{ "cells": [ { "cell_type": "markdown", "id": "656bb283-ac6d-48d2-a029-3c417c9961f8", "metadata": {}, "source": [ "## Introduction to Quimb backend in QiboTN\n", "\n", "#### Some imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "6722d94e-e311-48f9-b6df-c6d829bf67fb", "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "# from scipy import stats\n", "\n", "# import qibo\n", "from qibo import Circuit, gates, hamiltonians\n", "from qibo.backends import construct_backend" ] }, { "cell_type": "markdown", "id": "a009a5e0-cfd4-4a49-9f7c-e82f252c6147", "metadata": {}, "source": [ "#### Some hyper parameters" ] }, { "cell_type": "code", "execution_count": null, "id": "b0a1da82", "metadata": {}, "outputs": [], "source": [ "import cotengra as ctg\n", "ctg_opt = ctg.ReusableHyperOptimizer(\n", " max_time=10,\n", " minimize='combo',\n", " slicing_opts=None,\n", " parallel=True,\n", " progbar=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "64162116-1555-4a68-811c-01593739d622", "metadata": {}, "outputs": [], "source": [ "# construct qibotn backend\n", "quimb_backend = construct_backend(backend=\"qibotn\", platform=\"quimb\")\n", "\n", "# set number of qubits\n", "nqubits = 4\n", "\n", "# set numpy random seed\n", "np.random.seed(42)\n", "\n", "quimb_backend.setup_backend_specifics(\n", " qimb_backend=\"jax\", \n", " optimizer='auto-hq'\n", " )" ] }, { "cell_type": "markdown", "id": "252f5cd1-5932-4de6-8076-4a357d50ebad", "metadata": {}, "source": [ "#### Constructing a parametric quantum circuit" ] }, { "cell_type": "code", "execution_count": 3, "id": "4a22a172-f50d-411d-afa3-fa61937c7b3a", "metadata": {}, "outputs": [], "source": [ "def build_circuit(nqubits, nlayers):\n", " \"\"\"Construct a parametric quantum circuit.\"\"\"\n", " circ = Circuit(nqubits)\n", " for _ in range(nlayers):\n", " for q in range(nqubits):\n", " circ.add(gates.RY(q=q, theta=0.))\n", " circ.add(gates.RZ(q=q, theta=0.))\n", " [circ.add(gates.CNOT(q%nqubits, (q+1)%nqubits) for q in range(nqubits))]\n", " circ.add(gates.M(*range(nqubits)))\n", " return circ" ] }, { "cell_type": "code", "execution_count": 4, "id": "76f23c57-6d08-496b-9a27-52fb63bbfcb1", "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": [ "circuit = build_circuit(nqubits=nqubits, nlayers=3)\n", "circuit.draw()" ] }, { "cell_type": "code", "execution_count": 6, "id": "07b2c097-cea2-42ec-8f1d-b4bbb5b71d98", "metadata": {}, "outputs": [], "source": [ "# Setting random parameters\n", "circuit.set_parameters(\n", " parameters=np.random.uniform(-np.pi, np.pi, len(circuit.get_parameters())),\n", ")" ] }, { "cell_type": "markdown", "id": "fd0cea52-03f5-4366-a01a-a5a84aa8ebc7", "metadata": {}, "source": [ "#### Setting up the tensor network simulator\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 7, "id": "2ee03e94-d794-4a51-9e76-01e8d8a259ba", "metadata": {}, "outputs": [], "source": [ "# Customization of the tensor network simulation in the case of quimb backend\n", "# Here we use only some of the possible arguments\n", "quimb_backend.configure_tn_simulation(\n", " #ansatz=\"MPS\",\n", " max_bond_dimension=10\n", ")" ] }, { "cell_type": "markdown", "id": "648d85b8-445d-4081-aeed-1691fbae67be", "metadata": {}, "source": [ "#### Executing through the backend\n", "\n", "The `backend.execute_circuit` method can be used then. We can simulate results in three ways:\n", "1. reconstruction of the final state only if `return_array` is set to `True`;\n", "2. computation of the relevant probabilities of the final state.