JaunatisBlue
c4a82614b3
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573 lines
18 KiB
Plaintext
573 lines
18 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 Quimb 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": "0c5a8939",
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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": null,
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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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"quimb_backend = construct_backend(backend=\"qibotn\", platform=\"quimb\")\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)\n",
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"\n",
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"quimb_backend.setup_backend_specifics(quimb_backend=\"jax\", contractions_optimizer='auto-hq')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "926cfea5",
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"metadata": {},
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"source": [
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"Quimb accepts different methods for optimizing the way it does contractions, that we pass through \"contractions_optimizer\". \n",
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"We could also define our own cotengra contraction optimizer! \n",
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"\n",
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"cotengra is a Python library designed for **optimising contraction trees** and performing efficient contractions of large tensor‐networks.\n",
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"You can find it here: [https://github.com/jcmgray/cotengra](https://github.com/jcmgray/cotengra)\n",
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"\n",
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"For the sake of this tutorial however the default \"auto-hq\" will be fine :) "
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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": null,
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"id": "b0a1da82",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cotengra as ctg\n",
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"ctg_opt = ctg.ReusableHyperOptimizer(\n",
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" max_time=10,\n",
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" minimize='combo',\n",
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" slicing_opts=None,\n",
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" parallel=True,\n",
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" progbar=True\n",
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")\n",
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"# quimb_backend.setup_backend_specifics(quimb_backend=\"jax\", contractions_optimizer='ctg_opt')"
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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": 4,
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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": 5,
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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": 6,
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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": 7,
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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 quimb backend\n",
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"# Here we use only some of the possible arguments\n",
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"quimb_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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")"
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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 only if `return_array` is set to `True`;\n",
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"2. computation of the relevant probabilities of the final state.\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": 8,
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: creg\n",
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" warnings.warn(\n",
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"/home/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: measure\n",
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" warnings.warn(\n"
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]
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},
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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': qibotn (quimb),\n",
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" 'measures': Counter({'1101': 14,\n",
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" '1000': 12,\n",
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" '0010': 11,\n",
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" '0011': 11,\n",
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" '0110': 9,\n",
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" '0000': 8,\n",
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" '1010': 7,\n",
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" '1110': 6,\n",
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" '0100': 5,\n",
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" '1111': 5,\n",
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" '1011': 5,\n",
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" '0101': 4,\n",
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" '0111': 1,\n",
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" '0001': 1,\n",
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" '1100': 1}),\n",
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" 'measured_probabilities': {'1101': np.float64(0.12331159869893284),\n",
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" '1000': np.float64(0.11330883548333684),\n",
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" '0010': np.float64(0.0946686048198943),\n",
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" '0011': np.float64(0.07571277233522157),\n",
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" '0110': np.float64(0.051460648073692314),\n",
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" '0000': np.float64(0.08390937969317334),\n",
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" '1010': np.float64(0.03872758515126775),\n",
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" '1110': np.float64(0.07174919872960006),\n",
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" '0100': np.float64(0.07142939529687146),\n",
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" '1111': np.float64(0.10184806171791994),\n",
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" '1011': np.float64(0.053499396925872716),\n",
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" '0101': np.float64(0.05622305772698606),\n",
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" '0111': np.float64(0.040291850747292815),\n",
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" '0001': np.float64(0.004677011195208322),\n",
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" '1100': np.float64(0.013605984872668443)},\n",
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" 'prob_type': 'default',\n",
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" 'statevector': Array([[ 0.08809626-0.27595j ],\n",
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" [-0.05174781+0.04471214j],\n",
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" [ 0.00470146+0.30764672j],\n",
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" [-0.27208942+0.04098931j],\n",
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" [ 0.18807825+0.1898841j ],\n",
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" [ 0.22377063+0.07842041j],\n",
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" [-0.18900302+0.12545316j],\n",
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" [ 0.17105258-0.10503745j],\n",
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" [ 0.24859732-0.22695422j],\n",
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" [-0.04117391-0.0623003j ],\n",
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" [ 0.17371394-0.09247189j],\n",
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" [-0.22748126+0.04185291j],\n",
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" [ 0.09444097+0.06846087j],\n",
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" [-0.21784975-0.2754144j ],\n",
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" [-0.17359754+0.20399287j],\n",
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" [-0.01729751-0.31866732j]], dtype=complex64)}"
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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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"# # Simple execution (defaults)\n",
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"outcome = quimb_backend.execute_circuit(circuit=circuit, nshots=100, return_array=True)\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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" {'1101': np.float64(0.12331159869893284), '1000': np.float64(0.11330883548333684), '0010': np.float64(0.0946686048198943), '0011': np.float64(0.07571277233522157), '0110': np.float64(0.051460648073692314), '0000': np.float64(0.08390937969317334), '1010': np.float64(0.03872758515126775), '1110': np.float64(0.07174919872960006), '0100': np.float64(0.07142939529687146), '1111': np.float64(0.10184806171791994), '1011': np.float64(0.053499396925872716), '0101': np.float64(0.05622305772698606), '0111': np.float64(0.040291850747292815), '0001': np.float64(0.004677011195208322), '1100': np.float64(0.013605984872668443)}\n",
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"\n",
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"State:\n",
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" [[ 0.08809626-0.27595j ]\n",
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" [-0.05174781+0.04471214j]\n",
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" [ 0.00470146+0.30764672j]\n",
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" [-0.27208942+0.04098931j]\n",
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" [ 0.18807825+0.1898841j ]\n",
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" [ 0.22377063+0.07842041j]\n",
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" [-0.18900302+0.12545316j]\n",
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" [ 0.17105258-0.10503745j]\n",
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" [ 0.24859732-0.22695422j]\n",
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" [-0.04117391-0.0623003j ]\n",
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" [ 0.17371394-0.09247189j]\n",
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" [-0.22748126+0.04185291j]\n",
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" [ 0.09444097+0.06846087j]\n",
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" [-0.21784975-0.2754144j ]\n",
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" [-0.17359754+0.20399287j]\n",
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" [-0.01729751-0.31866732j]]\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": "9531f9d6",
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"metadata": {},
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"source": [
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"### Compute expectation values\n",
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"\n",
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"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",
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"\n",
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"---\n",
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"\n",
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"Let's start by importing some symbols, thanks to which we can build our observable."
