
}
},
"outputs": [
@@ -215,10 +215,10 @@
"id": "understood-ukraine",
"metadata": {
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- "iopub.status.idle": "2024-11-15T18:54:56.476075Z",
- "shell.execute_reply": "2024-11-15T18:54:56.475554Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.074533Z"
}
},
"outputs": [
@@ -252,10 +252,10 @@
"id": "german-agreement",
"metadata": {
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- "iopub.status.idle": "2024-11-15T18:54:56.481793Z",
- "shell.execute_reply": "2024-11-15T18:54:56.481172Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.080173Z"
}
},
"outputs": [
@@ -292,10 +292,10 @@
"id": "about-ordinary",
"metadata": {
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+ "shell.execute_reply": "2024-11-18T17:06:10.086655Z"
}
},
"outputs": [
@@ -331,10 +331,10 @@
"id": "fifty-scottish",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:54:56.642767Z",
- "shell.execute_reply": "2024-11-15T18:54:56.642167Z"
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}
},
"outputs": [
@@ -422,10 +422,10 @@
"id": "brief-lending",
"metadata": {
"execution": {
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+ "shell.execute_reply": "2024-11-18T17:06:10.245991Z"
}
},
"outputs": [],
@@ -447,10 +447,10 @@
"id": "integrated-palestinian",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:54:56.651790Z",
- "shell.execute_reply": "2024-11-15T18:54:56.651283Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.250423Z"
}
},
"outputs": [],
@@ -472,10 +472,10 @@
"id": "periodic-apparel",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:54:56.658064Z",
- "shell.execute_reply": "2024-11-15T18:54:56.657523Z"
+ "iopub.execute_input": "2024-11-18T17:06:10.252868Z",
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+ "shell.execute_reply": "2024-11-18T17:06:10.256722Z"
}
},
"outputs": [],
@@ -521,10 +521,10 @@
"id": "electronic-impact",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:54:56.665390Z",
- "shell.execute_reply": "2024-11-15T18:54:56.664865Z"
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+ "shell.execute_reply": "2024-11-18T17:06:10.263675Z"
}
},
"outputs": [],
@@ -549,10 +549,10 @@
"id": "revolutionary-freeze",
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-11-15T18:54:56.674874Z",
- "shell.execute_reply": "2024-11-15T18:54:56.674318Z"
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+ "iopub.status.busy": "2024-11-18T17:06:10.265952Z",
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+ "shell.execute_reply": "2024-11-18T17:06:10.273039Z"
}
},
"outputs": [],
@@ -576,10 +576,10 @@
"id": "suited-appointment",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:54:56.676863Z",
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- "iopub.status.idle": "2024-11-15T18:55:00.399923Z",
- "shell.execute_reply": "2024-11-15T18:55:00.399358Z"
+ "iopub.execute_input": "2024-11-18T17:06:10.275623Z",
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+ "iopub.status.idle": "2024-11-18T17:06:13.960790Z",
+ "shell.execute_reply": "2024-11-18T17:06:13.960234Z"
}
},
"outputs": [
@@ -596,7 +596,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 15,
@@ -622,10 +622,10 @@
"id": "greek-memphis",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:00.402062Z",
- "iopub.status.busy": "2024-11-15T18:55:00.401830Z",
- "iopub.status.idle": "2024-11-15T18:55:00.508241Z",
- "shell.execute_reply": "2024-11-15T18:55:00.507542Z"
+ "iopub.execute_input": "2024-11-18T17:06:13.963105Z",
+ "iopub.status.busy": "2024-11-18T17:06:13.962630Z",
+ "iopub.status.idle": "2024-11-18T17:06:14.052405Z",
+ "shell.execute_reply": "2024-11-18T17:06:14.051770Z"
}
},
"outputs": [
@@ -657,10 +657,10 @@
"id": "broadband-interview",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:00.510539Z",
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- "shell.execute_reply": "2024-11-15T18:55:00.534502Z"
