diff --git a/Makefile b/Makefile
index 7d6c018..ec09d79 100644
--- a/Makefile
+++ b/Makefile
@@ -20,13 +20,15 @@ init: venv-setup
$(PIP) install -e .[core]
# Setup development environment
-# includes a temporary fix for pytket-qiskit (https://github.com/CQCL/tket/issues/1023)
+# NOTE: Instead of pip installing 'core' dependencies directly from git repositories,
+# the relevant repositories are cloned into a sibling directory and installed in editable mode.
dev-init: venv-setup install-dev-deps pre-commit-setup transpile_benchy monodromy
$(PIP) install qiskit==0.43.3
$(PIP) install git+https://github.com/evmckinney9/qiskit-evmckinney9.git@sqisw-gate
+# force using my fork which includes the incomplete sqiswap decomposition PR
install-dev-deps:
- $(PIP) install -e .[dev] --quiet
+ $(PIP) install -e .[dev]
pre-commit-setup:
@$(PRE_COMMIT) install
@@ -53,6 +55,7 @@ monodromy:
cd monodromy; \
fi
$(PIP) install -e ../monodromy --quiet
+
clean: movefigs
@find ./ -type f -name '*.pyc' -exec rm -f {} \; 2>/dev/null || true
@find ./ -type d -name '__pycache__' -exec rm -rf {} \; 2>/dev/null || true
diff --git a/README.md b/README.md
index c3704ca..49174c0 100644
--- a/README.md
+++ b/README.md
@@ -81,8 +81,15 @@ mirage = Mirage(
mirage_qc = mirage.run(circuit=qc)
```
-> [!WARNING]
-> Neither method currently includes an optimized [decomposition pass](https://github.com/Qiskit/qiskit-terra/pull/9375). Previously I've [implemented the logic](https://github.com/Pitt-JonesLab/slam_decomposition/blob/main/src/slam/utils/transpiler_pass/weyl_decompose.py), but this PR suggests there were some bugs in the referenced paper- so I'll wait until that gets merged. When including "xx_plus_yy", you'll see some gates are decomposed into 4 basis gates due to limitations of the built-in decomposition method, but using the more updated decomposer (or looking up circuit-depth with monodromy) will see this won't ever exceed $k=3$.
+[!WARNING]
+[!WARNING]
+In the current version of Qiskit, there's no direct support for \( \sqrt{iSWAP} \) as a basis gate. As a workaround, I've been using `XX+YY`, which provides a partial solution but isn't fully optimized.
+
+However, there's an ongoing [pull request](https://github.com/Qiskit/qiskit-terra/pull/9375) in Qiskit that introduces a new gate, `SiSwapGate`, which represents \( \sqrt{iSWAP} \). This PR also brings in optimized decomposition methods for the gate. I've previously [implemented a similar logic](https://github.com/Pitt-JonesLab/slam_decomposition/blob/main/src/slam/utils/transpiler_pass/weyl_decompose.py), but the PR suggests there might have been some inaccuracies in the paper I referenced.
+
+To benefit from the advancements in the PR, I'm temporarily using a [fork of the PR](https://github.com/evmckinney9/qiskit-evmckinney9/tree/sqisw-gate) in this project. By leveraging the fork, when you use the `SiSwapGate`, you'll notice a more efficient decomposition compared to the `XX+YY` workaround.
+
+Please note that this is a provisional solution. I'll transition back to the main Qiskit repository once the PR is merged and the `SiSwapGate` with its decomposition methods becomes officially available.
### 📋 Prerequisites
diff --git a/expected_duration.png b/expected_duration.png
new file mode 100644
index 0000000..e792c67
Binary files /dev/null and b/expected_duration.png differ
diff --git a/pyproject.toml b/pyproject.toml
index b59fac4..94f3fb4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,12 +19,13 @@ dependencies = [
"ipykernel",
"qiskit==0.43.3",
"qiskit-terra @ git+https://github.com/evmckinney9/qiskit-evmckinney9.git@sqisw-gate",
+ "networkx",
]
[project.optional-dependencies]
core = [
"monodromy @ git+https://github.com/evmckinney9/monodromy.git",
- "transpile_benchy @ git+https://github.com/evmckinney9/transpile_benchy.git",
+ "transpile_benchy @ git+https://github.com/evmckinney9/transpile_benchy.git@no-mqt",
]
dev = [
"pre-commit",
diff --git a/src/circuits/small_circuits.txt b/src/circuits/small_circuits.txt
index 7ab5051..bc88043 100644
--- a/src/circuits/small_circuits.txt
+++ b/src/circuits/small_circuits.txt
@@ -1,9 +1,6 @@
toffoli_n3
-adder_n4
fredkin_n3
-qaoa_8
-dj_8
-qft_8
-qftentangled_8
-qpeexact_8
-ae_8
+dj_n8
+qft_n4
+qftentangled_n8
+ae_n8
diff --git a/src/mirror_gates/noisy_fidelity.py b/src/mirror_gates/noisy_fidelity.py
new file mode 100644
index 0000000..b96447d
--- /dev/null
+++ b/src/mirror_gates/noisy_fidelity.py
@@ -0,0 +1,216 @@
+"""Noisy fidelity of a circuit."""
+import numpy as np
+from qiskit import transpile
+from qiskit.circuit import Delay
+from qiskit.converters import circuit_to_dag
+from qiskit.quantum_info import state_fidelity
+from qiskit.transpiler import PassManager
+from qiskit.transpiler.passes import ASAPSchedule, Optimize1qGatesDecomposition
+from qiskit_aer import AerSimulator, QasmSimulator
+
+# Import from Qiskit Aer noise module
+from qiskit_aer.noise import (
+ NoiseModel,
+ RelaxationNoisePass,
+ depolarizing_error,
+ thermal_relaxation_error,
+)
+
+from mirror_gates.logging import transpile_benchy_logger
+from mirror_gates.sqiswap_decomposer import SiSwapDecomposePass
+
+# 80 microsec (in nanoseconds)
+T1 = 80e3
+# 80 microsec
+T2 = 80e3
+
+# Instruction times (in nanoseconds)
+time_u3 = 25
+time_cx = 100
+time_siswap = int(time_cx / 2.0)
+# divide by 2 again since
+# each sqrt(iSwap) is compiled to an RXX and RYY
+time_rxx = int(time_siswap / 2.0)
+
+p1 = 0.0
+p2 = 0.00658
+
+
+class NoiseModelBuilder:
+ """A class to help build a custom NoiseModel from scratch.
+
+ Many of the functions are based on examples from
+ https://github.com/Qiskit/qiskit-presentations/blob/master/2019-02-26_QiskitCamp/QiskitCamp_Simulation.ipynb
+ """
+
+ def __init__(self, basis_gates, coupling_map=None):
+ """Initialize a NoiseModelBuilder."""
+ self.noise_model = NoiseModel(basis_gates=basis_gates)
+ self.coupling_map = coupling_map
+
+ def construct_basic_device_model(self, p_depol1, p_depol2, t1, t2):
+ """Emulate qiskit.providers.aer.noise.device.models.basic_device_noise_model().
+
+ The noise model includes the following errors:
+
+ * Single qubit readout errors on measurements.
+ * Single-qubit gate errors consisting of a depolarizing error
+ followed by a thermal relaxation error for the qubit the gate
+ acts on.
+ * Two-qubit gate errors consisting of a 2-qubit depolarizing
+ error followed by single qubit thermal relaxation errors for
+ all qubits participating in the gate.
