JaunatisBlue
c4a82614b3
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582 lines
17 KiB
Plaintext
582 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "656bb283-ac6d-48d2-a029-3c417c9961f8",
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"metadata": {},
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"source": [
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"## Introduction to Quantum Matcha Tea backend in QiboTN\n",
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"\n",
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"#### Some imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6722d94e-e311-48f9-b6df-c6d829bf67fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import numpy as np\n",
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"from scipy import stats\n",
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"\n",
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"import qibo\n",
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"from qibo import Circuit, gates, hamiltonians\n",
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"from qibo.backends import construct_backend"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a009a5e0-cfd4-4a49-9f7c-e82f252c6147",
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"metadata": {},
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"source": [
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"#### Some hyper parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "64162116-1555-4a68-811c-01593739d622",
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"metadata": {},
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"outputs": [],
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"source": [
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"# construct qibotn backend\n",
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"qmatcha_backend = construct_backend(backend=\"qibotn\", platform=\"qmatchatea\")\n",
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"\n",
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"# set number of qubits\n",
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"nqubits = 4\n",
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"\n",
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"# set numpy random seed\n",
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"np.random.seed(42)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "252f5cd1-5932-4de6-8076-4a357d50ebad",
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"metadata": {},
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"source": [
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"#### Constructing a parametric quantum circuit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4a22a172-f50d-411d-afa3-fa61937c7b3a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_circuit(nqubits, nlayers):\n",
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" \"\"\"Construct a parametric quantum circuit.\"\"\"\n",
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" circ = Circuit(nqubits)\n",
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" for _ in range(nlayers):\n",
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" for q in range(nqubits):\n",
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" circ.add(gates.RY(q=q, theta=0.))\n",
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" circ.add(gates.RZ(q=q, theta=0.))\n",
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" [circ.add(gates.CNOT(q%nqubits, (q+1)%nqubits) for q in range(nqubits))]\n",
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" circ.add(gates.M(*range(nqubits)))\n",
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" return circ"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "76f23c57-6d08-496b-9a27-52fb63bbfcb1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0: ─RY─RZ─o─────X─RY─RZ─o─────X─RY─RZ─o─────X─M─\n",
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"1: ─RY─RZ─X─o───|─RY─RZ─X─o───|─RY─RZ─X─o───|─M─\n",
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"2: ─RY─RZ───X─o─|─RY─RZ───X─o─|─RY─RZ───X─o─|─M─\n",
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"3: ─RY─RZ─────X─o─RY─RZ─────X─o─RY─RZ─────X─o─M─\n"
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]
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}
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],
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"source": [
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"circuit = build_circuit(nqubits=nqubits, nlayers=3)\n",
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"circuit.draw()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "07b2c097-cea2-42ec-8f1d-b4bbb5b71d98",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Setting random parameters\n",
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"circuit.set_parameters(\n",
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" parameters=np.random.uniform(-np.pi, np.pi, len(circuit.get_parameters())),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fd0cea52-03f5-4366-a01a-a5a84aa8ebc7",
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"metadata": {},
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"source": [
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"#### Setting up the tensor network simulator\n",
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"\n",
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"Depending on the simulator, various parameters can be set. One can customize the tensor network execution via the `backend.configure_tn_simulation` function, whose face depends on the specific backend provider."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2ee03e94-d794-4a51-9e76-01e8d8a259ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Customization of the tensor network simulation in the case of qmatchatea\n",
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"# Here we use only some of the possible arguments\n",
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"qmatcha_backend.configure_tn_simulation(\n",
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" ansatz=\"MPS\",\n",
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" max_bond_dimension=10,\n",
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" cut_ratio=1e-6,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "648d85b8-445d-4081-aeed-1691fbae67be",
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"metadata": {},
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"source": [