+ "iopub.execute_input": "2024-11-18T17:06:14.054491Z",
+ "iopub.status.busy": "2024-11-18T17:06:14.054131Z",
+ "iopub.status.idle": "2024-11-18T17:06:14.078409Z",
+ "shell.execute_reply": "2024-11-18T17:06:14.077763Z"
}
},
"outputs": [],
@@ -684,10 +684,10 @@
"id": "steady-europe",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:00.536828Z",
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- "iopub.status.idle": "2024-11-15T18:55:00.541297Z",
- "shell.execute_reply": "2024-11-15T18:55:00.540768Z"
+ "iopub.execute_input": "2024-11-18T17:06:14.080409Z",
+ "iopub.status.busy": "2024-11-18T17:06:14.080069Z",
+ "iopub.status.idle": "2024-11-18T17:06:14.084538Z",
+ "shell.execute_reply": "2024-11-18T17:06:14.083919Z"
}
},
"outputs": [],
@@ -709,10 +709,10 @@
"id": "accessible-cowboy",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:00.543009Z",
- "iopub.status.busy": "2024-11-15T18:55:00.542808Z",
- "iopub.status.idle": "2024-11-15T18:55:00.545809Z",
- "shell.execute_reply": "2024-11-15T18:55:00.545287Z"
+ "iopub.execute_input": "2024-11-18T17:06:14.086587Z",
+ "iopub.status.busy": "2024-11-18T17:06:14.086248Z",
+ "iopub.status.idle": "2024-11-18T17:06:14.089316Z",
+ "shell.execute_reply": "2024-11-18T17:06:14.088788Z"
}
},
"outputs": [],
@@ -736,10 +736,10 @@
"id": "metric-cyprus",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:00.547699Z",
- "iopub.status.busy": "2024-11-15T18:55:00.547497Z",
- "iopub.status.idle": "2024-11-15T18:55:14.994683Z",
- "shell.execute_reply": "2024-11-15T18:55:14.994112Z"
+ "iopub.execute_input": "2024-11-18T17:06:14.091211Z",
+ "iopub.status.busy": "2024-11-18T17:06:14.090835Z",
+ "iopub.status.idle": "2024-11-18T17:06:28.373337Z",
+ "shell.execute_reply": "2024-11-18T17:06:28.372654Z"
},
"nbsphinx-thumbnail": {
"output-index": 0
@@ -759,7 +759,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 20,
@@ -777,10 +777,10 @@
"id": "bronze-spread",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:14.996966Z",
- "iopub.status.busy": "2024-11-15T18:55:14.996481Z",
- "iopub.status.idle": "2024-11-15T18:55:15.085031Z",
- "shell.execute_reply": "2024-11-15T18:55:15.084373Z"
+ "iopub.execute_input": "2024-11-18T17:06:28.375696Z",
+ "iopub.status.busy": "2024-11-18T17:06:28.375318Z",
+ "iopub.status.idle": "2024-11-18T17:06:28.464101Z",
+ "shell.execute_reply": "2024-11-18T17:06:28.463408Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "catholic-norway",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:15.087337Z",
- "iopub.status.busy": "2024-11-15T18:55:15.086833Z",
- "iopub.status.idle": "2024-11-15T18:55:15.173025Z",
- "shell.execute_reply": "2024-11-15T18:55:15.172350Z"
+ "iopub.execute_input": "2024-11-18T17:06:28.466494Z",
+ "iopub.status.busy": "2024-11-18T17:06:28.466005Z",
+ "iopub.status.idle": "2024-11-18T17:06:28.576569Z",
+ "shell.execute_reply": "2024-11-18T17:06:28.575973Z"
}
},
"outputs": [],
@@ -838,17 +838,17 @@
"id": "tested-handling",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:15.175041Z",
- "iopub.status.busy": "2024-11-15T18:55:15.174662Z",
- "iopub.status.idle": "2024-11-15T18:55:15.362793Z",
- "shell.execute_reply": "2024-11-15T18:55:15.362094Z"
+ "iopub.execute_input": "2024-11-18T17:06:28.578617Z",
+ "iopub.status.busy": "2024-11-18T17:06:28.578268Z",
+ "iopub.status.idle": "2024-11-18T17:06:28.763599Z",
+ "shell.execute_reply": "2024-11-18T17:06:28.763053Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 23,
@@ -931,17 +931,17 @@
"id": "persistent-combine",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:15.365148Z",
- "iopub.status.busy": "2024-11-15T18:55:15.364724Z",
- "iopub.status.idle": "2024-11-15T18:55:15.372295Z",
- "shell.execute_reply": "2024-11-15T18:55:15.371738Z"
+ "iopub.execute_input": "2024-11-18T17:06:28.765626Z",
+ "iopub.status.busy": "2024-11-18T17:06:28.765414Z",
+ "iopub.status.idle": "2024-11-18T17:06:28.772911Z",