+
+ :param p_depol1: Probability of a depolarising error on single qubit gates
+ :param p_depol2: Probability of a depolarising error on two qubit gates
+ :param t1: Thermal relaxation time constant
+ :param t2: Dephasing time constant
+ """
+ if t2 > 2 * t1:
+ raise ValueError("t2 cannot be greater than 2t1")
+
+ # Thermal relaxation error
+
+ # QuantumError objects
+ error_thermal_u3 = thermal_relaxation_error(t1, t2, time_u3)
+ error_thermal_cx = thermal_relaxation_error(t1, t2, time_cx).expand(
+ thermal_relaxation_error(t1, t2, time_cx)
+ )
+ error_thermal_rxx = thermal_relaxation_error(t1, t2, time_rxx).expand(
+ thermal_relaxation_error(t1, t2, time_rxx)
+ )
+
+ # Depolarizing error
+ error_depol1 = depolarizing_error(p_depol1, 1)
+ error_depol2 = depolarizing_error(p_depol2, 2)
+
+ self.noise_model.add_all_qubit_quantum_error(
+ error_depol1.compose(error_thermal_u3), "u"
+ )
+
+ for pair in self.coupling_map:
+ self.noise_model.add_quantum_error(
+ error_depol2.compose(error_thermal_cx), "cx", pair
+ )
+ self.noise_model.add_quantum_error(
+ error_depol2.compose(error_thermal_rxx), ["rxx", "ryy"], pair
+ )
+
+
+def heuristic_fidelity(N, duration, T1=None, T2=None):
+ """Get heuristic fidelity of a circuit."""
+ if T1 is None:
+ T1 = 80e3
+ if T2 is None:
+ T2 = 80e3
+ decay_factor = (1 / T1 + 1 / T2) * duration
+ single_qubit_fidelity = np.exp(-decay_factor)
+ total_fidelity = single_qubit_fidelity**N
+ return total_fidelity
+
+
+def get_noisy_fidelity(qc, coupling_map, sqrt_iswap_basis=False):
+ """Get noisy fidelity of a circuit.
+
+ NOTE: if qc is too big, will use heuristic fidelity function.
+
+ Args:
+ qc (QuantumCircuit): circuit to run, assumes all gates are consolidated
+ coupling_map (CouplingMap): coupling map of device
+
+ Returns:
+ fidelity (float): noisy fidelity of circuit
+ duration (int): duration of circuit
+ circ (QuantumCircuit): transpiled circuit
+ expected_fidelity (float): expected fidelity of circuit
+ """
+ N = coupling_map.size()
+ num_active = len(list(circuit_to_dag(qc).idle_wires())) - qc.num_clbits
+ basis_gates = ["cx", "u", "rxx", "ryy", "id"]
+
+ # Step 0. Create Noise Model
+
+ # 0A. Set up Instruction Durations
+ # (inst, qubits, time)
+ instruction_durations = []
+ for j in range(N):
+ instruction_durations.append(("u", j, time_u3))
+ for j, k in coupling_map:
+ instruction_durations.append(("cx", (j, k), time_cx))
+ instruction_durations.append(("rxx", (j, k), time_rxx))
+ instruction_durations.append(("ryy", (j, k), time_rxx))
+ instruction_durations.append(("save_density_matrix", list(range(N)), 0.0))
+
+ # 0B. If circuit is too big, use heuristic fidelity function
+ # Use heuristic fidelity function
+ circ = transpile(
+ qc,
+ basis_gates=basis_gates,
+ instruction_durations=instruction_durations,
+ scheduling_method="asap",
+ coupling_map=coupling_map,
+ )
+ duration = circ.duration
+ expected_fidelity = heuristic_fidelity(num_active, duration)
+ if N > 10:
+ return 0, duration, circ, expected_fidelity
+ else:
+ transpile_benchy_logger.debug(f"Expected fidelity: {expected_fidelity:.4g}")
+
+ # 0C. Build noise model
+ builder = NoiseModelBuilder(basis_gates, coupling_map)
+ builder.construct_basic_device_model(p_depol1=p1, p_depol2=p2, t1=T1, t2=T2)
+ noise_model = builder.noise_model
+
+ # 0D. Create noisy simulator
+ noisy_simulator = AerSimulator(noise_model=noise_model)
+
+ # Step 1. Given consolidated circuit, decompose into basis gates
+ if sqrt_iswap_basis:
+ decomposer = PassManager()
+ decomposer.append(SiSwapDecomposePass())
+ decomposer.append(Optimize1qGatesDecomposition())
+ qc = decomposer.run(qc)
+
+ # Step 2. Convert into simulator basis gates
+ # simulator = Aer.get_backend("density_matrix_gpu")
+ simulator = QasmSimulator(method="density_matrix")
+ circ = transpile(
+ qc,
+ simulator,
+ basis_gates=basis_gates,
+ coupling_map=coupling_map,
+ )
+
+ # Step 3. transpile with scheduling and durations
+ circ = transpile(
+ qc,
+ noisy_simulator,
+ basis_gates=basis_gates + ["save_density_matrix"],
+ instruction_durations=instruction_durations,
+ scheduling_method="asap",
+ coupling_map=coupling_map,
+ )
+
+ # Step 4. Relaxation noise for idle qubits
+ pm = PassManager()
+ pm.append(ASAPSchedule())
+ pm.append(
+ RelaxationNoisePass(
+ t1s=[T1] * N,
+ t2s=[T2] * N,
+ dt=1e-9,
+ op_types=[Delay],
+ )
+ )
+ circ = pm.run(circ)
+ duration = circ.duration
+
+ # Step 5. Run perfect and noisy simulation and compare
+ circ.save_density_matrix(list(range(N)))
+
+ perfect_result = simulator.run(circ).result().data()["density_matrix"]
+ noisy_result = noisy_simulator.run(circ).result().data()["density_matrix"]
+ fidelity = state_fidelity(perfect_result, noisy_result)
+
+ return fidelity, duration, circ, expected_fidelity
diff --git a/src/mirror_gates/sabre_layout_v2.py b/src/mirror_gates/sabre_layout_v2.py
index 4a3f426..a31c090 100644
--- a/src/mirror_gates/sabre_layout_v2.py
+++ b/src/mirror_gates/sabre_layout_v2.py
@@ -21,9 +21,7 @@
import numpy as np
import rustworkx as rx
-from qiskit._accelerate.nlayout import NLayout
-from qiskit._accelerate.sabre_layout import sabre_layout_and_routing
-from qiskit._accelerate.sabre_swap import Heuristic, NeighborTable
+from qiskit._accelerate.sabre_swap import NeighborTable
from qiskit.converters import dag_to_circuit
from qiskit.tools.parallel import CPU_COUNT
from qiskit.transpiler.basepasses import TransformationPass
@@ -35,7 +33,8 @@
FullAncillaAllocation,
)
from qiskit.transpiler.passes.layout.set_layout import SetLayout
-from qiskit.transpiler.passes.routing.sabre_swap import apply_gate, process_swaps
+
+# from mirror_gates.qiskit.sabre_swap import apply_gate, process_swaps
from qiskit.transpiler.passmanager import PassManager
# from concurrent.futures import ProcessPoolExecutor, TimeoutError, as_completed
@@ -293,85 +292,95 @@ def _run_with_custom_routing(self, dag):
def _run_with_rust_backend(self, dag):
"""Do the original `run` when `self.routing_pass` is `None`."""
- dist_matrix = self.coupling_map.distance_matrix
- original_qubit_indices = {bit: index for index, bit in enumerate(dag.qubits)}
- original_clbit_indices = {bit: index for index, bit in enumerate(dag.clbits)}
-
- dag_list = []
- for node in dag.topological_op_nodes():
- cargs = {original_clbit_indices[x] for x in node.cargs}
- if node.op.condition is not None:
- for clbit in dag._bits_in_condition(node.op.condition):
- cargs.add(original_clbit_indices[clbit])
-
- dag_list.append(
- (
- node._node_id,
- [original_qubit_indices[x] for x in node.qargs],
- cargs,
- )
- )
- (
- (initial_layout, final_layout),
- swap_map,
- gate_order,
- ) = sabre_layout_and_routing(
- len(dag.clbits),
- dag_list,
- self._neighbor_table,
- dist_matrix,
- Heuristic.Decay,
- self.max_iterations,
- self.swap_trials,
- self.layout_trials,
- self.seed,
- )
- # Apply initial layout selected.