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"#### Executing through the backend\n",
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"\n",
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"The `backend.execute_circuit` method can be used then. We can simulate results in three ways:\n",
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"1. reconstruction of the final state (statevector like, only if `nqubits < 20` due to Quantum Matcha Tea setup) only if `return_array` is set to `True`;\n",
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"2. computation of the relevant probabilities of the final state. There are three way of doing so, but we will see it directly into the docstrings;\n",
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"3. reconstruction of the relevant state's frequencies (only if `nshots` is not `None`)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "35a244c3-adba-4b8b-b28c-0ab592b0f7cf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'nqubits': 4,\n",
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" 'backend': QMatchaTeaBackend(),\n",
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" 'measures': None,\n",
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" 'measured_probabilities': {'U': {'0000': (0.0, 0.08390937969317301),\n",
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" '0001': (0.08390937969317301, 0.08858639088838134),\n",
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" '0010': (0.08858639088838131, 0.1832549957082757),\n",
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" '0011': (0.1832549957082757, 0.25896776804349736),\n",
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" '0100': (0.2589677680434974, 0.33039716334036867),\n",
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" '0101': (0.33039716334036867, 0.386620221067355),\n",
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" '0110': (0.3866202210673549, 0.4380808691410473),\n",
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" '0111': (0.4380808691410473, 0.47837271988834),\n",
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" '1000': (0.47837271988834, 0.5916815553716759),\n",
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" '1001': (0.5916815553716759, 0.5972581739037379),\n",
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" '1010': (0.5972581739037378, 0.6359857590550054),\n",
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" '1011': (0.6359857590550054, 0.6894851559808782),\n",
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" '1100': (0.6894851559808783, 0.7030911408535467),\n",
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" '1101': (0.7030911408535467, 0.8264027395524797),\n",
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" '1110': (0.8264027395524797, 0.8981519382820797),\n",
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" '1111': (0.8981519382820797, 0.9999999999999998)},\n",
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" 'E': [None],\n",
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" 'G': [None]},\n",
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" 'prob_type': 'U',\n",
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" 'statevector': None}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Simple execution (defaults)\n",
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"outcome = qmatcha_backend.execute_circuit(circuit=circuit)\n",
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"\n",
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"# Print outcome\n",
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"vars(outcome)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "60501c3d-2a44-421f-b434-4a508714b132",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'nqubits': 4,\n",
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" 'backend': QMatchaTeaBackend(),\n",
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" 'measures': None,\n",
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" 'measured_probabilities': {'U': [None],\n",
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" 'E': [None],\n",
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" 'G': {'1110': 0.07174919872960005,\n",
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" '1111': 0.10184806171792007,\n",
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" '0010': 0.09466860481989439,\n",
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" '0011': 0.07571277233522165}},\n",
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" 'prob_type': 'G',\n",
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" 'statevector': array([ 0.08809627-0.27595005j, 0.24859731-0.22695421j,\n",
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" 0.18807826+0.18988408j, 0.09444097+0.06846085j,\n",
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" 0.00470148+0.30764671j, 0.17371395-0.09247188j,\n",
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" -0.18900305+0.12545316j, -0.17359753+0.20399288j,\n",
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" -0.0517478 +0.04471215j, -0.0411739 -0.06230031j,\n",
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" 0.22377064+0.07842041j, -0.21784975-0.27541439j,\n",
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" -0.27208941+0.04098933j, -0.22748127+0.04185292j,\n",
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" 0.17105258-0.10503745j, -0.01729753-0.31866731j])}"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Execution with a specific probability type\n",
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"# We use here \"E\", which is cutting some of the components if under a threshold\n",
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"# We also retrieve the statevector\n",
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"outcome = qmatcha_backend.execute_circuit(\n",
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" circuit=circuit,\n",
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" prob_type=\"G\",\n",
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" prob_threshold=0.3,\n",
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" return_array=True,\n",
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")\n",
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"\n",
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"# Print outcome\n",
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"vars(outcome)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "84ec0b48-f6b4-495c-93b8-8e42d1a8b0df",