+ "shell.execute_reply": "2024-11-18T17:06:28.772367Z"
}
},
"outputs": [
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:55:15 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:28 2024 UTC
"
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diff --git a/tutorials/10_effective_dimension.html b/tutorials/10_effective_dimension.html
index fdf74d66e..420bab6b7 100644
--- a/tutorials/10_effective_dimension.html
+++ b/tutorials/10_effective_dimension.html
@@ -507,7 +507,7 @@ 3.1 Define QNN
-/tmp/ipykernel_12466/3558882843.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_12483/3558882843.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = SamplerQNN(
@@ -678,9 +678,7 @@ 4.1 Define Dataset and QNN
-/tmp/ipykernel_12466/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- estimator_qnn = EstimatorQNN(circuit=qc)
-/tmp/ipykernel_12466/1658004975.py:1: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (3). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_12483/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
estimator_qnn = EstimatorQNN(circuit=qc)
@@ -759,7 +757,7 @@ 4.2 Train QNN
-1.0
+0.8
@@ -791,8 +789,8 @@ 4.3 Compute Local Effective Dimension of trained QNN
-normalized local effective dimensions for trained QNN: [0.28205569 0.28769742 0.29113848 0.32033712 0.33066994 0.34456456
- 0.35628096 0.36497362 0.39479354 0.41931942]
+normalized local effective dimensions for trained QNN: [0.5017351 0.5084982 0.51229844 0.54411451 0.55572881 0.57139453
+ 0.58440001 0.5938124 0.62396281 0.64605484]
@@ -822,8 +820,8 @@ 4.4 Compute Local Effective Dimension of untrained QNN
-normalized local effective dimensions for untrained QNN: [0.65798382 0.66798395 0.67326579 0.71092265 0.72253992 0.73691291
- 0.74790328 0.75540639 0.77742264 0.79211656]
+normalized local effective dimensions for untrained QNN: [0.75123892 0.7624206 0.76783704 0.80108286 0.81017262 0.82096662
+ 0.82896467 0.83432645 0.84972592 0.85981575]
@@ -873,7 +871,7 @@ 4.5 Plot and analyze results
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:55:38 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:52 2024 UTC
diff --git a/tutorials/10_effective_dimension.ipynb b/tutorials/10_effective_dimension.ipynb
index 944430751..9cfe29c34 100644
--- a/tutorials/10_effective_dimension.ipynb
+++ b/tutorials/10_effective_dimension.ipynb
@@ -62,10 +62,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:17.991040Z",
- "iopub.status.busy": "2024-11-15T18:55:17.990839Z",
- "iopub.status.idle": "2024-11-15T18:55:19.463521Z",
- "shell.execute_reply": "2024-11-15T18:55:19.462793Z"
+ "iopub.execute_input": "2024-11-18T17:06:31.578708Z",
+ "iopub.status.busy": "2024-11-18T17:06:31.578233Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.052356Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.051620Z"
},
"slideshow": {
"slide_type": "skip"
@@ -114,10 +114,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.466233Z",
- "iopub.status.busy": "2024-11-15T18:55:19.465726Z",
- "iopub.status.idle": "2024-11-15T18:55:19.934447Z",
- "shell.execute_reply": "2024-11-15T18:55:19.933759Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.055081Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.054576Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.528804Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.528189Z"
}
},
"outputs": [
@@ -155,10 +155,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.936921Z",
- "iopub.status.busy": "2024-11-15T18:55:19.936325Z",
- "iopub.status.idle": "2024-11-15T18:55:19.939825Z",
- "shell.execute_reply": "2024-11-15T18:55:19.939160Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.531197Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.530737Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.533978Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.533447Z"
},
"pycharm": {
"name": "#%%\n"
