diff --git a/demos/qiskit_patterns/2_qiskit_patterns.ipynb b/demos/qiskit_patterns/2_qiskit_patterns.ipynb index 1e4729c..827f316 100644 --- a/demos/qiskit_patterns/2_qiskit_patterns.ipynb +++ b/demos/qiskit_patterns/2_qiskit_patterns.ipynb @@ -1343,4 +1343,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/how_tos/qiskit_patterns.ipynb b/how_tos/qiskit_patterns.ipynb deleted file mode 100644 index bab4876..0000000 --- a/how_tos/qiskit_patterns.ipynb +++ /dev/null @@ -1,1186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bc51e7bf-e582-49ba-93f8-035624d56ccf", - "metadata": {}, - "source": [ - "# Quantum Optimization with Qiskit Patterns\n", - "\n", - "In this how-to we will learn about Qiskit Patterns and quantum approximate optimization. Qiskit Patterns define a four-step process for running algorithms on a quantum computer:\n", - "\n", - "\n", - "\"Drawing\"\n", - "\n", - "\n", - " 1. Map classical problem to abstract quantum circuits and operators\n", - " 2. Optimize problem for quantum execution\n", - " 3. Execute on a quantum computer\n", - " 4. Post-process, return result in classical format" - ] - }, - { - "cell_type": "markdown", - "id": "8d3218ca-ce9e-40b4-a041-1b1d09bac8f5", - "metadata": {}, - "source": [ - "We will apply the patterns to the context of **combinatorial optimization** and show how to solve the **Max-Cut** problem using the **Quantum Approximate Optimization Algorithm (QAOA)**, a hybrid (quantum-classical) iterative method. " - ] - }, - { - "cell_type": "markdown", - "id": "1e943b1a-218a-468c-bb63-4269896ebebe", - "metadata": {}, - "source": [ - "# Part 1: (Small-scale) Qiskit Pattern for Optimization" - ] - }, - { - "cell_type": "markdown", - "id": "68fd0b4f-baa4-45dc-9f4c-d9cdff01a651", - "metadata": {}, - "source": [ - "The first part of the session will use a small-scale Max-Cut problem to ilustrate the steps required to solve an optimization problem using a quantum computer." - ] - }, - { - "cell_type": "markdown", - "id": "74b92ba5-c48a-405c-9c4b-04e985a7afbc", - "metadata": {}, - "source": [ - "Max-Cut is a hard to solve optimization problem with applications in clustering, network science, and statistical physics. In Max-Cut, we want to partition the nodes of a graph into to sets such that the number of edges traversed by this cut is maximum.\n", - "\n", - "\"Max-Cut\"/" - ] - }, - { - "cell_type": "markdown", - "id": "e2105a90-027f-44d7-97d1-c2c99373d488", - "metadata": {}, - "source": [ - "The workflow starts with a problem defined as a weighted graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fb223414-87fc-4d6a-933c-db2ea3f37b4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from demo_src.graph import generate_demo_graph, draw_graph\n", - "\n", - "demo_graph = generate_demo_graph()\n", - "draw_graph(demo_graph)" - ] - }, - { - "cell_type": "markdown", - "id": "12a9b2f4-570b-4f05-b77e-be1d261102ca", - "metadata": {}, - "source": [ - "Formally, it's an example of a classical combinatorial optimization problem with the form\n", - "\n", - "\\begin{align}\n", - "\\min_{x\\in \\{0, 1\\}^n}f(x)\n", - "\\end{align}\n", - "\n", - "Where the vector $x$ are the $n$ decision variables that correspond to every node of the graph. In this case, we have $n=5$, and each node can be 0 or 1, included or not included in the cut. The minimum of $f(x)$ in this case will be when the number of edges traversed by the cut are maximal.\n", - "\n", - "As you can see, there is nothing relating to quantum computing yet. We need to reformulate this problem into something that a quantum computer can understand." - ] - }, - { - "cell_type": "markdown", - "id": "a06e4386-d7bd-4914-9baa-36a5cc60e3ab", - "metadata": {}, - "source": [ - "## Step 1. Map the classical inputs to a quantum problem\n", - "\n", - "### Graph → Qubo\n", - "\n", - "The first step of the mapping is a notation change, can express our problem in Quadratic Unconstrained Binary Optimization notation:\n", - "\n", - "\\begin{align}\n", - "\\min_{x\\in \\{0, 1\\}^n}x^T Q x,\n", - "\\end{align}\n", - "\n", - "where $Q$ is a $n\\times n$ matrix of real numbers. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5947608b-f385-44bb-9bca-a7f632504a63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Problem name: Max-cut\n", - "\n", - "Maximize\n", - " -2*x_0*x_1 - 2*x_0*x_2 - 2*x_0*x_4 - 2*x_1*x_2 - 2*x_2*x_3 - 2*x_3*x_4 + 3*x_0\n", - " + 2*x_1 + 3*x_2 + 2*x_3 + 2*x_4\n", - "\n", - "Subject to\n", - " No constraints\n", - "\n", - " Binary variables (5)\n", - " x_0 x_1 x_2 x_3 x_4\n", - "\n" - ] - } - ], - "source": [ - "from demo_src.map import map_graph_to_qubo\n", - "\n", - "qubo = map_graph_to_qubo(demo_graph)\n", - "print(qubo.prettyprint())" - ] - }, - { - "cell_type": "markdown", - "id": "a5b9e551-38a1-4543-b9f1-caaefb0ef3a9", - "metadata": {}, - "source": [ - "### QUBO → Ising Hamiltonian\n", - "\n", - "To start, we will convert the binary variables $x_i$ to variables $z_i\\in\\{-1, 1\\}$ by doing\n", - "\n", - "\\begin{align}\n", - "x_i = \\frac{1-z_i}{2}.\n", - "\\end{align}\n", - "\n", - "Here, for example, we see that if $x_i$ is $0$ then $z_i$ is $1$. When we substitute the $x_i$'s for the $z_i$'s in the QUBO above, we obtain the equivalent formulations for our optimization task\n", - "\n", - "\\begin{align}\n", - "\\min_{x\\in\\{0,1\\}^n} x^TQx\\Longleftrightarrow \\min_{z\\in\\{-1,1\\}^n}z^TQz + b^Tz\n", - "\\end{align}\n", - "\n", - "The details of the computation are shown in Appendix A below. Here, $b$ depends on $Q$. Note that to obtain $z^TQz + b^Tz$ we dropped an irrelevant factor of 1/4 and a constant offset of $n^2$ which do not play a role in the optimization. Now, to obtain a quantum formulation of the problem we promot the $z_i$ variables to a Pauli $Z$ matrix, i.e., a $2\\times 2$ matrix of the form\n", - "\n", - "\\begin{align}\n", - "Z_i = \\begin{pmatrix}1 & 0 \\\\ 0 & -1\\end{pmatrix}.\n", - "\\end{align}\n", - "\n", - "When we substitute these matrices in the QUBO above we obtain the following Hamiltonian\n", - "\n", - "\\begin{align}\n", - "H_C=\\sum_{ij}Q_{ij}Z_iZ_j + \\sum_i b_iZ_i.\n", - "\\end{align}\n", - "\n", - "We refer to this Hamiltonian as the **cost function Hamiltonian**. It has the property that its gound state corresponds to the solution that **minimizes the cost function $f(x)$**.\n", - "Therefore, to solve our optimization problem we now need to prepare the ground state of $H_C$ (or a state with a high overlap with it) on the quantum computer. Then, sampling from this state will, with a high probability, yield the solution to $min f(x)$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "01a2d8eb-b63b-40bc-93c5-0b547c06b194", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Offset: -3.0\n", - "Cost Function Hamiltonian: SparsePauliOp(['IIIZZ', 'IIZIZ', 'ZIIIZ', 'IIZZI', 'IZZII', 'ZZIII'],\n", - " coeffs=[0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j])\n" - ] - } - ], - "source": [ - "from demo_src.map import map_qubo_to_ising\n", - "\n", - "cost_hamiltonian, offset = map_qubo_to_ising(qubo)\n", - "print(\"Offset:\", offset)\n", - "print(\"Cost Function Hamiltonian:\", cost_hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "33f71b0d-4a2a-4082-8c1a-ce9d2b769048", - "metadata": {}, - "source": [ - "### Ising Hamiltonian → Quantum Circuit" - ] - }, - { - "cell_type": "markdown", - "id": "00431c46-30c2-40f9-99df-40baf8da98f6", - "metadata": {}, - "source": [ - "The Hamiltonian $H_c$ obtained from step 1 contains the quantum definition of our problem. We will now create a quantum circuit that will help us *sample* good solutions form the quantum computer. The QAOA is inspired from quantum annealing and proceeds by applying alternating layers of operators in the quantum cirucit.\n", - "\n", - "[image]\n", - "\n", - "Losely speaking, the idea is to start in the ground state of a known system, $H^{\\otimes n}|0\\rangle$ above, and then steer the system into the ground state of the cost operator that we are interested in. This is done by applying the operators $\\exp\\{-i\\gamma_k H_C\\}$ and $\\exp\\{-i\\beta_k H_m\\}$ with the right angles $\\gamma_1,...,\\gamma_p$ and $\\beta_1,...,\\beta_p~$. \n", - "\n", - "We can generate a quantum circuit that is **parametrized** by these angles. In this case we will try an example with 1 QAOA layer that contains two parameters: $\\gamma_0$ and $\\beta_0$." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7bd8c6d4-f40f-4a11-a440-0b26d9021b53", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from demo_src.map import map_ising_to_circuit\n", - "\n", - "circuit = map_ising_to_circuit(cost_hamiltonian, num_layers=1)\n", - "circuit.draw('mpl')" - ] - }, - { - "cell_type": "markdown", - "id": "82f70daa-ff68-447a-8064-8b7df7a646cf", - "metadata": {}, - "source": [ - "## Step 2. Optimize problem for quantum execution" - ] - }, - { - "cell_type": "markdown", - "id": "c08be444-e3ed-4178-a10b-414069b1b411", - "metadata": {}, - "source": [ - "The circuit above contains a series of abstractions useful to think about quantum algorithms, but not possible to run in the hardware. To be able to run it in a quantum chip, the circuit needs to undergo a series of operations that make up the **transpilation** or **circuit optimization** step of the pattern.\n", - "\n", - "The Qiskit library offers a series of **transpilation passes** that cater to a wide range of circuit transformations. We don't only want to get a circuit, but we want to make sure that the circuit is **optimized** for our purpose. Transpilation may involve many steps, such as:\n", - "\n", - "* **Intiall mapping** of the qubits in the circuit (i.e. decision variables) to physical qubits on the device.\n", - "* **Unrolling** of the instructions in the quantum circuit to the hardware native instructions that the backend understands.\n", - "* **Routing** of any qubits in the circuit that interact to physical qubits that are adjacent with one another.\n", - "* **Error supression** by adding single-qubit gates to supress noise with dynamical decoupling.\n", - "* ...\n", - "\n", - "In this example, we have encapsulated a series of transpilation passes in the `optimize_circuit` function. The function takes a circuit and a backend (device) and returns an optimized circuit ready to run in the corresponding device:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "95cd3eed-0348-4373-b664-16a65d42f1e7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# IBM Quantum devices are named after cities\n", - "# For the purpose of the first part of this demo, we will\n", - "# use a simulated device from the \"fake_provider\"\n", - "from qiskit_ibm_runtime.fake_provider import FakeVigoV2\n", - "backend = FakeVigoV2()\n", - "\n", - "from demo_src.transpile import optimize_circuit\n", - "opt_circuit = optimize_circuit(circuit, backend)\n", - "opt_circuit.draw('mpl', fold=False)" - ] - }, - { - "cell_type": "markdown", - "id": "4e75cad7-f599-4937-b5fe-f4d01f53423c", - "metadata": {}, - "source": [ - "## Step 3. Execute using Qiskit Runtime primitives" - ] - }, - { - "cell_type": "markdown", - "id": "9b99ce67-f121-4244-b62a-536be38fea86", - "metadata": {}, - "source": [ - "In the general QAOA workflow, the optimal QAOA parameters are found in an iterative optimization loop, where we run a series of circuit evaluations and use a classical optimizer to find the optimal $\\beta_k$ and $\\gamma_k$ parameters. In this demo, we will not optimize these parameters, we will simply assume that someone has done this for us. Note that finding such parameters is a research field in itself.\n", - "\n", - "Once the optimal parameters are defined, we want to perform one final sampling with the resulting circuit to find our candidate solution.\n", - "\n", - "This means preparing a quantum state $\\psi$ in the computer and then measuring it. A measurement will collapse the state into a single computational basis state, for example `010101110000...` which corresponds to a candidate solution $x$ to our initial optimization problem ($\\max f(x)$ or $\\min f(x)$ depending on the task).\n", - "\n", - "The execution is done through the **cloud** using the **Qiskit IBM Runtime service**." - ] - }, - { - "cell_type": "markdown", - "id": "00b2b0f1-9bad-4ad3-b93e-5cbf40395dbf", - "metadata": {}, - "source": [ - "### Define circuit with optimal parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "afa5747f-44dc-4e41-a875-7b6f896f13e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "optimal_gamma = 5.11350346\n", - "optimal_beta = 5.52673212\n", - "\n", - "candidate_circuit = opt_circuit.assign_parameters([optimal_gamma, optimal_beta])\n", - "candidate_circuit.draw('mpl', fold=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b867f1b0-7196-4d34-9b28-e3fb1de8221c", - "metadata": {}, - "source": [ - "### Define backend and execution primitive\n", - "\n", - "To interact with a IBM backends we use the **Qiskit Runtime Primitives**. There are two primitives: Sampler and Estimator, and the choice of primitive depends on the task that we want to run on the quantum computer. We are interested in drawing samples from the quantum computer, so we will use the **Sampler**. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ff947109-cddc-4d3c-9119-2c729df73115", - "metadata": {}, - "outputs": [], - "source": [ - "# For the purpose of the first part of this demo, we will\n", - "# use a simulated device from the \"fake_provider\"\n", - "from qiskit_ibm_runtime.fake_provider import FakeVigoV2\n", - "from qiskit.primitives import BackendSampler\n", - "\n", - "backend = FakeVigoV2()\n", - "sampler = BackendSampler(backend=backend)" - ] - }, - { - "cell_type": "markdown", - "id": "ebe288e7-ce87-4b6d-949c-041db09c7c47", - "metadata": {}, - "source": [ - "### Run" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2989e76e-4296-4dd8-b065-2b8fced064cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{11: 0.1059, 13: 0.0403, 12: 0.0271, 22: 0.1068, 18: 0.0435, 21: 0.0405, 20: 0.1031, 10: 0.0413, 16: 0.0125, 15: 0.0117, 26: 0.0684, 29: 0.0064, 17: 0.0304, 3: 0.0131, 23: 0.0116, 9: 0.1026, 5: 0.0717, 6: 0.0112, 14: 0.0289, 19: 0.0302, 30: 0.0083, 25: 0.0104, 1: 0.0094, 28: 0.012, 7: 0.0024, 24: 0.0022, 4: 0.0081, 31: 0.0067, 2: 0.0063, 8: 0.0106, 0: 0.006, 27: 0.0104}\n" - ] - } - ], - "source": [ - "final_distribution = sampler.run(candidate_circuit, shots=int(1e4)).result().quasi_dists[0]\n", - "print(final_distribution)" - ] - }, - { - "cell_type": "markdown", - "id": "dace5fed-5555-4f1c-9109-7f5a31832d04", - "metadata": {}, - "source": [ - "## Step 4. Post-process, return result in classical format" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d4f7fc70-883f-4b6b-8e92-2fc4afbbea46", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result bitstring: [0 1 1 0 1]\n" - ] - } - ], - "source": [ - "from demo_src.post import sample_most_likely\n", - "\n", - "best_result = sample_most_likely(final_distribution, len(demo_graph))\n", - "print(\"Result bitstring:\", best_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "650875e9-adbc-43bd-9505-556be2566278", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from demo_src.post import plot_distribution\n", - "\n", - "plot_distribution(final_distribution)" - ] - }, - { - "cell_type": "markdown", - "id": "207443f2-34d9-424a-a6d7-44707ef1488b", - "metadata": {}, - "source": [ - "### Visualize best cut" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "33135970-8bc4-4fb2-ab87-08726a432ce4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from demo_src.post import plot_result\n", - "\n", - "plot_result(demo_graph, best_result)" - ] - }, - { - "cell_type": "markdown", - "id": "2e2a89de-cef3-46ea-b201-cf931b65dfea", - "metadata": {}, - "source": [ - "---\n", - "\n", - "# Part 2: Let's scale it up and run!" - ] - }, - { - "cell_type": "markdown", - "id": "9a9a69e0-aa07-40aa-91ac-ef6740f2677f", - "metadata": {}, - "source": [ - "Let's try to solve Max-Cut on a 127-node weighted graph, with both positive (green) and negative (red) weights.\n", - "\n", - "
\n", - "\n", - "
\n", - "\n", - "We have precomputed steps 1-3 for you, so you get to run step 4 on a real quantum device." - ] - }, - { - "cell_type": "markdown", - "id": "31bb3bc1-f19e-4553-9e93-a89e92ea5469", - "metadata": {}, - "source": [ - "## Step 1. Map the classical inputs to a quantum problem" - ] - }, - { - "cell_type": "markdown", - "id": "e9cf2c59-1f55-432c-9243-d1f94d50d0ad", - "metadata": {}, - "source": [ - "### Graph → QUBO\n", - "\n", - "The cell below shows you what the LP file that stores the quadratic program for this problem looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "01e39c4e-5c44-44c7-9844-f7a59596998b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\ This file has been generated by DOcplex\n", - "\\ ENCODING=ISO-8859-1\n", - "\\Problem name: CPLEX\n", - "\n", - "Minimize\n", - " obj: [ - 8 x0^2 + 8 x0*x1 + 8 x0*x13 - 8 x1^2 + 8 x1*x2 - 8 x2*x3 + 8 x3*x4\n", - " - 4 x4^2 - 8 x4*x5 + 8 x4*x14 + 8 x5*x6 - 8 x6^2 + 8 x6*x7 - 8 x7^2\n", - " + 8 x7*x8 - 12 x8^2 + 8 x8*x9 + 8 x8*x15 - 8 x9*x10 + 8 x10^2 - 8 x10*x11\n", - " + 8 x11*x12 - 8 x12*x16 - 8 x13*x17 - 8 x14*x21 - 8 x15*x25 + 8 x16^2\n", - " - 8 x16*x29 + 8 x17*x18 - 8 x18^2 + 8 x18*x19 + 4 x19^2 - 8 x19*x20\n", - " - 8 x19*x32 + 8 x20*x21 - 4 x21^2 + 8 x21*x22 - 8 x22^2 + 8 x22*x23\n", - " - 12 x23^2 + 8 x23*x24 + 8 x23*x33 - 8 x24^2 + 8 x24*x25 + 4 x25^2\n", - " - 8 x25*x26 + 8 x26*x27 - 4 x27^2 + 8 x27*x28 - 8 x27*x34 - 8 x28*x29\n", - " + 12 x29^2 - 8 x29*x30 + 8 x30^2 - 8 x30*x31 + 8 x31^2 - 8 x31*x35\n", - " + 8 x32*x38 - 8 x33*x42 + 8 x34^2 - 8 x34*x46 + 8 x35^2 - 8 x35*x50\n", - " - 8 x36^2 + 8 x36*x37 + 8 x36*x51 - 8 x37*x38 - 4 x38^2 + 8 x38*x39\n", - " - 8 x39*x40 + 12 x40^2 - 8 x40*x41 - 8 x40*x52 + 8 x41^2 - 8 x41*x42\n", - " + 12 x42^2 - 8 x42*x43 + 8 x43*x44 - 4 x44^2 + 8 x44*x45 - 8 x44*x53\n", - " - 8 x45*x46 + 12 x46^2 - 8 x46*x47 + 8 x47*x48 + 4 x48^2 - 8 x48*x49\n", - " - 8 x48*x54 + 8 x49^2 - 8 x49*x50 + 8 x50^2 - 8 x51^2 + 8 x51*x55\n", - " + 8 x52*x59 + 8 x53^2 - 8 x53*x63 + 8 x54*x67 - 8 x55^2 + 8 x55*x56\n", - " - 8 x56^2 + 8 x56*x57 - 4 x57^2 - 8 x57*x58 + 8 x57*x70 + 8 x58^2\n", - " - 8 x58*x59 - 4 x59^2 + 8 x59*x60 - 8 x60^2 + 8 x60*x61 - 4 x61^2\n", - " - 8 x61*x62 + 8 x61*x71 + 8 x62^2 - 8 x62*x63 + 4 x63^2 + 8 x63*x64\n", - " - 8 x64^2 + 8 x64*x65 + 4 x65^2 - 8 x65*x66 - 8 x65*x72 + 8 x66*x67\n", - " - 12 x67^2 + 8 x67*x68 - 8 x68^2 + 8 x68*x69 - 8 x69^2 + 8 x69*x73\n", - " - 8 x70^2 + 8 x70*x76 - 8 x71^2 + 8 x71*x80 + 8 x72*x84 - 8 x73*x88\n", - " - 8 x74*x75 + 8 x74*x89 + 8 x75*x76 - 12 x76^2 + 8 x76*x77 - 8 x77*x78\n", - " + 4 x78^2 + 8 x78*x79 - 8 x78*x90 - 8 x79^2 + 8 x79*x80 - 4 x80^2\n", - " - 8 x80*x81 + 8 x81^2 - 8 x81*x82 - 4 x82^2 + 8 x82*x83 + 8 x82*x91\n", - " - 8 x83^2 + 8 x83*x84 - 12 x84^2 + 8 x84*x85 - 8 x85^2 + 8 x85*x86\n", - " - 4 x86^2 + 8 x86*x87 - 8 x86*x92 - 8 x87^2 + 8 x87*x88 - 8 x89*x93\n", - " + 8 x90^2 - 8 x90*x97 - 8 x91*x101 + 8 x92^2 - 8 x92*x105 + 8 x93^2\n", - " - 8 x93*x94 + 8 x94*x95 - 12 x95^2 + 8 x95*x96 + 8 x95*x108 - 8 x96*x97\n", - " + 4 x97^2 + 8 x97*x98 - 8 x98*x99 + 12 x99^2 - 8 x99*x100 - 8 x99*x109\n", - " + 8 x100*x101 - 4 x101^2 + 8 x101*x102 - 8 x102^2 + 8 x102*x103 + 4 x103^2\n", - " - 8 x103*x104 - 8 x103*x110 + 8 x104*x105 + 4 x105^2 - 8 x105*x106\n", - " + 8 x106^2 - 8 x106*x107 + 8 x107^2 - 8 x107*x111 - 8 x108*x112\n", - " + 8 x109*x116 + 8 x110^2 - 8 x110*x120 + 8 x111*x124 + 8 x112^2\n", - " - 8 x112*x113 + 8 x113*x114 - 8 x114*x115 + 8 x115^2 - 8 x115*x116\n", - " - 4 x116^2 + 8 x116*x117 - 8 x117^2 + 8 x117*x118 - 8 x118*x119\n", - " + 8 x119*x120 + 4 x120^2 - 8 x120*x121 + 8 x121^2 - 8 x121*x122 + 8 x122^2\n", - " - 8 x122*x123 + 8 x123*x124 - 8 x124^2 ]/2 + 4\n", - "Subject To\n", - "\n", - "Bounds\n", - " 0 <= x0 <= 1\n", - " 0 <= x1 <= 1\n", - " 0 <= x2 <= 1\n", - " 0 <= x3 <= 1\n", - " 0 <= x4 <= 1\n", - " 0 <= x5 <= 1\n", - " 0 <= x6 <= 1\n", - " 0 <= x7 <= 1\n", - " 0 <= x8 <= 1\n", - " 0 <= x9 <= 1\n", - " 0 <= x10 <= 1\n", - " 0 <= x11 <= 1\n", - " 0 <= x12 <= 1\n", - " 0 <= x13 <= 1\n", - " 0 <= x14 <= 1\n", - " 0 <= x15 <= 1\n", - " 0 <= x16 <= 1\n", - " 0 <= x17 <= 1\n", - " 0 <= x18 <= 1\n", - " 0 <= x19 <= 1\n", - " 0 <= x20 <= 1\n", - " 0 <= x21 <= 1\n", - " 0 <= x22 <= 1\n", - " 0 <= x23 <= 1\n", - " 0 <= x24 <= 1\n", - " 0 <= x25 <= 1\n", - " 0 <= x26 <= 1\n", - " 0 <= x27 <= 1\n", - " 0 <= x28 <= 1\n", - " 0 <= x29 <= 1\n", - " 0 <= x30 <= 1\n", - " 0 <= x31 <= 1\n", - " 0 <= x32 <= 1\n", - " 0 <= x33 <= 1\n", - " 0 <= x34 <= 1\n", - " 0 <= x35 <= 1\n", - " 0 <= x36 <= 1\n", - " 0 <= x37 <= 1\n", - " 0 <= x38 <= 1\n", - " 0 <= x39 <= 1\n", - " 0 <= x40 <= 1\n", - " 0 <= x41 <= 1\n", - " 0 <= x42 <= 1\n", - " 0 <= x43 <= 1\n", - " 0 <= x44 <= 1\n", - " 0 <= x45 <= 1\n", - " 0 <= x46 <= 1\n", - " 0 <= x47 <= 1\n", - " 0 <= x48 <= 1\n", - " 0 <= x49 <= 1\n", - " 0 <= x50 <= 1\n", - " 0 <= x51 <= 1\n", - " 0 <= x52 <= 1\n", - " 0 <= x53 <= 1\n", - " 0 <= x54 <= 1\n", - " 0 <= x55 <= 1\n", - " 0 <= x56 <= 1\n", - " 0 <= x57 <= 1\n", - " 0 <= x58 <= 1\n", - " 0 <= x59 <= 1\n", - " 0 <= x60 <= 1\n", - " 0 <= x61 <= 1\n", - " 0 <= x62 <= 1\n", - " 0 <= x63 <= 1\n", - " 0 <= x64 <= 1\n", - " 0 <= x65 <= 1\n", - " 0 <= x66 <= 1\n", - " 0 <= x67 <= 1\n", - " 0 <= x68 <= 1\n", - " 0 <= x69 <= 1\n", - " 0 <= x70 <= 1\n", - " 0 <= x71 <= 1\n", - " 0 <= x72 <= 1\n", - " 0 <= x73 <= 1\n", - " 0 <= x74 <= 1\n", - " 0 <= x75 <= 1\n", - " 0 <= x76 <= 1\n", - " 0 <= x77 <= 1\n", - " 0 <= x78 <= 1\n", - " 0 <= x79 <= 1\n", - " 0 <= x80 <= 1\n", - " 0 <= x81 <= 1\n", - " 0 <= x82 <= 1\n", - " 0 <= x83 <= 1\n", - " 0 <= x84 <= 1\n", - " 0 <= x85 <= 1\n", - " 0 <= x86 <= 1\n", - " 0 <= x87 <= 1\n", - " 0 <= x88 <= 1\n", - " 0 <= x89 <= 1\n", - " 0 <= x90 <= 1\n", - " 0 <= x91 <= 1\n", - " 0 <= x92 <= 1\n", - " 0 <= x93 <= 1\n", - " 0 <= x94 <= 1\n", - " 0 <= x95 <= 1\n", - " 0 <= x96 <= 1\n", - " 0 <= x97 <= 1\n", - " 0 <= x98 <= 1\n", - " 0 <= x99 <= 1\n", - " 0 <= x100 <= 1\n", - " 0 <= x101 <= 1\n", - " 0 <= x102 <= 1\n", - " 0 <= x103 <= 1\n", - " 0 <= x104 <= 1\n", - " 0 <= x105 <= 1\n", - " 0 <= x106 <= 1\n", - " 0 <= x107 <= 1\n", - " 0 <= x108 <= 1\n", - " 0 <= x109 <= 1\n", - " 0 <= x110 <= 1\n", - " 0 <= x111 <= 1\n", - " 0 <= x112 <= 1\n", - " 0 <= x113 <= 1\n", - " 0 <= x114 <= 1\n", - " 0 <= x115 <= 1\n", - " 0 <= x116 <= 1\n", - " 0 <= x117 <= 1\n", - " 0 <= x118 <= 1\n", - " 0 <= x119 <= 1\n", - " 0 <= x120 <= 1\n", - " 0 <= x121 <= 1\n", - " 0 <= x122 <= 1\n", - " 0 <= x123 <= 1\n", - " 0 <= x124 <= 1\n", - "\n", - "Binaries\n", - " x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21\n", - " x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41\n", - " x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61\n", - " x62 x63 x64 x65 x66 x67 x68 x69 x70 x71 x72 x73 x74 x75 x76 x77 x78 x79 x80 x81\n", - " x82 x83 x84 x85 x86 x87 x88 x89 x90 x91 x92 x93 x94 x95 x96 x97 x98 x99 x100\n", - " x101 x102 x103 x104 x105 x106 x107 x108 x109 x110 x111 x112 x113 x114 x115 x116\n", - " x117 x118 x119 x120 x121 x122 x123 x124\n", - "End\n", - "\n" - ] - } - ], - "source": [ - "with open(\"data/125node_example.lp\", \"r\") as file:\n", - " problem = file.read()\n", - "print(problem)" - ] - }, - { - "cell_type": "markdown", - "id": "af34ecad-5132-4ea3-88f5-1bdacc1e9f07", - "metadata": {}, - "source": [ - "### QUBO → Hamiltonian\n", - "\n", - "Below we show the first 10 Pauli terms in the cost-function Hamiltonian. They look similar, but pay attention to the `Z` and the coefficients which correspond to the weight of the edges in the graph for which we want to find the maximum cut." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d1715aab-b84e-46d0-98d7-22f7cf68befd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII, -1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIZIIII, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII, -1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII, 1.0\n", - "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII, 1.0\n" - ] - } - ], - "source": [ - "with open(\"data/125node_example_ising.txt\") as input_file:\n", - " for _ in range(10):\n", - " print(str(next(input_file)).replace(\"\\n\", \"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "cf31d488-d672-4c91-8a80-867273502396", - "metadata": {}, - "source": [ - "## Step 2. Optimize problem for quantum execution\n", - "\n", - "We have prepared the optimized circuits for you. These are stored in LP files that we can import with Qiskit's serializer named QPY." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4247e41e-b27b-400b-9212-c30bc7aecc1e", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit import qpy # QPY is the circuit serializer in Qiskit.\n", - "\n", - "# Load the circuits\n", - "team = 1 # Fill in your team here, either 1 or 2\n", - "if team == 1:\n", - " backend_type = \"eagle\"\n", - "elif team == 2:\n", - " backend_type == \"heron\"\n", - "else:\n", - " raise ValueError(\"team should be 1 or 2.\")\n", - "\n", - "# Depth zero-circuit\n", - "with open(f\"data/125node_{backend_type}_depth_zero.qpy\", \"rb\") as fd:\n", - " depth_zero_circuit = qpy.load(fd)[0]\n", - "\n", - "# Depth one-circuit\n", - "with open(f\"data/125node_{backend_type}_depth_one.qpy\", \"rb\") as fd:\n", - " depth_one_circuit = qpy.load(fd)[0]" - ] - }, - { - "cell_type": "markdown", - "id": "077936f8-6dbe-457d-aceb-8662cd1be924", - "metadata": {}, - "source": [ - "You can print the circuit by uncommenting the line below. However, note that the output for utility-scale experiments is typically large." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "94e58ec2-856b-4d83-a976-bb5ad4aafc29", - "metadata": {}, - "outputs": [], - "source": [ - "#print(depth_one_circuit)" - ] - }, - { - "cell_type": "markdown", - "id": "8a8e65f0-9089-4237-b833-6f99da859ce2", - "metadata": {}, - "source": [ - "## Step 3. Execute using Qiskit Runtime Primitives\n", - "\n", - "To run QAOA we need to know the optimal parameters $\\gamma_k$ and $\\beta_k$ to put in the variational circuit. We will not optimize these parameters here. We will simply assume that someone has done this for us. The optimal parameters to use are\n", - "\n", - "* Depth-zero QAOA: none\n", - "* Depth-one optimal (gamma, beta): (0.3792, 0.3792)\n", - "\n", - "Note that findin gsuch parameters is a research field in itself." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "eee717c7-6aed-47f2-9e20-04e7886cf72b", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import QiskitRuntimeService\n", - "\n", - "# Backend for team 1\n", - "service = QiskitRuntimeService(channel='ibm_quantum')\n", - "backend = service.get_backend('ibm_sherbrooke')\n", - "\n", - "# Backend for team 2\n", - "service = QiskitRuntimeService(channel='ibm_quantum')\n", - "backend = service.get_backend('ibm_torino')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "39cd3a9d-e63a-45a9-afe6-237ca1fbda22", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import Sampler, Options\n", - "\n", - "# Since we have already done the transpilation we can skip it.\n", - "options = Options()\n", - "options.transpilation.skip_transpilation = True\n", - "\n", - "sampler = Sampler(backend=backend, options=options)" - ] - }, - { - "cell_type": "markdown", - "id": "24b10973-5d7c-4536-842c-b87049b6fc7f", - "metadata": {}, - "source": [ - "### Bind the parameters for the depth-one ansatz\n", - "\n", - "As mentioned above, the depth-one QAOA circuit has two parameters $\\gamma$ and $\\beta$ that need to be bound before we can run the circuits." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e6a4f88c-c2a4-48c1-ad14-3e5ed58031e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ParameterView([Parameter(β1), Parameter(γ1)])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "depth_one_circuit.parameters" - ] - }, - { - "cell_type": "markdown", - "id": "bb0702af-0581-4aa5-89fa-a3afcb0c20d2", - "metadata": {}, - "source": [ - "We create a circuit with bound parameter values below." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "12f9dc1f-bfcb-44e7-8304-c249e4f1fa53", - "metadata": {}, - "outputs": [], - "source": [ - "depth_one_bound_circuit = depth_one_circuit.assign_parameters([0.3927, 0.3927], inplace=False)" - ] - }, - { - "cell_type": "markdown", - "id": "5e11ce39-a046-4f65-a8e6-bc9ca123eb9a", - "metadata": {}, - "source": [ - "### Sample candidate solution from backend" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "db601a74-afb7-47ac-b6ad-cfb8e77f9594", - "metadata": {}, - "outputs": [], - "source": [ - "sampler_job = sampler.run([depth_one_bound_circuit, depth_zero_circuit])" - ] - }, - { - "cell_type": "markdown", - "id": "4ac5aab1-e82a-4321-8130-ff7b30e71af0", - "metadata": {}, - "source": [ - "Each call to the sampler returns a JobId that we can use to retrive the results when they are ready." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "89dcca9f-3b53-4973-97bd-fae1da61738f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'cqc945rvxaq00083e690'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampler_job.job_id()" - ] - }, - { - "cell_type": "markdown", - "id": "364f0f06-4a28-455b-b893-3883576e5a7c", - "metadata": {}, - "source": [ - "In addition, we can check the status of our jobs to see if they are finished." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1f01dc43-d91c-4b9c-a542-aeab3eda8ec4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampler_job.status()" - ] - }, - { - "cell_type": "markdown", - "id": "0b853887-d2eb-499a-b1b8-c55d6c144313", - "metadata": {}, - "source": [ - "Now, we will locally save the samples that the backend returned so that we can share them and later analyze them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd479ac2-1bde-4f6f-83a0-bf96094b1d25", - "metadata": {}, - "outputs": [], - "source": [ - "from demo_src.run import save_result\n", - "\n", - "save_result(sampler_job.result(), backend_type, path=\"sampler_data\")" - ] - }, - { - "cell_type": "markdown", - "id": "50ad3918-5cc4-488a-b323-6743ebfc7400", - "metadata": {}, - "source": [ - "## Step 4. Post-process, return result in classical format\n", - "\n", - "Now, we need to compute the objective value of each sample that we measured on the quantum computer. The best one will be the solution returned by the quantum computer." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6fa05a0d-782e-499d-9b96-e8657b6d52fd", - "metadata": {}, - "outputs": [], - "source": [ - "from demo_src.post import load_data, samples_to_objective_values, load_qp, plot_cdf\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a4b43732-da13-4732-813f-db1b9534f17a", - "metadata": {}, - "outputs": [], - "source": [ - "qp, max_cut, min_cut = load_qp()" - ] - }, - { - "cell_type": "markdown", - "id": "00883eef-7a32-414d-b99c-751c25165e55", - "metadata": {}, - "source": [ - "The following cell will load all the data that we have gathered. Note that we give it `qp`, the quadratic program of the MaxCut optimization problem, so that `load_data` can directly convert the samples to the value of the objective function." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8a264ca3-1657-4788-a3bd-7e878c70a7de", - "metadata": {}, - "outputs": [], - "source": [ - "depth_one_heron, depth_zero_heron, depth_one_eagle, depth_zero_eagle = load_data(qp)" - ] - }, - { - "cell_type": "markdown", - "id": "351dee24-6eb9-4589-8a16-b0c50deb231d", - "metadata": {}, - "source": [ - "Now, in the last cell we will display the cumulative distribution function of the sampled solutions for depth-zero QAOA (blue line) and depth-one QAOA (orange line). The dashed orange line displays the best sampled solution from the quantum computer. The black dashed line is the optimal solution. We can see that there is still a gap between the best sampled solution and the optimal solution. To get better solutions we would need to use deeper QAOA circuits. But be careful, this might result in more noise." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "822487d7-da2a-44e2-9524-9285b50d4a77", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "plot_cdf(depth_zero_eagle, ax, \"Depth-zero\")\n", - "plot_cdf(depth_one_eagle, ax, \"Depth-one\")\n", - "ax.vlines(max_cut, 0, 1, \"k\", linestyle=\"--\")\n", - "ax.vlines(max(list(depth_one_eagle.keys())), 0, 1, \"C1\", linestyle=\"--\")\n", - "approx = 100 * (max(list(depth_one_eagle.keys())) - min_cut) / (max_cut - min_cut)\n", - "ax.set_title(f\"Approximation ratio {approx}%\")\n", - "ax.set_xlabel(\"Objective function value\")\n", - "ax.set_ylabel(\"Cumulative distribution function\")\n", - "ax.legend(loc=2);" - ] - }, - { - "cell_type": "markdown", - "id": "69ebc85b-6a29-4671-8d16-1ac97f089607", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "In this tutorial we present the Qiskit patterns and how to solve an optimization problem with a quantum computer. We demonstrate this at utility-scale, i.e., using on the order of 100 qubits. Currently, quantum computers do not outperform classical computers for combinatorial optimization. They are currently too noisy. However, the hardware is getting better and better and new algorithms for quantum computers are always being developped. This is also where the era of utility will be important. Indeed, much of the research working on quantum heuristics for combinatorial optimization are tested with classical simulations which only allow for a small number of qubits, typically aroud 20 qubits. Now, with larger qubit counts and devices with less noise we will be able to start benchmarking these quantum heuristics at large problem sizes on actual quantum hardware." - ] - }, - { - "cell_type": "markdown", - "id": "9b6bd37f-973a-48f2-b0df-b5d9172d09b9", - "metadata": {}, - "source": [ - "## Appendix A: Reformulation in spin variables\n", - "\n", - "Here, we rewrite the QUBO $x^TQx$ in terms of spin-variables $x_i=(1-z_i)/2$.\n", - "\\begin{align}\n", - "x^TQx=\\sum_{ij}Q_{ij}x_ix_j=\\frac{1}{4}\\sum_{ij}Q_{ij}(1-z_i)(1-z_j)=\\frac{1}{4}\\sum_{ij}Q_{ij}z_iz_j-\\frac{1}{4}\\sum_{ij}(Q_{ij}+Q_{ji})z_i + \\frac{n^2}{4}.\n", - "\\end{align}\n", - "If we write $b_i=-\\sum_{j}(Q_{ij}+Q_{ji})$ and remove the prefactor and the constant $n^2$ term we arrive at the two equivalent formulations of the same optimization problem\n", - "\\begin{align}\n", - "\\max_{x\\in\\{0,1\\}^n} x^TQx\\Longleftrightarrow \\max_{z\\in\\{-1,1\\}^n}z^TQz + b^Tz\n", - "\\end{align}" - ] - }, - { - "cell_type": "markdown", - "id": "d1e0a59c-ba1a-4310-8eb7-cd969e013daa", - "metadata": {}, - "source": [ - "## Appendix B: Quantum notation\n", - "\n", - "The $Z$ matrices are imbedded in the quantum computer's computational space, i.e., a Hilbert space of size $2^n\\times 2^n$. Therefore, you should understand terms such as $Z_iZ_j$ as the tensor product $Z_i\\otimes Z_j$ imbedded in the $2^n\\times 2^n$ Hilbert space. For example, in a problem with five decision variables the term $Z_1Z_3$ is understood to mean $I\\otimes Z_3\\otimes I\\otimes Z_1\\otimes I$ where $I$ is the $2\\times 2$ identity matrix." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}