\n", "3. reconstruction of the relevant state's frequencies (only if `nshots` is not `None`)." ] }, { "cell_type": "code", "execution_count": 8, "id": "35a244c3-adba-4b8b-b28c-0ab592b0f7cf", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mattia/main_env/lib/python3.12/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: creg\n", " warnings.warn(\n", "/home/mattia/main_env/lib/python3.12/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: measure\n", " warnings.warn(\n", "/home/mattia/main_env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "data": { "text/plain": [ "{'nqubits': 4,\n", " 'backend': qibotn (quimb),\n", " 'measures': Counter({'0011': 8,\n", " '0010': 12,\n", " '0111': 4,\n", " '1011': 7,\n", " '0000': 8,\n", " '1110': 14,\n", " '0101': 4,\n", " '1010': 4,\n", " '1000': 14,\n", " '1111': 8,\n", " '0100': 6,\n", " '1101': 8,\n", " '1100': 1,\n", " '0110': 2}),\n", " 'measured_probabilities': {'1110': np.float64(0.07174919872959985),\n", " '1000': np.float64(0.11330883548333587),\n", " '0010': np.float64(0.09466860481989385),\n", " '0011': np.float64(0.07571277233522114),\n", " '0000': np.float64(0.08390937969317269),\n", " '1111': np.float64(0.10184806171791962),\n", " '1101': np.float64(0.12331159869893256),\n", " '1011': np.float64(0.053499396925872744),\n", " '0100': np.float64(0.07142939529687138),\n", " '0111': np.float64(0.04029185074729259),\n", " '0101': np.float64(0.05622305772698622),\n", " '1010': np.float64(0.03872758515126756),\n", " '0110': np.float64(0.05146064807369214),\n", " '1100': np.float64(0.013605984872668404)},\n", " 'prob_type': 'default',\n", " 'statevector': Array([[ 0.08809624-0.27594998j],\n", " [-0.05174781+0.04471217j],\n", " [ 0.00470147+0.30764672j],\n", " [-0.27208942+0.0409893j ],\n", " [ 0.18807822+0.18988408j],\n", " [ 0.2237706 +0.07842042j],\n", " [-0.18900308+0.12545314j],\n", " [ 0.17105256-0.10503749j],\n", " [ 0.24859734-0.22695419j],\n", " [-0.0411739 -0.06230037j],\n", " [ 0.17371392-0.09247189j],\n", " [-0.22748128+0.0418529j ],\n", " [ 0.09444095+0.06846087j],\n", " [-0.21784972-0.2754144j ],\n", " [-0.17359753+0.20399286j],\n", " [-0.01729754-0.31866732j]], dtype=complex64)}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# # Simple execution (defaults)\n", "outcome = quimb_backend.execute_circuit(circuit=circuit, nshots=100, return_array=True)\n", "\n", "# # Print outcome\n", "vars(outcome)" ] }, { "cell_type": "markdown", "id": "84ec0b48-f6b4-495c-93b8-8e42d1a8b0df", "metadata": {}, "source": [ "---\n", "\n", "One can access to the specific contents of the simulation outcome." ] }, { "cell_type": "code", "execution_count": 9, "id": "c0443efc-21ef-4ed5-9cf4-785d204a1881", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probabilities:\n", " {'1110': np.float64(0.07174919872959985), '1000': np.float64(0.11330883548333587), '0010': np.float64(0.09466860481989385), '0011': np.float64(0.07571277233522114), '0000': np.float64(0.08390937969317269), '1111': np.float64(0.10184806171791962), '1101': np.float64(0.12331159869893256), '1011': np.float64(0.053499396925872744), '0100': np.float64(0.07142939529687138), '0111': np.float64(0.04029185074729259), '0101': np.float64(0.05622305772698622), '1010': np.float64(0.03872758515126756), '0110': np.float64(0.05146064807369214), '1100': np.float64(0.013605984872668404)}\n", "\n", "State:\n", " [[ 0.08809624-0.27594998j]\n", " [-0.05174781+0.04471217j]\n", " [ 0.00470147+0.30764672j]\n", " [-0.27208942+0.0409893j ]\n", " [ 0.18807822+0.18988408j]\n", " [ 0.2237706 +0.07842042j]\n", " [-0.18900308+0.12545314j]\n", " [ 0.17105256-0.10503749j]\n", " [ 0.24859734-0.22695419j]\n", " [-0.0411739 -0.06230037j]\n", " [ 0.17371392-0.09247189j]\n", " [-0.22748128+0.0418529j ]\n", " [ 0.09444095+0.06846087j]\n", " [-0.21784972-0.2754144j ]\n", " [-0.17359753+0.20399286j]\n", " [-0.01729754-0.31866732j]]\n", "\n" ] } ], "source": [ "print(f\"Probabilities:\\n {outcome.probabilities()}\\n\")\n", "print(f\"State:\\n {outcome.state()}\\n\")" ] }, { "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": 10, "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": 11, "id": "ddecc910-7804-4199-8577-a7db38a16db8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Qibo 0.2.20|INFO|2025-09-20 16:43:42]: 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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from qibo.symbols import Z, X, I\n", "# 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": 