@@ -179,10 +179,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.941813Z",
- "iopub.status.busy": "2024-11-15T18:55:19.941312Z",
- "iopub.status.idle": "2024-11-15T18:55:19.946162Z",
- "shell.execute_reply": "2024-11-15T18:55:19.945536Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.535975Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.535587Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.540079Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.539546Z"
},
"pycharm": {
"name": "#%%\n"
@@ -193,7 +193,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_12466/3558882843.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_12483/3558882843.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = SamplerQNN(\n"
]
}
@@ -229,10 +229,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.948226Z",
- "iopub.status.busy": "2024-11-15T18:55:19.947778Z",
- "iopub.status.idle": "2024-11-15T18:55:19.951422Z",
- "shell.execute_reply": "2024-11-15T18:55:19.950758Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.542198Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.541798Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.545127Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.544619Z"
},
"pycharm": {
"name": "#%%\n"
@@ -261,10 +261,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.953512Z",
- "iopub.status.busy": "2024-11-15T18:55:19.953061Z",
- "iopub.status.idle": "2024-11-15T18:55:19.956738Z",
- "shell.execute_reply": "2024-11-15T18:55:19.956223Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.547040Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.546658Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.550407Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.549872Z"
},
"pycharm": {
"name": "#%%\n"
@@ -291,10 +291,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.958462Z",
- "iopub.status.busy": "2024-11-15T18:55:19.958265Z",
- "iopub.status.idle": "2024-11-15T18:55:19.961087Z",
- "shell.execute_reply": "2024-11-15T18:55:19.960574Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.552321Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.551940Z",
+ "iopub.status.idle": "2024-11-18T17:06:33.555005Z",
+ "shell.execute_reply": "2024-11-18T17:06:33.554466Z"
},
"pycharm": {
"name": "#%%\n"
@@ -319,10 +319,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:19.963024Z",
- "iopub.status.busy": "2024-11-15T18:55:19.962661Z",
- "iopub.status.idle": "2024-11-15T18:55:21.302433Z",
- "shell.execute_reply": "2024-11-15T18:55:21.301712Z"
+ "iopub.execute_input": "2024-11-18T17:06:33.557076Z",
+ "iopub.status.busy": "2024-11-18T17:06:33.556703Z",
+ "iopub.status.idle": "2024-11-18T17:06:34.904185Z",
+ "shell.execute_reply": "2024-11-18T17:06:34.903577Z"
},
"pycharm": {
"name": "#%%\n"
@@ -345,10 +345,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:21.305103Z",
- "iopub.status.busy": "2024-11-15T18:55:21.304755Z",
- "iopub.status.idle": "2024-11-15T18:55:21.309066Z",
- "shell.execute_reply": "2024-11-15T18:55:21.308521Z"
+ "iopub.execute_input": "2024-11-18T17:06:34.906713Z",
+ "iopub.status.busy": "2024-11-18T17:06:34.906220Z",
+ "iopub.status.idle": "2024-11-18T17:06:34.910054Z",
+ "shell.execute_reply": "2024-11-18T17:06:34.909463Z"
},
"pycharm": {
"name": "#%%\n"
@@ -385,10 +385,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:21.311059Z",
- "iopub.status.busy": "2024-11-15T18:55:21.310679Z",
- "iopub.status.idle": "2024-11-15T18:55:22.644709Z",
- "shell.execute_reply": "2024-11-15T18:55:22.643990Z"
+ "iopub.execute_input": "2024-11-18T17:06:34.912038Z",
+ "iopub.status.busy": "2024-11-18T17:06:34.911651Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.237127Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.236536Z"
},
"pycharm": {
"name": "#%%\n"
@@ -407,10 +407,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.647228Z",
- "iopub.status.busy": "2024-11-15T18:55:22.646805Z",