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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": "647f2073",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import jax\n",
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"from qibo.backends import construct_backend\n",
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"from qibo import Circuit, gates"
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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": "74c63a41",
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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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"quimb_backend = construct_backend(backend=\"qibotn\", platform=\"quimb\")\n",
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"\n",
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"quimb_backend.setup_backend_specifics(\n",
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" quimb_backend =\"jax\", \n",
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" contractions_optimizer='auto-hq'\n",
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" )\n",
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"\n",
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"quimb_backend.configure_tn_simulation(\n",
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" max_bond_dimension=10\n",
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")"
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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": 18,
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"id": "b2a0decb",
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"metadata": {},
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"outputs": [],
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"source": [
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"from qibo.symbols import X, Z, Y\n",
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"from qibo.hamiltonians import XXZ\n",
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"\n",
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"# define Hamiltonian\n",
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"hamiltonian = XXZ(4, dense=False, backend=quimb_backend)"
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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": 19,
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"id": "bd734be8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# define circuit\n",
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"def build_circuit(nqubits, nlayers):\n",
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" circ = Circuit(nqubits)\n",
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" for layer 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.RX(q=q, theta=0.))\n",
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" for q in range(nqubits - 1):\n",
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" circ.add(gates.CNOT(q, q + 1))\n",
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" circ.add(gates.SWAP(q, q + 1))\n",
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" circ.add(gates.M(*range(nqubits)))\n",
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" return circ\n",
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"\n",
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"def build_circuit_problematic(nqubits, nlayers):\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\n",
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"\n",
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"\n",
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"nqubits = 4\n",
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"circuit = build_circuit(nqubits=nqubits, nlayers=3)\n"
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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": 20,
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"id": "fe63ff24",
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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",
|
||
"text": [
|
||
"Expectation value: 2.0\n",
|
||
"Elapsed time: 0.0268 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"start = time.time()\n",
|
||
"expval = hamiltonian.expectation(circuit)\n",
|
||
"\n",
|
||
"elapsed = time.time() - start\n",
|
||
"print(f\"Expectation value: {expval}\")\n",
|
||
"print(f\"Elapsed time: {elapsed:.4f} seconds\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d976a849",
|
||
"metadata": {},
|
||
"source": [
|
||
"Try with Qibo (which is by default using the Qibojit backend)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "fb1436c8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Qibo 0.2.21|INFO|2025-10-27 16:24:00]: Using numpy backend on /CPU:0\n",
|
||
"WARNING:root:Calculation of expectation values starting from the state is deprecated, use the ``expectation_from_state`` method if you really need it, or simply pass the circuit you want to calculate the expectation value from.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Expectation value: 2.0\n",
|
||
"Elapsed time: 0.0360 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"sym_hamiltonian = XXZ(4, dense=False, backend=None)\n",
|
||
"\n",
|
||
"# Let's show it\n",
|
||
"sym_hamiltonian.form\n",
|
||
"\n",
|
||
"# Compute expectation value\n",
|
||
"start = time.time()\n",
|
||
"result = sym_hamiltonian.expectation(circuit().state())\n",
|
||
"elapsed = time.time() - start\n",
|
||
"print(f\"Expectation value: {result}\")\n",
|
||
"print(f\"Elapsed time: {elapsed:.4f} seconds\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "77bef077",
|
||
"metadata": {},
|
||
"source": [
|
||
"They match! 🥳"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "50130ae6",
|
||
"metadata": {},
|
||
"source": [
|
||
"We can also compute gradient of expectation function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "6a3b26e4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:4927: UserWarning: Unsupported options for computing local_expectation with an MPS circuit supplied, ignoring: R, None, None, jax, None\n",
|
||
" warnings.warn(\n",
|
||
"/home/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:4927: UserWarning: Unsupported options for computing local_expectation with an MPS circuit supplied, ignoring: R, None, None, jax, None\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[-0.24630009 0.8370421 -0.11103702 -0.12855841 0.41325414 -0.0628037\n",
|
||
" 0.51638705 0.794163 -0.27972788 -1.0718998 0.02731732 1.0153619\n",
|
||
" -0.34494495 1.5744264 0.26920277 -0.36333832 0.12331417 0.5196531\n",
|
||
" 1.1294655 0.29257926 -0.18237355 0.8914014 -0.9471657 0.3492473\n",
|
||
" -0.3477673 0.24325958 0.04818404 -0.87983793 0.47196424 0.36605012\n",
|
||
" 1.005 0.65054715 -0.94860053 0.14459445 0.36571163 -0.2550101 ]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def f(circuit, hamiltonian, params):\n",
|
||
" circuit.set_parameters(params)\n",
|
||
" return hamiltonian.expectation(\n",
|
||
" circuit=circuit,\n",
|
||
" )\n",
|
||
"\n",
|
||
"parameters = np.random.uniform(-np.pi, np.pi, size=len(circuit.get_parameters()))\n",
|
||
"print(jax.grad(f, argnums=2)(circuit, hamiltonian, parameters))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "aeafa5a6-2afa-429c-a101-effa84bac1d2",
|
||
"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": "3.11.12"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|