- original_dag = dag
- layout_dict = {}
- num_qubits = len(dag.qubits)
- for k, v in initial_layout.layout_mapping():
- if k < num_qubits:
- layout_dict[dag.qubits[k]] = v
- initital_layout = Layout(layout_dict)
- self.property_set["layout"] = initital_layout
- # If skip_routing is set then return the layout in the property set
- # and throwaway the extra work we did to compute the swap map
- if self.skip_routing:
- return dag
- # After this point the pass is no longer an analysis pass and the
- # output circuit returned is transformed with the layout applied
- # and swaps inserted
- dag = self._apply_layout_no_pass_manager(dag)
- # Apply sabre swap ontop of circuit with sabre layout
- final_layout_mapping = final_layout.layout_mapping()
- self.property_set["final_layout"] = Layout(
- {dag.qubits[k]: v for (k, v) in final_layout_mapping}
- )
- mapped_dag = dag.copy_empty_like()
- canonical_register = dag.qregs["q"]
- qubit_indices = {bit: idx for idx, bit in enumerate(canonical_register)}
- original_layout = NLayout.generate_trivial_layout(self.coupling_map.size())
- for node_id in gate_order:
- node = original_dag._multi_graph[node_id]
- process_swaps(
- swap_map,
- node,
- mapped_dag,
- original_layout,
- canonical_register,
- False,
- qubit_indices,
- )
- apply_gate(
- mapped_dag,
- node,
- original_layout,
- canonical_register,
- False,
- layout_dict,
- )
- return mapped_dag
+ raise NotImplementedError("Package conflicts prevent this from being run.")
+
+ # dist_matrix = self.coupling_map.distance_matrix
+ # original_qubit_indices = {
+ # bit: index for index, bit in enumerate(dag.qubits)
+ # }
+ # original_clbit_indices = {
+ # bit: index for index, bit in enumerate(dag.clbits)
+ # }
+
+ # dag_list = []
+ # for node in dag.topological_op_nodes():
+ # cargs = {original_clbit_indices[x] for x in node.cargs}
+ # if node.op.condition is not None:
+ # for clbit in dag._bits_in_condition(node.op.condition):
+ # cargs.add(original_clbit_indices[clbit])
+
+ # dag_list.append(
+ # (
+ # node._node_id,
+ # [original_qubit_indices[x] for x in node.qargs],
+ # cargs,
+ # )
+ # )
+ # (
+ # (initial_layout, final_layout),
+ # swap_map,
+ # gate_order,
+ # ) = sabre_layout_and_routing(
+ # len(dag.clbits),
+ # dag_list,
+ # self._neighbor_table,
+ # dist_matrix,
+ # Heuristic.Decay,
+ # self.max_iterations,
+ # self.swap_trials,
+ # self.layout_trials,
+ # self.seed,
+ # )
+ # # Apply initial layout selected.
+ # original_dag = dag
+ # layout_dict = {}
+ # num_qubits = len(dag.qubits)
+ # for k, v in initial_layout.layout_mapping():
+ # if k < num_qubits:
+ # layout_dict[dag.qubits[k]] = v
+ # initital_layout = Layout(layout_dict)
+ # self.property_set["layout"] = initital_layout
+ # # If skip_routing is set then return the layout in the property set
+ # # and throwaway the extra work we did to compute the swap map
+ # if self.skip_routing:
+ # return dag
+ # # After this point the pass is no longer an analysis pass and the
+ # # output circuit returned is transformed with the layout applied
+ # # and swaps inserted
+ # dag = self._apply_layout_no_pass_manager(dag)
+ # # Apply sabre swap ontop of circuit with sabre layout
+ # final_layout_mapping = final_layout.layout_mapping()
+ # self.property_set["final_layout"] = Layout(
+ # {dag.qubits[k]: v for (k, v) in final_layout_mapping}
+ # )
+ # mapped_dag = dag.copy_empty_like()
+ # canonical_register = dag.qregs["q"]
+ # qubit_indices = {
+ # bit: idx for idx, bit in enumerate(canonical_register)
+ # }
+ # original_layout = NLayout.generate_trivial_layout(
+ # self.coupling_map.size()
+ # )
+ # for node_id in gate_order:
+ # node = original_dag._multi_graph[node_id]
+ # process_swaps(
+ # swap_map,
+ # node,
+ # mapped_dag,
+ # original_layout,
+ # canonical_register,
+ # False,
+ # qubit_indices,
+ # )
+ # apply_gate(
+ # mapped_dag,
+ # node,
+ # original_layout,
+ # canonical_register,
+ # False,
+ # layout_dict,
+ # )
+ # return mapped_dag
def run(self, dag):
"""Run the SabreLayout pass on `dag`.
diff --git a/src/mirror_gates/sqiswap_decomposer.py b/src/mirror_gates/sqiswap_decomposer.py
new file mode 100644
index 0000000..4ff5f9c
--- /dev/null
+++ b/src/mirror_gates/sqiswap_decomposer.py
@@ -0,0 +1,34 @@
+"""Decompose 2Q gates into SiSwap gates.
+
+Relies on this branch: https://github.com/evmckinney9/qiskit-evmckinney9/tree/sqisw-gate
+where I merged this PR (https://github.com/Qiskit/qiskit/pull/9375)
+into qiskit-terra 0.24.2
+"""
+
+from qiskit.converters import circuit_to_dag
+from qiskit.synthesis.su4 import SiSwapDecomposer
+from qiskit.transpiler.basepasses import TransformationPass
+
+from mirror_gates.fast_unitary import FastConsolidateBlocks
+
+decomp = SiSwapDecomposer(euler_basis=["u"])
+
+
+class SiSwapDecomposePass(TransformationPass):
+ """Decompose 2Q gates into SiSwap gates."""
+
+ def __init__(self):
+ """Initialize the SiSwapDecomposePass pass."""
+ super().__init__()
+ self.requires = [FastConsolidateBlocks(coord_caching=True)]
+
+ def run(self, dag):
+ """Run the SiSwapDecomposePass pass on `dag`."""
+ # for every 2Q gate
+ for node in dag.two_qubit_ops():
+ decomp_node = decomp(node.op)
+ decomp_dag = circuit_to_dag(decomp_node)
+ # dag.substitute_node(node, decomp_node)
+ dag.substitute_node_with_dag(node, decomp_dag)
+
+ return dag
diff --git a/src/notebooks/deprecated/dev_consolidate.ipynb b/src/notebooks/deprecated/dev_consolidate.ipynb
index db7bec8..faf1edc 100644
--- a/src/notebooks/deprecated/dev_consolidate.ipynb
+++ b/src/notebooks/deprecated/dev_consolidate.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -34,7 +34,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -60,7 +60,7 @@
"from transpile_benchy.interfaces.qasm_interface import QASMBench\n",
"from transpile_benchy.library import CircuitLibrary\n",
"\n",
- "lib = CircuitLibrary.from_txt(\"../../circuits/medium_circuits.txt\")\n",
+ "lib = CircuitLibrary.from_txt(\"../circuits/medium_circuits.txt\")\n",
"qc = lib.get_circuit(\"seca_n11\")\n",
"# lib = CircuitLibrary(circuit_list=[\"qft_n4\"])\n",
"# qc = lib.get_circuit(\"qft_n4\")\n",
@@ -73,7 +73,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -95,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -1650,8 +1650,8 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/home/evm9/mirror-gates/src/notebooks/results/debug_consolidate.ipynb Cell 4\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 32\u001b[0m \u001b[39mfor\u001b[39;00m gate \u001b[39min\u001b[39;00m qc0:\n\u001b[1;32m 33\u001b[0m \u001b[39mif\u001b[39;00m gate\u001b[39m.\u001b[39moperation\u001b[39m.\u001b[39mname \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mbarrier\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[1;32m 34\u001b[0m \u001b[39m# print(gate.operation)\\\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m corrd \u001b[39m=\u001b[39m c1c2c3(gate\u001b[39m.\u001b[39;49moperation\u001b[39m.\u001b[39;49mto_matrix())\n\u001b[1;32m 36\u001b[0m list1\u001b[39m.\u001b[39mappend(corrd)\n\u001b[1;32m 37\u001b[0m \u001b[39m# print(corrd)\u001b[39;00m\n",