- "iopub.status.idle": "2024-11-15T18:55:22.650626Z",
- "shell.execute_reply": "2024-11-15T18:55:22.650016Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.239505Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.239116Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.242927Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.242298Z"
},
"pycharm": {
"name": "#%%\n"
@@ -437,10 +437,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.652685Z",
- "iopub.status.busy": "2024-11-15T18:55:22.652295Z",
- "iopub.status.idle": "2024-11-15T18:55:22.732727Z",
- "shell.execute_reply": "2024-11-15T18:55:22.732112Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.244951Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.244497Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.324629Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.323990Z"
},
"pycharm": {
"name": "#%%\n"
@@ -497,10 +497,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.734698Z",
- "iopub.status.busy": "2024-11-15T18:55:22.734481Z",
- "iopub.status.idle": "2024-11-15T18:55:22.739479Z",
- "shell.execute_reply": "2024-11-15T18:55:22.738919Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.326841Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.326474Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.331422Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.330883Z"
},
"pycharm": {
"name": "#%%\n"
@@ -535,10 +535,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.741510Z",
- "iopub.status.busy": "2024-11-15T18:55:22.741124Z",
- "iopub.status.idle": "2024-11-15T18:55:22.744954Z",
- "shell.execute_reply": "2024-11-15T18:55:22.744390Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.333211Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.333015Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.336786Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.336235Z"
},
"pycharm": {
"name": "#%%\n"
@@ -549,9 +549,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_12466/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " estimator_qnn = EstimatorQNN(circuit=qc)\n",
- "/tmp/ipykernel_12466/1658004975.py:1: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (3). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_12483/1658004975.py:1: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" estimator_qnn = EstimatorQNN(circuit=qc)\n"
]
}
@@ -574,10 +572,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.746710Z",
- "iopub.status.busy": "2024-11-15T18:55:22.746528Z",
- "iopub.status.idle": "2024-11-15T18:55:22.750169Z",
- "shell.execute_reply": "2024-11-15T18:55:22.749619Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.338725Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.338344Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.342140Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.341456Z"
},
"pycharm": {
"name": "#%%\n"
@@ -601,10 +599,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.752124Z",
- "iopub.status.busy": "2024-11-15T18:55:22.751744Z",
- "iopub.status.idle": "2024-11-15T18:55:22.755215Z",
- "shell.execute_reply": "2024-11-15T18:55:22.754677Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.344023Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.343638Z",
+ "iopub.status.idle": "2024-11-18T17:06:36.347197Z",
+ "shell.execute_reply": "2024-11-18T17:06:36.346570Z"
},
"pycharm": {
"name": "#%%\n"
@@ -628,10 +626,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:22.757126Z",
- "iopub.status.busy": "2024-11-15T18:55:22.756749Z",
- "iopub.status.idle": "2024-11-15T18:55:37.443188Z",
- "shell.execute_reply": "2024-11-15T18:55:37.442612Z"
+ "iopub.execute_input": "2024-11-18T17:06:36.349169Z",
+ "iopub.status.busy": "2024-11-18T17:06:36.348830Z",
+ "iopub.status.idle": "2024-11-18T17:06:50.989847Z",
+ "shell.execute_reply": "2024-11-18T17:06:50.989296Z"
},
"pycharm": {
"name": "#%%\n"