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"metadata": {},
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"source": [
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"---\n",
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"\n",
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"One can access to the specific contents of the simulation outcome."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "c0443efc-21ef-4ed5-9cf4-785d204a1881",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Probabilities:\n",
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" [0.0717492 0.10184806 0.0946686 0.07571277]\n",
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"\n",
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"State:\n",
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" [ 0.08809627-0.27595005j 0.24859731-0.22695421j 0.18807826+0.18988408j\n",
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" 0.09444097+0.06846085j 0.00470148+0.30764671j 0.17371395-0.09247188j\n",
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" -0.18900305+0.12545316j -0.17359753+0.20399288j -0.0517478 +0.04471215j\n",
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" -0.0411739 -0.06230031j 0.22377064+0.07842041j -0.21784975-0.27541439j\n",
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" -0.27208941+0.04098933j -0.22748127+0.04185292j 0.17105258-0.10503745j\n",
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" -0.01729753-0.31866731j]\n",
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"\n"
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]
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}
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],
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"source": [
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"print(f\"Probabilities:\\n {outcome.probabilities()}\\n\")\n",
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"print(f\"State:\\n {outcome.state()}\\n\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9f477388-ca45-409a-a0ce-6603ec294e42",
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"metadata": {},
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"source": [
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"---\n",
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"\n",
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"But frequencies cannot be accessed, since no shots have been set."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e9413c7-602a-44ed-a50c-1c3dd4dd7494",
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"metadata": {},
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"source": [
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"---\n",
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"\n",
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"We can then repeat the execution by setting the number of shots"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "68122cd3-662f-4fd1-bb9c-d33b6f5448dd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'nqubits': 4,\n",
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" 'backend': QMatchaTeaBackend(),\n",
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" 'measures': {'0000': 92,\n",
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" '0001': 7,\n",
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" '0010': 85,\n",
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" '0011': 79,\n",
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" '0100': 81,\n",
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" '0101': 55,\n",
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" '0110': 47,\n",
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" '0111': 39,\n",
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" '1000': 117,\n",
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" '1001': 7,\n",
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" '1010': 38,\n",
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" '1011': 53,\n",
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" '1100': 22,\n",
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" '1101': 129,\n",
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" '1110': 74,\n",
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" '1111': 99},\n",
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" 'measured_probabilities': {'U': [None],\n",
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" 'E': {'0000': 0.08390937969317301,\n",
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" '0010': 0.09466860481989439,\n",
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" '0011': 0.07571277233522165,\n",
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" '0100': 0.07142939529687124,\n",
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" '0101': 0.05622305772698632,\n",
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" '0110': 0.05146064807369245,\n",
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" '1000': 0.11330883548333581,\n",
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" '1011': 0.053499396925872765,\n",
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" '1101': 0.12331159869893296,\n",
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" '1110': 0.07174919872960005,\n",
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" '1111': 0.10184806171792007},\n",
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" 'G': [None]},\n",
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" 'prob_type': 'E',\n",
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" 'statevector': array([ 0.08809627-0.27595005j, 0.24859731-0.22695421j,\n",
|
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" 0.18807826+0.18988408j, 0.09444097+0.06846085j,\n",
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" 0.00470148+0.30764671j, 0.17371395-0.09247188j,\n",
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" -0.18900305+0.12545316j, -0.17359753+0.20399288j,\n",
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" -0.0517478 +0.04471215j, -0.0411739 -0.06230031j,\n",
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" 0.22377064+0.07842041j, -0.21784975-0.27541439j,\n",
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" -0.27208941+0.04098933j, -0.22748127+0.04185292j,\n",
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" 0.17105258-0.10503745j, -0.01729753-0.31866731j])}"
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]
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},