- "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/weylchamber/coordinates.py:40\u001b[0m, in \u001b[0;36mc1c2c3\u001b[0;34m(U, ndigits)\u001b[0m\n\u001b[1;32m 38\u001b[0m U \u001b[39m=\u001b[39m qutip\u001b[39m.\u001b[39mQobj(U, dims\u001b[39m=\u001b[39m[[\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m], [\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m]])\n\u001b[1;32m 39\u001b[0m \u001b[39mif\u001b[39;00m U\u001b[39m.\u001b[39mshape \u001b[39m!=\u001b[39m (\u001b[39m4\u001b[39m, \u001b[39m4\u001b[39m):\n\u001b[0;32m---> 40\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mGates must have a 4\u00d74 shape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 41\u001b[0m U_tilde \u001b[39m=\u001b[39m SySy \u001b[39m*\u001b[39m U\u001b[39m.\u001b[39mtrans() \u001b[39m*\u001b[39m SySy\n\u001b[1;32m 42\u001b[0m ev \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39meigvals(\n\u001b[1;32m 43\u001b[0m ((U \u001b[39m*\u001b[39m U_tilde)\u001b[39m.\u001b[39mfull())\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39msqrt(\u001b[39mcomplex\u001b[39m(np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mdet(U\u001b[39m.\u001b[39mfull()))))\n",
+ "\u001b[1;32m/home/evm9/mirror-gates/src/notebooks/debug_consolidate.ipynb Cell 4\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 32\u001b[0m \u001b[39mfor\u001b[39;00m gate \u001b[39min\u001b[39;00m qc0:\n\u001b[1;32m 33\u001b[0m \u001b[39mif\u001b[39;00m gate\u001b[39m.\u001b[39moperation\u001b[39m.\u001b[39mname \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mbarrier\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[1;32m 34\u001b[0m \u001b[39m# print(gate.operation)\\\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m corrd \u001b[39m=\u001b[39m c1c2c3(gate\u001b[39m.\u001b[39;49moperation\u001b[39m.\u001b[39;49mto_matrix())\n\u001b[1;32m 36\u001b[0m list1\u001b[39m.\u001b[39mappend(corrd)\n\u001b[1;32m 37\u001b[0m \u001b[39m# print(corrd)\u001b[39;00m\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.11/site-packages/weylchamber/coordinates.py:40\u001b[0m, in \u001b[0;36mc1c2c3\u001b[0;34m(U, ndigits)\u001b[0m\n\u001b[1;32m 38\u001b[0m U \u001b[39m=\u001b[39m qutip\u001b[39m.\u001b[39mQobj(U, dims\u001b[39m=\u001b[39m[[\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m], [\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m]])\n\u001b[1;32m 39\u001b[0m \u001b[39mif\u001b[39;00m U\u001b[39m.\u001b[39mshape \u001b[39m!=\u001b[39m (\u001b[39m4\u001b[39m, \u001b[39m4\u001b[39m):\n\u001b[0;32m---> 40\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mGates must have a 4\u00d74 shape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 41\u001b[0m U_tilde \u001b[39m=\u001b[39m SySy \u001b[39m*\u001b[39m U\u001b[39m.\u001b[39mtrans() \u001b[39m*\u001b[39m SySy\n\u001b[1;32m 42\u001b[0m ev \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39meigvals(\n\u001b[1;32m 43\u001b[0m ((U \u001b[39m*\u001b[39m U_tilde)\u001b[39m.\u001b[39mfull())\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39msqrt(\u001b[39mcomplex\u001b[39m(np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mdet(U\u001b[39m.\u001b[39mfull()))))\n",
"\u001b[0;31mValueError\u001b[0m: Gates must have a 4\u00d74 shape"
]
}
@@ -1733,7 +1733,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.9.17"
},
"orig_nbformat": 4
},
diff --git a/src/notebooks/results/01_bench.ipynb b/src/notebooks/results/01_bench.ipynb
new file mode 100644
index 0000000..c758c1e
--- /dev/null
+++ b/src/notebooks/results/01_bench.ipynb
@@ -0,0 +1,554 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transpile_benchy.metrics.gate_counts import (\n",
+ " DepthMetric,\n",
+ " TotalMetric,\n",
+ " TotalSwaps,\n",
+ ")\n",
+ "from qiskit.circuit.library import iSwapGate\n",
+ "from qiskit.transpiler import CouplingMap\n",
+ "from mirror_gates.pass_managers import Mirage, QiskitLevel3\n",
+ "from mirror_gates.utilities import SubsMetric\n",
+ "from mirror_gates.logging import transpile_benchy_logger"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# N = 4\n",
+ "# coupling_map = CouplingMap.from_line(N)\n",
+ "# coupling_map = CouplingMap.from_heavy_hex(5)\n",
+ "coupling_map = CouplingMap.from_grid(6, 6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transpile_benchy.library import CircuitLibrary\n",
+ "\n",
+ "library = CircuitLibrary.from_txt(\"../../../circuits/medium_circuits.txt\")\n",
+ "# library = CircuitLibrary.from_txt(\"../../circuits/debug.txt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# XXX set consolidate to False\n",
+ "# this is allowed only because my pass manager will preserve consolidation\n",
+ "# see post_stage, I call fastconsolidate manually\n",
+ "\n",
+ "# NOTE: use TotalSwaps to verify baselines have > 0 swaps\n",
+ "# otherwise, there is no room for improvement.\n",
+ "# we can include these if we want to show our methods will still work\n",
+ "# but somewhat trivial since we just append VF2Layout\n",
+ "metrics = [\n",
+ " DepthMetric(consolidate=False),\n",
+ " TotalMetric(consolidate=False),\n",
+ " TotalSwaps(consolidate=False),\n",
+ " SubsMetric(),\n",
+ "]\n",
+ "\n",
+ "transpilers = [\n",
+ " # QiskitLevel3(coupling_map, cx_basis=True),\n",
+ " # Mirage(coupling_map, cx_basis=True, parallel=0),\n",
+ " QiskitLevel3(coupling_map),\n",
+ " Mirage(coupling_map, logger=transpile_benchy_logger),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:transpile_benchy:Running benchmarks for circuits...\n",
+ "Circuits from library: 0%| | 0/15 [00:00, ?it/s]INFO:transpile_benchy:Running benchmark for circuit qec9xz_n17\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading qec9xz_n17 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 7%|\u258b | 1/15 [02:02<28:33, 122.43s/it]INFO:transpile_benchy:Running benchmark for circuit seca_n11\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading seca_n11 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 13%|\u2588\u258e | 2/15 [04:16<28:02, 129.42s/it]INFO:transpile_benchy:Running benchmark for circuit qram_n20\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading qram_n20 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 20%|\u2588\u2588 | 3/15 [06:57<28:45, 143.78s/it]INFO:transpile_benchy:Running benchmark for circuit knn_n25\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading knn_n25 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 27%|\u2588\u2588\u258b | 4/15 [09:38<27:35, 150.50s/it]INFO:transpile_benchy:Running benchmark for circuit swap_test_n25\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading swap_test_n25 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 33%|\u2588\u2588\u2588\u258e | 5/15 [12:19<25:43, 154.39s/it]INFO:transpile_benchy:Running benchmark for circuit bigadder_n18\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading bigadder_n18 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 40%|\u2588\u2588\u2588\u2588 | 6/15 [15:25<24:46, 165.17s/it]INFO:transpile_benchy:Running benchmark for circuit multiplier_n15\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading multiplier_n15 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 47%|\u2588\u2588\u2588\u2588\u258b | 7/15 [19:39<25:54, 194.26s/it]INFO:transpile_benchy:Running benchmark for circuit qft_n18\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading qft_n18 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 53%|\u2588\u2588\u2588\u2588\u2588\u258e | 8/15 [24:14<25:38, 219.80s/it]INFO:transpile_benchy:Running benchmark for circuit sat_n11\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading sat_n11 from QASMBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 9/15 [28:24<22:55, 229.21s/it]INFO:transpile_benchy:Running benchmark for circuit circuit-166607\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading portfolioqaoa_n16 from MQTBench\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Circuits from library: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 9/15 [33:11<22:07, 221.32s/it]\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/home/evm9/mirror-gates/src/notebooks/results/main_bench/02_SL_bench.