@@ -640,7 +638,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -673,17 +671,17 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:37.445313Z",
- "iopub.status.busy": "2024-11-15T18:55:37.445085Z",
- "iopub.status.idle": "2024-11-15T18:55:37.513941Z",
- "shell.execute_reply": "2024-11-15T18:55:37.513366Z"
+ "iopub.execute_input": "2024-11-18T17:06:50.991997Z",
+ "iopub.status.busy": "2024-11-18T17:06:50.991595Z",
+ "iopub.status.idle": "2024-11-18T17:06:51.210876Z",
+ "shell.execute_reply": "2024-11-18T17:06:51.210232Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
- "1.0"
+ "0.8"
]
},
"execution_count": 18,
@@ -710,10 +708,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:37.516157Z",
- "iopub.status.busy": "2024-11-15T18:55:37.515743Z",
- "iopub.status.idle": "2024-11-15T18:55:38.156476Z",
- "shell.execute_reply": "2024-11-15T18:55:38.155824Z"
+ "iopub.execute_input": "2024-11-18T17:06:51.213251Z",
+ "iopub.status.busy": "2024-11-18T17:06:51.212886Z",
+ "iopub.status.idle": "2024-11-18T17:06:51.827005Z",
+ "shell.execute_reply": "2024-11-18T17:06:51.826375Z"
},
"pycharm": {
"name": "#%%\n"
@@ -724,8 +722,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "normalized local effective dimensions for trained QNN: [0.28205569 0.28769742 0.29113848 0.32033712 0.33066994 0.34456456\n",
- " 0.35628096 0.36497362 0.39479354 0.41931942]\n"
+ "normalized local effective dimensions for trained QNN: [0.5017351 0.5084982 0.51229844 0.54411451 0.55572881 0.57139453\n",
+ " 0.58440001 0.5938124 0.62396281 0.64605484]\n"
]
}
],
@@ -759,10 +757,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:38.158853Z",
- "iopub.status.busy": "2024-11-15T18:55:38.158436Z",
- "iopub.status.idle": "2024-11-15T18:55:38.768726Z",
- "shell.execute_reply": "2024-11-15T18:55:38.768059Z"
+ "iopub.execute_input": "2024-11-18T17:06:51.829193Z",
+ "iopub.status.busy": "2024-11-18T17:06:51.828806Z",
+ "iopub.status.idle": "2024-11-18T17:06:52.467000Z",
+ "shell.execute_reply": "2024-11-18T17:06:52.466411Z"
}
},
"outputs": [
@@ -770,8 +768,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "normalized local effective dimensions for untrained QNN: [0.65798382 0.66798395 0.67326579 0.71092265 0.72253992 0.73691291\n",
- " 0.74790328 0.75540639 0.77742264 0.79211656]\n"
+ "normalized local effective dimensions for untrained QNN: [0.75123892 0.7624206 0.76783704 0.80108286 0.81017262 0.82096662\n",
+ " 0.82896467 0.83432645 0.84972592 0.85981575]\n"
]
}
],
@@ -807,10 +805,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-11-15T18:55:38.770859Z",
- "iopub.status.busy": "2024-11-15T18:55:38.770488Z",
- "iopub.status.idle": "2024-11-15T18:55:38.862619Z",
- "shell.execute_reply": "2024-11-15T18:55:38.862085Z"
+ "iopub.execute_input": "2024-11-18T17:06:52.469249Z",
+ "iopub.status.busy": "2024-11-18T17:06:52.468827Z",
+ "iopub.status.idle": "2024-11-18T17:06:52.569474Z",
+ "shell.execute_reply": "2024-11-18T17:06:52.568811Z"
},
"pycharm": {
"name": "#%%\n"
@@ -822,7 +820,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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@@ -873,7 +871,7 @@
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+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:06:52 2024 UTC
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diff --git a/tutorials/11_quantum_convolutional_neural_networks.html b/tutorials/11_quantum_convolutional_neural_networks.html
index 2cdb98509..7dae8d61d 100644
--- a/tutorials/11_quantum_convolutional_neural_networks.html
+++ b/tutorials/11_quantum_convolutional_neural_networks.html
@@ -802,9 +802,7 @@ 5. Training our QCNN
-/tmp/ipykernel_12862/224527722.py:31: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
- qnn = EstimatorQNN(
-/tmp/ipykernel_12862/224527722.py:31: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (8). If `circuit` is transpiled, this may cause unstable behaviour.