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"execution_count": 10,
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"metadata": {},
|
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"output_type": "execute_result"
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|
}
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],
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"source": [
|
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"# Execution with a specific probability type\n",
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"# We use here \"E\", which is cutting some of the components if under a threshold\n",
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"outcome = qmatcha_backend.execute_circuit(\n",
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" circuit=circuit,\n",
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" nshots=1024,\n",
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" prob_type=\"E\",\n",
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" prob_threshold=0.05,\n",
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" return_array=True\n",
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")\n",
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"\n",
|
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"# Print outcome\n",
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"vars(outcome)"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": 11,
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|
"id": "ef0e9591-ccca-4cdd-a81b-2bfb3caaf3d0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Frequencies:\n",
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" {'0000': 92, '0001': 7, '0010': 85, '0011': 79, '0100': 81, '0101': 55, '0110': 47, '0111': 39, '1000': 117, '1001': 7, '1010': 38, '1011': 53, '1100': 22, '1101': 129, '1110': 74, '1111': 99}\n",
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"\n",
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"Probabilities:\n",
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" [0.08390938 0.0946686 0.07571277 0.0714294 0.05622306 0.05146065\n",
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" 0.11330884 0.0534994 0.1233116 0.0717492 0.10184806]\n",
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"\n",
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"State:\n",
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" [ 0.08809627-0.27595005j 0.24859731-0.22695421j 0.18807826+0.18988408j\n",
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" 0.09444097+0.06846085j 0.00470148+0.30764671j 0.17371395-0.09247188j\n",
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" -0.18900305+0.12545316j -0.17359753+0.20399288j -0.0517478 +0.04471215j\n",
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" -0.0411739 -0.06230031j 0.22377064+0.07842041j -0.21784975-0.27541439j\n",
|
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" -0.27208941+0.04098933j -0.22748127+0.04185292j 0.17105258-0.10503745j\n",
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" -0.01729753-0.31866731j]\n",
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"\n"
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]
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}
|
|
],
|
|
"source": [
|
|
"# Frequencies and probabilities\n",
|
|
"print(f\"Frequencies:\\n {outcome.frequencies()}\\n\")\n",
|
|
"print(f\"Probabilities:\\n {outcome.probabilities()}\\n\")\n",
|
|
"print(f\"State:\\n {outcome.state()}\\n\") # Only if return_array = True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dd84f1f3-7aa5-4ad1-ae09-81e0aff75b5b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Compute expectation values\n",
|
|
"\n",
|
|
"Another important feature of this backend is the `expectation` function. In fact, we can compute expectation values of given observables thorugh a Qibo-friendly interface.\n",
|
|
"\n",
|
|
"---\n",
|
|
"\n",
|
|
"Let's start by importing some symbols, thanks to which we can build our observable."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "0b46e315-7786-4247-bd2a-83ea1c5842eb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from qibo.symbols import Z, X"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "37385485-e8a3-4ab0-ad44-bcc4e9da24ca",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0: ─RY─RZ─o─────X─RY─RZ─o─────X─RY─RZ─o─────X─M─\n",
|
|
"1: ─RY─RZ─X─o───|─RY─RZ─X─o───|─RY─RZ─X─o───|─M─\n",
|
|
"2: ─RY─RZ───X─o─|─RY─RZ───X─o─|─RY─RZ───X─o─|─M─\n",
|
|
"3: ─RY─RZ─────X─o─RY─RZ─────X─o─RY─RZ─────X─o─M─\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# We are going to compute the expval of an Hamiltonian\n",
|
|
"# On the state prepared by the following circuit\n",
|
|
"circuit.draw()\n",
|
|
"\n",
|
|
"circuit.set_parameters(\n",
|
|
" np.random.randn(len(circuit.get_parameters()))\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "ddecc910-7804-4199-8577-a7db38a16db8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Qibo 0.2.15|INFO|2025-02-12 14:36:17]: Using qibojit (numba) backend on /CPU:0\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/latex": [
|
|
"$\\displaystyle - 1.5 X_{0} Z_{2} + 0.5 Z_{0} Z_{1} + Z_{3}$"
|
|
],
|
|
"text/plain": [
|
|
"-1.5*X0*Z2 + 0.5*Z0*Z1 + Z3"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can create a symbolic Hamiltonian\n",
|
|
"form = 0.5 * Z(0) * Z(1) +- 1.5 * X(0) * Z(2) + Z(3)\n",
|
|
"hamiltonian = hamiltonians.SymbolicHamiltonian(form)\n",
|
|
"\n",
|
|
"# Let's show it\n",
|
|
"hamiltonian.form"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "163b70a3-814a-4a62-a98a-2ffca933a544",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.4355195352502318"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# And compute its expectation value\n",
|
|
"qmatcha_backend.expectation(\n",
|
|
" circuit=circuit,\n",
|
|
" observable=hamiltonian,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "2d8c4a9c-eca3-49d0-bdbf-ab054172c4e5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.43551953525022985"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Try with Qibo (which is by default using the Qibojit backend)\n",
|
|
"hamiltonian = hamiltonians.SymbolicHamiltonian(form)\n",
|
|
"hamiltonian.expectation(circuit().state())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "94df291c-9ddc-4b2e-8442-5fca00784bd8",
|
|
"metadata": {},
|
|
"source": [
|
|
"They match! 🥳"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.10.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|