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtranspile_benchy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbenchmark\u001b[39;00m \u001b[39mimport\u001b[39;00m Benchmark\n\u001b[1;32m 3\u001b[0m benchmark \u001b[39m=\u001b[39m Benchmark(\n\u001b[1;32m 4\u001b[0m transpilers\u001b[39m=\u001b[39mtranspilers,\n\u001b[1;32m 5\u001b[0m circuit_library\u001b[39m=\u001b[39mlibrary,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 8\u001b[0m num_runs\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m,\n\u001b[1;32m 9\u001b[0m )\n\u001b[0;32m---> 11\u001b[0m benchmark\u001b[39m.\u001b[39;49mrun()\n\u001b[1;32m 12\u001b[0m \u001b[39m# print(benchmark)\u001b[39;00m\n",
+ "File \u001b[0;32m~/transpile_benchy/src/transpile_benchy/benchmark.py:107\u001b[0m, in \u001b[0;36mBenchmark.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 101\u001b[0m total \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlibrary\u001b[39m.\u001b[39mcircuit_count()\n\u001b[1;32m 102\u001b[0m \u001b[39mfor\u001b[39;00m circuit \u001b[39min\u001b[39;00m tqdm(\n\u001b[1;32m 103\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlibrary,\n\u001b[1;32m 104\u001b[0m total\u001b[39m=\u001b[39mtotal,\n\u001b[1;32m 105\u001b[0m desc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCircuits from library\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 106\u001b[0m ):\n\u001b[0;32m--> 107\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrun_single_circuit(circuit)\n",
+ "File \u001b[0;32m~/transpile_benchy/src/transpile_benchy/benchmark.py:89\u001b[0m, in \u001b[0;36mBenchmark.run_single_circuit\u001b[0;34m(self, circuit)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[39mfor\u001b[39;00m transpiler \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtranspilers:\n\u001b[1;32m 88\u001b[0m \u001b[39mfor\u001b[39;00m run_index \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnum_runs):\n\u001b[0;32m---> 89\u001b[0m transpiled_circuit \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_try_transpilation(\n\u001b[1;32m 90\u001b[0m transpiler, circuit, run_index\n\u001b[1;32m 91\u001b[0m )\n\u001b[1;32m 92\u001b[0m \u001b[39mif\u001b[39;00m transpiled_circuit \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 93\u001b[0m \u001b[39mcontinue\u001b[39;00m\n",
+ "File \u001b[0;32m~/transpile_benchy/src/transpile_benchy/benchmark.py:78\u001b[0m, in \u001b[0;36mBenchmark._try_transpilation\u001b[0;34m(self, transpiler, circuit, run_index)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(transpiler, \u001b[39m\"\u001b[39m\u001b[39mseed\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 76\u001b[0m transpiler\u001b[39m.\u001b[39mseed \u001b[39m=\u001b[39m run_index\n\u001b[0;32m---> 78\u001b[0m transpiled_circuit \u001b[39m=\u001b[39m transpiler\u001b[39m.\u001b[39;49mrun(circuit)\n\u001b[1;32m 79\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 80\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTranspiler failed\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
+ "File \u001b[0;32m~/mirror-gates/src/mirror_gates/pass_managers.py:141\u001b[0m, in \u001b[0;36mCustomLayoutRoutingManager.run\u001b[0;34m(self, circuit)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\u001b[39mself\u001b[39m, circuit):\n\u001b[1;32m 140\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Run the transpiler on the circuit.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 141\u001b[0m circuit \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mrun(circuit)\n\u001b[1;32m 143\u001b[0m \u001b[39m# FIXME: either benchmarker uses default value\u001b[39;00m\n\u001b[1;32m 144\u001b[0m \u001b[39m# or we configure SubsMetric differently\u001b[39;00m\n\u001b[1;32m 145\u001b[0m \u001b[39m# accepted_subs missing if QiskitRunner is used or if VF2Layout succeeds\u001b[39;00m\n\u001b[1;32m 146\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39maccepted_subs\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperty_set:\n",
+ "File \u001b[0;32m~/transpile_benchy/src/transpile_benchy/passmanagers/abc_runner.py:61\u001b[0m, in \u001b[0;36mCustomPassManager.run\u001b[0;34m(self, circuit)\u001b[0m\n\u001b[1;32m 59\u001b[0m stage_end \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m# start timer for each stage\u001b[39;00m\n\u001b[1;32m 60\u001b[0m stage\u001b[39m.\u001b[39mproperty_set \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperty_set\n\u001b[0;32m---> 61\u001b[0m circuit \u001b[39m=\u001b[39m stage\u001b[39m.\u001b[39;49mrun(circuit)\n\u001b[1;32m 62\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperty_set\u001b[39m.\u001b[39mupdate(stage\u001b[39m.\u001b[39mproperty_set)\n\u001b[1;32m 63\u001b[0m stage_start \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m# end timer for each stage\u001b[39;00m\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/qiskit/transpiler/passmanager.py:231\u001b[0m, in \u001b[0;36mPassManager.run\u001b[0;34m(self, circuits, output_name, callback)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[39mreturn\u001b[39;00m circuits\n\u001b[1;32m 230\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(circuits, QuantumCircuit):\n\u001b[0;32m--> 231\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_single_circuit(circuits, output_name, callback)\n\u001b[1;32m 232\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(circuits) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 233\u001b[0m \u001b[39mreturn\u001b[39;00m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_single_circuit(circuits[\u001b[39m0\u001b[39m], output_name, callback)]\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/qiskit/transpiler/passmanager.py:292\u001b[0m, in \u001b[0;36mPassManager._run_single_circuit\u001b[0;34m(self, circuit, output_name, callback)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Run all the passes on a ``circuit``.\u001b[39;00m\n\u001b[1;32m 281\u001b[0m \n\u001b[1;32m 282\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[39m The transformed circuit.\u001b[39;00m\n\u001b[1;32m 290\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 291\u001b[0m running_passmanager \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_running_passmanager()\n\u001b[0;32m--> 292\u001b[0m result \u001b[39m=\u001b[39m running_passmanager\u001b[39m.\u001b[39;49mrun(circuit, output_name\u001b[39m=\u001b[39;49moutput_name, callback\u001b[39m=\u001b[39;49mcallback)\n\u001b[1;32m 293\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperty_set \u001b[39m=\u001b[39m running_passmanager\u001b[39m.\u001b[39mproperty_set\n\u001b[1;32m 294\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/qiskit/transpiler/runningpassmanager.py:125\u001b[0m, in \u001b[0;36mRunningPassManager.run\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[39mfor\u001b[39;00m passset \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mworking_list:\n\u001b[1;32m 124\u001b[0m \u001b[39mfor\u001b[39;00m pass_ \u001b[39min\u001b[39;00m passset:\n\u001b[0;32m--> 125\u001b[0m dag \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_pass(pass_, dag, passset\u001b[39m.\u001b[39;49moptions)\n\u001b[1;32m 127\u001b[0m circuit \u001b[39m=\u001b[39m dag_to_circuit(dag, copy_operations\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 128\u001b[0m \u001b[39mif\u001b[39;00m output_name:\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/qiskit/transpiler/runningpassmanager.py:173\u001b[0m, in \u001b[0;36mRunningPassManager._do_pass\u001b[0;34m(self, pass_, dag, options)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[39m# Run the pass itself, if not already run\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[39mif\u001b[39;00m pass_ \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvalid_passes:\n\u001b[0;32m--> 173\u001b[0m dag \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_this_pass(pass_, dag)\n\u001b[1;32m 175\u001b[0m \u001b[39m# update the valid_passes property\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_valid_passes(pass_)\n",
+ "File \u001b[0;32m~/mirror-gates/.venv/lib/python3.10/site-packages/qiskit/transpiler/runningpassmanager.py:202\u001b[0m, in \u001b[0;36mRunningPassManager._run_this_pass\u001b[0;34m(self, pass_, dag)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[39mif\u001b[39;00m pass_\u001b[39m.\u001b[39mis_transformation_pass:\n\u001b[1;32m 200\u001b[0m \u001b[39m# Measure time if we have a callback or logging set\u001b[39;00m\n\u001b[1;32m 201\u001b[0m start_time \u001b[39m=\u001b[39m time()\n\u001b[0;32m--> 202\u001b[0m new_dag \u001b[39m=\u001b[39m pass_\u001b[39m.\u001b[39;49mrun(dag)\n\u001b[1;32m 203\u001b[0m end_time \u001b[39m=\u001b[39m time()\n\u001b[1;32m 204\u001b[0m run_time \u001b[39m=\u001b[39m end_time \u001b[39m-\u001b[39m start_time\n",
+ "File \u001b[0;32m~/mirror-gates/src/mirror_gates/sabre_layout_v2.py:396\u001b[0m, in \u001b[0;36mSabreLayout.run\u001b[0;34m(self, dag)\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init_circ \u001b[39m=\u001b[39m dag_to_circuit(dag)\n\u001b[1;32m 395\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init_rev_circ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init_circ\u001b[39m.\u001b[39mreverse_ops()\n\u001b[0;32m--> 396\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_with_custom_routing(dag)\n\u001b[1;32m 397\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 398\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_with_rust_backend(dag)\n",
+ "File \u001b[0;32m~/mirror-gates/src/mirror_gates/sabre_layout_v2.py:239\u001b[0m, in \u001b[0;36mSabreLayout._run_with_custom_routing\u001b[0;34m(self, dag)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperty_set[\u001b[39m\"\u001b[39m\u001b[39mlayout_trials\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m []\n\u001b[1;32m 237\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mparallel:\n\u001b[1;32m 238\u001b[0m \u001b[39m# Create a multiprocessing pool.\u001b[39;00m\n\u001b[0;32m--> 239\u001b[0m \u001b[39mwith\u001b[39;00m Pool() \u001b[39mas\u001b[39;00m pool:\n\u001b[1;32m 240\u001b[0m results \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39mmap(\n\u001b[1;32m 241\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_single_layout_restart, \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayout_trials)\n\u001b[1;32m 242\u001b[0m )\n\u001b[1;32m 243\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+ "File \u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py:649\u001b[0m, in \u001b[0;36mExecutor.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__exit__\u001b[39m(\u001b[39mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[0;32m--> 649\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshutdown(wait\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 650\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n",
+ "File \u001b[0;32m/usr/lib/python3.10/concurrent/futures/process.py:775\u001b[0m, in \u001b[0;36mProcessPoolExecutor.shutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m 772\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executor_manager_thread_wakeup\u001b[39m.\u001b[39mwakeup()\n\u001b[1;32m 774\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executor_manager_thread \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m wait:\n\u001b[0;32m--> 775\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_executor_manager_thread\u001b[39m.\u001b[39;49mjoin()\n\u001b[1;32m 776\u001b[0m \u001b[39m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[1;32m 777\u001b[0m \u001b[39m# objects that use file descriptors.\u001b[39;00m\n\u001b[1;32m 778\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executor_manager_thread \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+ "File \u001b[0;32m/usr/lib/python3.10/threading.py:1096\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcannot join current thread\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 1095\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1096\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wait_for_tstate_lock()\n\u001b[1;32m 1097\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1098\u001b[0m \u001b[39m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[39m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[39m=\u001b[39m\u001b[39mmax\u001b[39m(timeout, \u001b[39m0\u001b[39m))\n",
+ "File \u001b[0;32m/usr/lib/python3.10/threading.py:1116\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m 1115\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1116\u001b[0m \u001b[39mif\u001b[39;00m lock\u001b[39m.\u001b[39;49macquire(block, timeout):\n\u001b[1;32m 1117\u001b[0m lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m 1118\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stop()\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "from transpile_benchy.benchmark import Benchmark\n",
+ "\n",
+ "benchmark = Benchmark(\n",
+ " transpilers=transpilers,\n",
+ " circuit_library=library,\n",
+ " metrics=metrics,\n",
+ " logger=transpile_benchy_logger,\n",
+ " num_runs=5,\n",
+ ")\n",
+ "\n",
+ "benchmark.run()\n",
+ "# print(benchmark)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Transpiler: Mirage-$\\sqrt{\\texttt{iSWAP}}$\n",
+ "\n",
+ " Metric: accepted_subs\n",
+ " Circuit: ae_n16 Mean result: 0.933 Trials: [1.0, 0.9327731092436975, 0.7899159663865546, 1.0, 0.9411764705882353]\n",
+ " Circuit: bigadder_n18 Mean result: 0.244 Trials: [0.20353982300884957, 0.23893805309734514, 0.3274336283185841, 0.22123893805309736, 0.23008849557522124]\n",
+ " Circuit: knn_n25 Mean result: 0.344 Trials: [0.5352112676056338, 0.3380281690140845, 0.30985915492957744, 0.22535211267605634, 0.30985915492957744]\n",
+ " Circuit: multiplier_n15 Mean result: 0.397 Trials: [0.38071065989847713, 0.45685279187817257, 0.41116751269035534, 0.36548223350253806, 0.37055837563451777]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 1.000 Trials: [1.0, 1.0, 1.0, 1.0, 1.0]\n",
+ " Circuit: qec9xz_n17 Mean result: 0.432 Trials: [0.41935483870967744, 0.45161290322580644, 0.2903225806451613, 0.3548387096774194, 0.6451612903225806]\n",
+ " Circuit: qft_n18 Mean result: 0.996 Trials: [1.0, 1.0, 0.9802631578947368, 1.0, 1.0]\n",
+ " Circuit: qftentangled_n16 Mean result: 0.803 Trials: [0.8059701492537313, 0.8134328358208955, 0.8880597014925373, 0.6194029850746269, 0.8880597014925373]\n",
+ " Circuit: qpeexact_n16 Mean result: 0.933 Trials: [1.0, 0.9327731092436975, 0.7899159663865546, 1.0, 0.9411764705882353]\n",
+ " Circuit: qram_n20 Mean result: 0.353 Trials: [0.3116883116883117, 0.35064935064935066, 0.2597402597402597, 0.36363636363636365, 0.4805194805194805]\n",
+ " Circuit: sat_n11 Mean result: 0.397 Trials: [0.4019138755980861, 0.430622009569378, 0.3492822966507177, 0.4258373205741627, 0.37799043062200954]\n",
+ " Circuit: seca_n11 Mean result: 0.282 Trials: [0.3013698630136986, 0.273972602739726, 0.273972602739726, 0.3013698630136986, 0.2602739726027397]\n",
+ " Circuit: swap_test_n25 Mean result: 0.344 Trials: [0.5352112676056338, 0.3380281690140845, 0.30985915492957744, 0.22535211267605634, 0.30985915492957744]\n",
+ "\n",
+ " Metric: monodromy_depth\n",
+ " Circuit: ae_n16 Mean result: 67.312 Trials: [62.0, 67.5, 80.0, 65.0, 63.5]\n",
+ " Circuit: bigadder_n18 Mean result: 87.793 Trials: [88.5, 88.5, 86.0, 87.0, 89.0]\n",
+ " Circuit: knn_n25 Mean result: 64.635 Trials: [64.0, 60.0, 65.5, 65.0, 69.0]\n",
+ " Circuit: multiplier_n15 Mean result: 147.498 Trials: [139.0, 155.0, 138.0, 151.0, 155.5]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 90.500 Trials: [90.5, 90.5, 90.5, 90.5, 90.5]\n",
+ " Circuit: qec9xz_n17 Mean result: 15.751 Trials: [18.0, 19.0, 14.0, 15.0, 13.5]\n",
+ " Circuit: qft_n18 Mean result: 52.341 Trials: [48.5, 48.5, 48.5, 48.5, 71.0]\n",
+ " Circuit: qftentangled_n16 Mean result: 52.662 Trials: [52.0, 49.0, 45.0, 78.5, 45.0]\n",
+ " Circuit: qpeexact_n16 Mean result: 66.810 Trials: [61.5, 67.0, 79.5, 64.5, 63.0]\n",
+ " Circuit: qram_n20 Mean result: 76.280 Trials: [69.0, 74.5, 80.5, 79.0, 79.0]\n",
+ " Circuit: sat_n11 Mean result: 209.375 Trials: [210.0, 211.0, 198.5, 207.0, 221.0]\n",
+ " Circuit: seca_n11 Mean result: 33.892 Trials: [33.5, 34.5, 35.0, 33.0, 33.5]\n",
+ " Circuit: swap_test_n25 Mean result: 64.635 Trials: [64.0, 60.0, 65.5, 65.0, 69.0]\n",
+ "\n",
+ " Metric: monodromy_total\n",
+ " Circuit: ae_n16 Mean result: 182.935 Trials: [182.0, 180.0, 192.5, 182.0, 178.5]\n",
+ " Circuit: bigadder_n18 Mean result: 135.863 Trials: [130.0, 136.5, 136.5, 137.0, 139.5]\n",
+ " Circuit: knn_n25 Mean result: 93.162 Trials: [95.0, 83.5, 96.5, 95.0, 96.5]\n",
+ " Circuit: multiplier_n15 Mean result: 253.759 Trials: [238.0, 252.5, 247.0, 267.0, 265.5]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 539.000 Trials: [539.0, 539.0, 539.0, 539.0, 539.0]\n",
+ " Circuit: qec9xz_n17 Mean result: 43.162 Trials: [45.5, 50.0, 41.0, 44.0, 36.5]\n",
+ " Circuit: qft_n18 Mean result: 231.424 Trials: [228.5, 228.5, 228.5, 228.5, 243.5]\n",
+ " Circuit: qftentangled_n16 Mean result: 200.219 Trials: [198.0, 197.5, 194.5, 217.5, 194.5]\n",
+ " Circuit: qpeexact_n16 Mean result: 182.435 Trials: [181.5, 179.5, 192.0, 181.5, 178.0]\n",
+ " Circuit: qram_n20 Mean result: 110.585 Trials: [98.0, 112.5, 118.0, 112.0, 113.5]\n",
+ " Circuit: sat_n11 Mean result: 260.477 Trials: [261.5, 261.5, 254.5, 265.0, 260.0]\n",
+ " Circuit: seca_n11 Mean result: 73.791 Trials: [73.5, 75.0, 75.0, 72.0, 73.5]\n",
+ " Circuit: swap_test_n25 Mean result: 93.162 Trials: [95.0, 83.5, 96.5, 95.0, 96.5]\n",
+ "\n",
+ " Metric: total_runtime\n",
+ " Circuit: ae_n16 Mean result: 98.892 Trials: [98.75190782546997, 101.36828851699829, 96.25164723396301, 99.49494457244873, 98.59378910064697]\n",
+ " Circuit: bigadder_n18 Mean result: 60.308 Trials: [58.578924894332886, 60.44477844238281, 60.33641576766968, 58.738484621047974, 63.44267654418945]\n",
+ " Circuit: knn_n25 Mean result: 55.787 Trials: [59.37458252906799, 58.333773136138916, 52.73238182067871, 52.89974117279053, 55.59314966201782]\n",
+ " Circuit: multiplier_n15 Mean result: 87.824 Trials: [90.42920804023743, 86.22878050804138, 88.13999366760254, 84.22012209892273, 90.1005027294159]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 156.437 Trials: [151.85605192184448, 159.13030862808228, 160.82827734947205, 157.3104419708252, 153.06163454055786]\n",
+ " Circuit: qec9xz_n17 Mean result: 47.882 Trials: [52.82168698310852, 47.28297209739685, 45.99554681777954, 46.84721922874451, 46.463829040527344]\n",
+ " Circuit: qft_n18 Mean result: 83.004 Trials: [87.6683349609375, 83.59174466133118, 78.99340534210205, 80.79135036468506, 83.97451543807983]\n",
+ " Circuit: qftentangled_n16 Mean result: 88.544 Trials: [88.03295731544495, 87.76529598236084, 89.96857833862305, 86.58528327941895, 90.368497133255]\n",
+ " Circuit: qpeexact_n16 Mean result: 93.193 Trials: [86.40069675445557, 90.55616903305054, 92.97855997085571, 98.67332220077515, 97.35458159446716]\n",
+ " Circuit: qram_n20 Mean result: 53.363 Trials: [53.45637345314026, 55.51834177970886, 52.618696451187134, 52.285887718200684, 52.934725284576416]\n",
+ " Circuit: sat_n11 Mean result: 83.124 Trials: [84.04471230506897, 82.66998147964478, 85.94827461242676, 83.07049226760864, 79.88657188415527]\n",
+ " Circuit: seca_n11 Mean result: 53.094 Trials: [52.64947533607483, 52.174591064453125, 52.89228940010071, 53.319941997528076, 54.434468269348145]\n",
+ " Circuit: swap_test_n25 Mean result: 54.687 Trials: [57.61495757102966, 57.67288517951965, 53.60969638824463, 52.333826541900635, 52.20472288131714]\n",
+ "\n",
+ " Metric: total_swaps\n",
+ " Circuit: ae_n16 Mean result: 2.897 Trials: [2, 3, 17, 2, 1]\n",
+ " Circuit: bigadder_n18 Mean result: 13.429 Trials: [10, 14, 13, 15, 16]\n",
+ " Circuit: knn_n25 Mean result: 11.553 Trials: [14, 5, 14, 15, 14]\n",
+ " Circuit: multiplier_n15 Mean result: 33.352 Trials: [24, 31, 30, 43, 43]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qec9xz_n17 Mean result: 6.892 Trials: [9, 12, 6, 8, 3]\n",
+ " Circuit: qft_n18 Mean result: 7.300 Trials: [6, 6, 6, 6, 16]\n",
+ " Circuit: qftentangled_n16 Mean result: 3.965 Trials: [7, 5, 1, 28, 1]\n",
+ " Circuit: qpeexact_n16 Mean result: 2.897 Trials: [2, 3, 17, 2, 1]\n",
+ " Circuit: qram_n20 Mean result: 19.254 Trials: [12, 21, 25, 21, 20]\n",
+ " Circuit: sat_n11 Mean result: 25.741 Trials: [27, 25, 23, 28, 26]\n",
+ " Circuit: seca_n11 Mean result: 0.000 Trials: [1, 2, 2, 0, 1]\n",
+ " Circuit: swap_test_n25 Mean result: 11.553 Trials: [14, 5, 14, 15, 14]\n",
+ "\n",
+ "Transpiler: Qiskit-$\\sqrt{\\texttt{iSWAP}}$\n",
+ "\n",
+ " Metric: accepted_subs\n",
+ " Circuit: ae_n16 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: bigadder_n18 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: knn_n25 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: multiplier_n15 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qec9xz_n17 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qft_n18 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qftentangled_n16 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qpeexact_n16 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: qram_n20 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: sat_n11 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: seca_n11 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ " Circuit: swap_test_n25 Mean result: 0.000 Trials: [0, 0, 0, 0, 0]\n",
+ "\n",
+ " Metric: monodromy_depth\n",
+ " Circuit: ae_n16 Mean result: 120.638 Trials: [122.0, 138.5, 117.5, 110.0, 117.0]\n",
+ " Circuit: bigadder_n18 Mean result: 109.040 Trials: [110.0, 107.5, 112.0, 113.0, 103.0]\n",
+ " Circuit: knn_n25 Mean result: 69.573 Trials: [66.0, 70.0, 71.0, 71.5, 69.5]\n",
+ " Circuit: multiplier_n15 Mean result: 175.528 Trials: [174.5, 183.5, 168.0, 174.5, 177.5]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 236.824 Trials: [208.5, 263.0, 224.5, 244.5, 247.5]\n",
+ " Circuit: qec9xz_n17 Mean result: 22.211 Trials: [21.0, 18.0, 26.0, 22.0, 25.0]\n",
+ " Circuit: qft_n18 Mean result: 108.948 Trials: [131.0, 113.5, 117.0, 97.5, 90.5]\n",
+ " Circuit: qftentangled_n16 Mean result: 95.484 Trials: [109.5, 100.0, 92.0, 95.5, 82.5]\n",
+ " Circuit: qpeexact_n16 Mean result: 139.716 Trials: [148.5, 140.0, 133.5, 138.5, 138.5]\n",
+ " Circuit: qram_n20 Mean result: 83.561 Trials: [76.5, 86.0, 90.5, 85.0, 80.5]\n",
+ " Circuit: sat_n11 Mean result: 235.682 Trials: [237.0, 229.5, 230.5, 232.0, 250.0]\n",
+ " Circuit: seca_n11 Mean result: 49.288 Trials: [47.0, 45.0, 54.5, 51.5, 49.0]\n",
+ " Circuit: swap_test_n25 Mean result: 69.392 Trials: [70.0, 68.5, 69.5, 68.0, 71.0]\n",
+ "\n",
+ " Metric: monodromy_total\n",
+ " Circuit: ae_n16 Mean result: 207.763 Trials: [206.5, 215.0, 205.5, 203.5, 208.5]\n",
+ " Circuit: bigadder_n18 Mean result: 153.085 Trials: [154.5, 156.0, 153.5, 151.5, 150.0]\n",
+ " Circuit: knn_n25 Mean result: 92.664 Trials: [88.0, 92.0, 94.5, 94.5, 94.5]\n",
+ " Circuit: multiplier_n15 Mean result: 287.812 Trials: [280.0, 294.0, 282.0, 285.0, 298.5]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 624.746 Trials: [612.5, 636.5, 621.0, 630.5, 623.5]\n",
+ " Circuit: qec9xz_n17 Mean result: 46.375 Trials: [44.0, 47.0, 45.5, 48.5, 47.0]\n",
+ " Circuit: qft_n18 Mean result: 256.646 Trials: [266.0, 263.5, 260.0, 253.0, 241.5]\n",
+ " Circuit: qftentangled_n16 Mean result: 238.954 Trials: [247.5, 238.0, 235.5, 234.0, 240.0]\n",
+ " Circuit: qpeexact_n16 Mean result: 239.833 Trials: [243.5, 243.0, 236.5, 246.0, 230.5]\n",
+ " Circuit: qram_n20 Mean result: 107.336 Trials: [101.0, 110.0, 110.5, 110.0, 105.5]\n",
+ " Circuit: sat_n11 Mean result: 299.262 Trials: [298.5, 290.5, 301.0, 303.5, 303.0]\n",
+ " Circuit: seca_n11 Mean result: 90.248 Trials: [94.5, 85.5, 88.5, 91.5, 91.5]\n",
+ " Circuit: swap_test_n25 Mean result: 93.166 Trials: [93.5, 93.5, 91.5, 90.0, 97.5]\n",
+ "\n",
+ " Metric: total_runtime\n",
+ " Circuit: ae_n16 Mean result: 0.498 Trials: [0.4448697566986084, 0.5962345600128174, 0.4232339859008789, 0.5744400024414062, 0.45206499099731445]\n",
+ " Circuit: bigadder_n18 Mean result: 0.236 Trials: [0.213545560836792, 0.20427584648132324, 0.21396970748901367, 0.34128570556640625, 0.2067251205444336]\n",
+ " Circuit: knn_n25 Mean result: 0.160 Trials: [0.15566134452819824, 0.1806797981262207, 0.15013504028320312, 0.1592426300048828, 0.15271949768066406]\n",
+ " Circuit: multiplier_n15 Mean result: 0.695 Trials: [0.8022236824035645, 0.7325093746185303, 0.5118999481201172, 0.7301218509674072, 0.6996762752532959]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 1.789 Trials: [1.8008484840393066, 1.7781050205230713, 1.7889549732208252, 1.7809314727783203, 1.7949213981628418]\n",
+ " Circuit: qec9xz_n17 Mean result: 0.104 Trials: [0.09743189811706543, 0.0786588191986084, 0.10518622398376465, 0.12534213066101074, 0.1147909164428711]\n",
+ " Circuit: qft_n18 Mean result: 0.668 Trials: [0.7057349681854248, 0.7008929252624512, 0.5198922157287598, 0.6825127601623535, 0.7332291603088379]\n",
+ " Circuit: qftentangled_n16 Mean result: 0.495 Trials: [0.45197367668151855, 0.591606616973877, 0.42526698112487793, 0.580042839050293, 0.4259822368621826]\n",
+ " Circuit: qpeexact_n16 Mean result: 0.516 Trials: [0.6059279441833496, 0.417954683303833, 0.5668957233428955, 0.42623209953308105, 0.5606374740600586]\n",
+ " Circuit: qram_n20 Mean result: 0.203 Trials: [0.16831660270690918, 0.17808270454406738, 0.17980527877807617, 0.16183781623840332, 0.3248105049133301]\n",
+ " Circuit: sat_n11 Mean result: 0.525 Trials: [0.4876976013183594, 0.5811176300048828, 0.418407678604126, 0.5554184913635254, 0.5824823379516602]\n",
+ " Circuit: seca_n11 Mean result: 0.137 Trials: [0.14063620567321777, 0.13848209381103516, 0.13629698753356934, 0.13498473167419434, 0.13541889190673828]\n",
+ " Circuit: swap_test_n25 Mean result: 0.155 Trials: [0.15360522270202637, 0.16704583168029785, 0.14922618865966797, 0.1491854190826416, 0.15654373168945312]\n",
+ "\n",
+ " Metric: total_swaps\n",
+ " Circuit: ae_n16 Mean result: 54.109 Trials: [52, 60, 53, 51, 55]\n",
+ " Circuit: bigadder_n18 Mean result: 25.961 Trials: [27, 28, 26, 25, 24]\n",
+ " Circuit: knn_n25 Mean result: 13.441 Trials: [10, 13, 15, 15, 15]\n",
+ " Circuit: multiplier_n15 Mean result: 59.601 Trials: [54, 64, 56, 58, 67]\n",
+ " Circuit: portfolioqaoa_n16 Mean result: 156.257 Trials: [146, 165, 155, 162, 154]\n",
+ " Circuit: qec9xz_n17 Mean result: 9.544 Trials: [8, 10, 9, 11, 10]\n",
+ " Circuit: qft_n18 Mean result: 60.541 Trials: [71, 68, 64, 56, 47]\n",
+ " Circuit: qftentangled_n16 Mean result: 60.709 Trials: [67, 59, 60, 57, 61]\n",
+ " Circuit: qpeexact_n16 Mean result: 76.489 Trials: [80, 78, 74, 81, 70]\n",
+ " Circuit: qram_n20 Mean result: 19.037 Trials: [15, 21, 21, 21, 18]\n",
+ " Circuit: sat_n11 Mean result: 58.520 Trials: [58, 53, 60, 61, 61]\n",
+ " Circuit: seca_n11 Mean result: 12.021 Trials: [15, 9, 11, 13, 13]\n",
+ " Circuit: swap_test_n25 Mean result: 13.905 Trials: [14, 14, 13, 12, 17]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(benchmark)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'monodromy_depth': {'average_change': -29.58293759547691,\n",
+ " 'aggregrate_change': -32.08691273036692,\n",
+ " 'best_circuit': 'portfolioqaoa_n16',\n",
+ " 'worst_circuit': 'swap_test_n25'},\n",
+ " 'monodromy_total': {'average_change': -10.253081206893013,\n",
+ " 'aggregrate_change': -12.342163085432158,\n",
+ " 'best_circuit': 'qpeexact_n16',\n",
+ " 'worst_circuit': 'qram_n20'},\n",
+ " 'total_swaps': {'average_change': -59.862211355192535,\n",
+ " 'aggregrate_change': -77.61235151089284,\n",
+ " 'best_circuit': 'seca_n11',\n",
+ " 'worst_circuit': 'qram_n20'},\n",
+ " 'accepted_subs': {'average_change': inf,\n",
+ " 'aggregrate_change': inf,\n",
+ " 'best_circuit': 'qec9xz_n17',\n",
+ " 'worst_circuit': 'qec9xz_n17'},\n",
+ " 'total_runtime': {'average_change': 23912.23202600301,\n",
+ " 'aggregrate_change': 16339.791596060439,\n",
+ " 'best_circuit': 'portfolioqaoa_n16',\n",
+ " 'worst_circuit': 'qec9xz_n17'}}"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "benchmark.summary_statistics(transpilers[0], transpilers[1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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