13, "id": "163b70a3-814a-4a62-a98a-2ffca933a544", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expectation value: 0.7143489122390747\n", "Elapsed time: 12.4550 seconds\n" ] } ], "source": [ "start = time.time()\n", "expval = quimb_backend.expectation(\n", " circuit=circuit,\n", " observable=hamiltonian,\n", ")\n", "elapsed = time.time() - start\n", "print(f\"Expectation value: {expval}\")\n", "print(f\"Elapsed time: {elapsed:.4f} seconds\")" ] }, { "cell_type": "markdown", "id": "90663e28", "metadata": {}, "source": [ "Try with Qibo (which is by default using the Qibojit backend)\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "e2d05707", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expectation value: 0.7143570920618565\n", "Elapsed time: 0.5871 seconds\n" ] } ], "source": [ "start = time.time()\n", "result = 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": "94df291c-9ddc-4b2e-8442-5fca00784bd8", "metadata": {}, "source": [ "They match! 🥳" ] }, { "cell_type": "markdown", "id": "d2d119fc", "metadata": {}, "source": [ "### Derivative of the extimation function" ] }, { "cell_type": "code", "execution_count": 31, "id": "8df55c5f", "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": [ "# grad of this circuit returning nan for some reason...\n", "\n", "def build_circuit(nqubits, nlayers):\n", " \"\"\"Construct a parametric quantum circuit.\"\"\"\n", " circ = Circuit(nqubits)\n", " for _ in range(nlayers):\n", " for q in range(nqubits):\n", " circ.add(gates.RY(q=q, theta=0.))\n", " circ.add(gates.RZ(q=q, theta=0.))\n", " [circ.add(gates.CNOT(q%nqubits, (q+1)%nqubits) for q in range(nqubits))]\n", " circ.add(gates.M(*range(nqubits)))\n", " return circ\n", "\n", "circuit = build_circuit(nqubits=nqubits, nlayers=3)\n", "circuit.draw()" ] }, { "cell_type": "code", "execution_count": null, "id": "b02de56b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: ─RY─RZ─RX─o─x─────────RY─RZ─RX─o─x─────────RY─RZ─RX─o─x─────────M─\n", "1: ─RY─RZ─RX─X─x─o─x─────RY─RZ─RX─X─x─o─x─────RY─RZ─RX─X─x─o─x─────M─\n", "2: ─RY─RZ─RX─────X─x─o─x─RY─RZ─RX─────X─x─o─x─RY─RZ─RX─────X─x─o─x─M─\n", "3: ─RY─RZ─RX─────────X─x─RY─RZ─RX─────────X─x─RY─RZ─RX─────────X─x─M─\n" ] } ], "source": [ "def build_circuit(nqubits, nlayers):\n", " circ = Circuit(nqubits)\n", " for _ in range(nlayers):\n", " for q in range(nqubits):\n", " circ.add(gates.RY(q=q, theta=0.))\n", " circ.add(gates.RZ(q=q, theta=0.))\n", " circ.add(gates.RX(q=q, theta=0.))\n", " for q in range(nqubits - 1):\n", " circ.add(gates.CNOT(q, q + 1))\n", " circ.add(gates.SWAP(q, q + 1))\n", " circ.add(gates.M(*range(nqubits)))\n", " return circ\n", "\n", "circuit = build_circuit(nqubits=nqubits, nlayers=3)\n", "circuit.draw()" ] }, { "cell_type": "code", "execution_count": 23, "id": "b803250f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Array(1.4999985, dtype=float32)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quimb_backend.expectation(\n", " circuit=circuit, \n", " observable=hamiltonian,\n", " )" ] }, { "cell_type": "code", "execution_count": 29, "id": "0943482e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Array(0.4465402, dtype=float32), Array([-1.5755819e-01, 9.7801067e-02, -1.2350259e-01, 1.3670625e-01,\n", " 3.6954228e-03, -1.7437905e-02, 2.7746204e-01, -1.0357879e-01,\n", " 1.1504190e-01, -4.5175910e-02, -4.8447326e-02, 1.4743687e-01,\n", " -3.0708680e-01, 2.0652822e-01, 1.9298886e-01, 5.1306009e-02,\n", " -3.3362946e-01, -7.5548244e-01, -3.0034758e-02, -5.2868712e-01,\n", " 4.8458660e-01, -2.9802322e-08, 8.0767423e-02, 0.0000000e+00], dtype=float32))\n" ] } ], "source": [ "import jax\n", "\n", "def f(params):\n", " circuit.set_parameters(params)\n", " return quimb_backend.expectation(\n", " circuit=circuit,\n", " observable=hamiltonian,\n", " )\n", "\n", "parameters = np.random.uniform(-np.pi, np.pi, size=len(circuit.get_parameters()))\n", "print(jax.value_and_grad(f)(parameters))\n" ] } ], "metadata": { "kernelspec": { "display_name": "main_env", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }