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Extend qtensor implementations for vertex cover, variants #48

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1853715
Extend qtensor functionality for vertex cover problems
pangara Feb 23, 2024
f6bd40c
Make consistent with dev branch changes
pangara Feb 23, 2024
7f24b7a
add example notebook for vertex cover and qaoa
pangara Feb 23, 2024
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332 changes: 332 additions & 0 deletions examples/qtensor-vertex-cover.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e54d32e8-4528-4c68-9064-bf60c74673fe",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import networkx as nx\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a211498-211b-4a87-9762-d6c9f6652c4e",
"metadata": {},
"outputs": [],
"source": [
"sys.path.append(\"../\")"
]
},
{
"cell_type": "markdown",
"id": "89452af5-c62b-4e46-bd3a-840adc78ab7c",
"metadata": {},
"source": [
"## Sanity check"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b757515-99d8-44e5-8c53-c370a3b1a331",
"metadata": {},
"outputs": [],
"source": [
"from qtensor import QAOA_energy\n",
"\n",
"G = nx.random_regular_graph(3, 10)\n",
"gamma, beta = [np.pi/3], [np.pi/2]\n",
"\n",
"E = QAOA_energy(G, gamma, beta)"
]
},
{
"cell_type": "markdown",
"id": "5c466f06-511a-483b-8f15-e0aad686bcec",
"metadata": {},
"source": [
"## Vertex Cover: Qiskit"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "508eb5b9-782b-4213-a797-5116d847d6b8",
"metadata": {},
"outputs": [],
"source": [
"import qiskit\n",
"qiskit.__qiskit_version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3f64065-7a1f-4183-a887-813a235958ee",
"metadata": {},
"outputs": [],
"source": [
"# Qtensor branch: dev\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import networkx as nx\n",
"from qtensor import QAOAComposer\n",
"from qtensor import CirqQAOAComposer, QtreeQAOAComposer\n",
"from qtensor import VCQiskitQAOAComposer, PCQiskitQAOAComposer\n",
"from qtensor import VCQtreeQAOAComposer, PCQtreeQAOAComposer"
]
},
{
"cell_type": "markdown",
"id": "f8b97479-45a4-4b8c-aa64-adda17f1d7eb",
"metadata": {},
"source": [
"### QAOA problem graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5dbf5964-25d8-43d8-b11d-7e205dc4cd45",
"metadata": {},
"outputs": [],
"source": [
"\n",
"G = nx.erdos_renyi_graph(4, 2/(5-1))\n",
"nx.draw_kamada_kawai(G, with_labels=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ce1f7b9-49f9-4fe1-8706-495fbad08a09",
"metadata": {},
"outputs": [],
"source": [
"p = 1\n",
"qiskit_qaoa = VCQiskitQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"qiskit_qaoa.ansatz_state()\n",
"qiskit_qaoa.circuit.draw('mpl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d06acf1-f0a7-4b3d-b3e8-2044b9e662b8",
"metadata": {},
"outputs": [],
"source": [
"p = 1\n",
"qiskit_qaoa = PCQiskitQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"qiskit_qaoa.ansatz_state()\n",
"qiskit_qaoa.circuit.draw('mpl')"
]
},
{
"cell_type": "markdown",
"id": "17285e25-e4a8-48f3-830f-819003df454e",
"metadata": {},
"source": [
"## Simulate circuits\n",
"## Use lightcone optimisaiton\n",
"\n",
"Suppose we are interested in an expectation value of particular operator in a state $|\\psi\\rangle = \\hat U | 0\\rangle$. \n",
"We can use the fact that in the expression\n",
"$$\\langle \\psi | \\hat E | \\psi \\rangle = \\langle 0 | \\hat U^\\dagger \\hat E \\hat U |0\\rangle$$\n",
"a lot of operators from $\\hat U$ cancel out."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bacfda45-45de-48c6-84b2-700595abff81",
"metadata": {},
"outputs": [],
"source": [
"p = 2\n",
"qiskit_qaoa = VCQiskitQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"qiskit_qaoa.energy_expectation_lightcone((0,1))\n",
"qiskit_qaoa.circuit.draw('mpl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a96e8f63-c3ee-4918-bc38-bd60f511d305",
"metadata": {},
"outputs": [],
"source": [
"p = 2\n",
"qiskit_qaoa = PCQiskitQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"qiskit_qaoa.energy_expectation_lightcone((0,1))\n",
"qiskit_qaoa.circuit.draw('mpl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e0500a31-2cc0-4cba-9169-0c47fc74a969",
"metadata": {},
"outputs": [],
"source": [
"com = VCQtreeQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"com.energy_expectation_lightcone(list(G.edges())[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0473fced-0447-4ecf-ae0d-e964b7511cdb",
"metadata": {},
"outputs": [],
"source": [
"com = PCQtreeQAOAComposer(G, gamma=[.1]*p, beta=[.2]*p)\n",
"com.energy_expectation_lightcone(list(G.edges())[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1da40689-a891-4599-9d75-2ab1bcd6c861",
"metadata": {},
"outputs": [],
"source": [
"from qtensor.QAOASimulator import QAOAQtreeSimulator\n",
"qaoa_sim = QAOAQtreeSimulator(VCQtreeQAOAComposer)\n",
"\n",
"qaoa_sim.energy_expectation(G, gamma=[.1]*p, beta=[.2]*p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79f55af2-a9aa-44f5-beb4-a7180545c6f8",
"metadata": {},
"outputs": [],
"source": [
"from qtensor.QAOASimulator import QAOAQtreeSimulator\n",
"qaoa_sim = QAOAQtreeSimulator(PCQtreeQAOAComposer)\n",
"\n",
"p=3\n",
"qaoa_sim.energy_expectation(G, gamma=[.1]*p, beta=[.2]*p)"
]
},
{
"cell_type": "markdown",
"id": "b158dcfe-404a-4e4b-9654-2d5ee32cde01",
"metadata": {},
"source": [
"## QAOA"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "166e32f4-ddce-45f8-96e5-1bc12ef0a8c8",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.optimize import minimize"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f860402-2c18-463a-8136-8f13e47cc7d7",
"metadata": {},
"outputs": [],
"source": [
"edges = [(0, 1), (1, 2), (2, 0), (2, 3)]\n",
"graph = nx.Graph(edges)\n",
"\n",
"nx.draw(graph, with_labels=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d1495c2-fe6b-4b98-89fb-33c1fe8fb196",
"metadata": {},
"outputs": [],
"source": [
"\n",
"p=3\n",
"initial_params = np.array([.1]*2*p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca2cf6ef-7f52-453c-beb6-906fb863afe8",
"metadata": {},
"outputs": [],
"source": [
"def cost_function(params):\n",
" p = len(params) // 2\n",
"\n",
" # Split the single list into two lists\n",
" gammas = params[:p]\n",
" betas = params[p:]\n",
" expectation = qaoa_sim.energy_expectation(graph, gammas, betas)\n",
" return expectation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16de68dc-291a-4bf5-a358-845392b8f9d8",
"metadata": {},
"outputs": [],
"source": [
"# Minimize the function\n",
"result = minimize(cost_function, initial_params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5716d24f-9e74-4e07-afe5-c56ee70f667f",
"metadata": {},
"outputs": [],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bb1e7c3-285c-4440-8255-7722db98be57",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
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