+/tmp/ipykernel_12879/224527722.py:31: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = EstimatorQNN(
@@ -955,7 +953,7 @@ 7. References
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:57:31 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:08:43 2024 UTC
diff --git a/tutorials/11_quantum_convolutional_neural_networks.ipynb b/tutorials/11_quantum_convolutional_neural_networks.ipynb
index 3770be164..c5db2250b 100644
--- a/tutorials/11_quantum_convolutional_neural_networks.ipynb
+++ b/tutorials/11_quantum_convolutional_neural_networks.ipynb
@@ -40,10 +40,10 @@
"id": "3ceca583",
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"execution": {
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@@ -246,10 +246,10 @@
"id": "809524ce",
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@@ -305,10 +305,10 @@
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@@ -387,10 +387,10 @@
"id": "3c742cc9",
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- "shell.execute_reply": "2024-11-15T18:55:43.972212Z"
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@@ -441,10 +441,10 @@
"id": "8b37f922",
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"execution": {
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@@ -525,10 +525,10 @@
"id": "3a674ebf",
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"scrolled": false
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@@ -585,10 +585,10 @@
"id": "ed1828c5",
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- "iopub.status.idle": "2024-11-15T18:55:44.204324Z",
- "shell.execute_reply": "2024-11-15T18:55:44.203829Z"
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@@ -615,10 +615,10 @@
"id": "0afeaa5f",
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@@ -680,10 +680,10 @@
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@@ -760,10 +760,10 @@
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@@ -772,9 +772,7 @@
"name": "stderr",
"output_type": "stream",
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- "/tmp/ipykernel_12862/224527722.py:31: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
- " qnn = EstimatorQNN(\n",
- "/tmp/ipykernel_12862/224527722.py:31: UserWarning: No number of qubits was not specified (None) and was retrieved from `circuit` (8). If `circuit` is transpiled, this may cause unstable behaviour.\n",
+ "/tmp/ipykernel_12879/224527722.py:31: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = EstimatorQNN(\n"
]
}
@@ -824,10 +822,10 @@
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@@ -1094,7 +1092,7 @@
{
"data": {
"text/html": [
- "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:57:31 2024 UTC
"
+ "Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:08:43 2024 UTC
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diff --git a/tutorials/12_quantum_autoencoder.html b/tutorials/12_quantum_autoencoder.html
index 05fb602aa..ae9e745d0 100644
--- a/tutorials/12_quantum_autoencoder.html
+++ b/tutorials/12_quantum_autoencoder.html
@@ -663,7 +663,7 @@ 6. A Simple Example: The Domain Wall Autoencoder
-/tmp/ipykernel_13544/1035603420.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_13632/1035603420.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = SamplerQNN(
@@ -722,7 +722,7 @@ 6. A Simple Example: The Domain Wall Autoencoder
-Fit in 18.86 seconds
+Fit in 18.59 seconds
Looks like it has converged! After training our Quantum Autoencoder, let’s build it and see how well it compresses the state!
@@ -936,7 +936,7 @@ 7. A Quantum Autoencoder for Digital Compression
-/tmp/ipykernel_13544/1099023668.py:5: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_13632/1099023668.py:5: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qnn = SamplerQNN(
@@ -1002,7 +1002,7 @@ 7. A Quantum Autoencoder for Digital Compression
-Fit in 18.82 seconds
+Fit in 18.58 seconds
Looks like it has converged!
@@ -1090,7 +1090,7 @@ 9. References
-Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Fri Nov 15 18:58:16 2024 UTC
+Version Information
Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:09:27 2024 UTC
diff --git a/tutorials/12_quantum_autoencoder.ipynb b/tutorials/12_quantum_autoencoder.ipynb
index 0efaa296d..707e380dd 100644
--- a/tutorials/12_quantum_autoencoder.ipynb
+++ b/tutorials/12_quantum_autoencoder.ipynb
@@ -258,10 +258,10 @@
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@@ -306,10 +306,10 @@
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@@ -332,10 +332,10 @@
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@@ -406,10 +406,10 @@
"id": "1d415550",
"metadata": {
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@@ -481,10 +481,10 @@
"id": "2787d73c",
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}
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@@ -534,10 +534,10 @@
"id": "602efbb0",
"metadata": {
"execution": {
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}
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@@ -575,10 +575,10 @@
"id": "varying-township",
"metadata": {
"execution": {
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}
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@@ -586,7 +586,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_13544/1035603420.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_13632/1035603420.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = SamplerQNN(\n"
]
}
@@ -622,10 +622,10 @@
"id": "28abf03b",
"metadata": {
"execution": {
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}
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"outputs": [],
@@ -660,10 +660,10 @@
"id": "71344086",
"metadata": {
"execution": {
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}
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"outputs": [
@@ -681,7 +681,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fit in 18.86 seconds\n"
+ "Fit in 18.59 seconds\n"
]
}
],
@@ -726,10 +726,10 @@
"id": "749338a0",
"metadata": {
"execution": {
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}
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"outputs": [
@@ -773,10 +773,10 @@
"id": "shaped-marina",
"metadata": {
"execution": {
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@@ -798,10 +798,10 @@
"id": "756cfa05",
"metadata": {
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@@ -857,10 +857,10 @@
"id": "41d40622",
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@@ -974,10 +974,10 @@
"id": "a11ec8f3",
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@@ -1022,10 +1022,10 @@
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@@ -1033,7 +1033,7 @@
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+ "/tmp/ipykernel_13632/1099023668.py:5: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qnn = SamplerQNN(\n"
]
}
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@@ -1151,7 +1151,7 @@
"name": "stdout",
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- "Fit in 18.82 seconds\n"
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"text/html": [
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Software Version qiskit1.2.4 qiskit_machine_learning0.8.0 System information Python version 3.10.15 OS Linux Mon Nov 18 17:09:27 2024 UTC
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diff --git a/tutorials/13_quantum_bayesian_inference.html b/tutorials/13_quantum_bayesian_inference.html
index 02183607d..553213649 100644
--- a/tutorials/13_quantum_bayesian_inference.html
+++ b/tutorials/13_quantum_bayesian_inference.html
@@ -633,7 +633,7 @@ 3.1.1 Two Node Bayesian Network Example
-/tmp/ipykernel_14402/2947965752.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_14551/2947965752.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qb_2n = QBayesian(circuit=qc_2n)
@@ -705,7 +705,7 @@ 3.1.2. Burglary Alarm Example
-/tmp/ipykernel_14402/2141156116.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
+/tmp/ipykernel_14551/2141156116.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.
qb_ba = QBayesian(circuit=qc_ba)
diff --git a/tutorials/13_quantum_bayesian_inference.ipynb b/tutorials/13_quantum_bayesian_inference.ipynb
index 96d89ee89..5bb744f2a 100644
--- a/tutorials/13_quantum_bayesian_inference.ipynb
+++ b/tutorials/13_quantum_bayesian_inference.ipynb
@@ -136,10 +136,10 @@
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@@ -428,10 +428,10 @@
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@@ -439,7 +439,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_14402/2947965752.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_14551/2947965752.py:6: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qb_2n = QBayesian(circuit=qc_2n)\n"
]
},
@@ -488,10 +488,10 @@
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"name": "stderr",
"output_type": "stream",
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- "/tmp/ipykernel_14402/2141156116.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
+ "/tmp/ipykernel_14551/2141156116.py:2: DeprecationWarning: V1 Primitives are deprecated as of qiskit-machine-learning 0.8.0 and will be removed no sooner than 4 months after the release date. Use V2 primitives for continued compatibility and support.\n",
" qb_ba = QBayesian(circuit=qc_ba)\n"
]
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