diff --git a/examples/quimb_intro/quimb_introduction.ipynb b/examples/quimb_intro/quimb_introduction.ipynb new file mode 100644 index 0000000..5eaacc4 --- /dev/null +++ b/examples/quimb_intro/quimb_introduction.ipynb @@ -0,0 +1,572 @@ +{ + "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": "0c5a8939", + "metadata": {}, + "source": [ + "#### Some hyper parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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(quimb_backend=\"jax\", contractions_optimizer='auto-hq')" + ] + }, + { + "cell_type": "markdown", + "id": "926cfea5", + "metadata": {}, + "source": [ + "Quimb accepts different methods for optimizing the way it does contractions, that we pass through \"contractions_optimizer\". \n", + "We could also define our own cotengra contraction optimizer! \n", + "\n", + "cotengra is a Python library designed for **optimising contraction trees** and performing efficient contractions of large tensor‐networks.\n", + "You can find it here: [https://github.com/jcmgray/cotengra](https://github.com/jcmgray/cotengra)\n", + "\n", + "For the sake of this tutorial however the default \"auto-hq\" will be fine :) " + ] + }, + { + "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", + "# quimb_backend.setup_backend_specifics(quimb_backend=\"jax\", contractions_optimizer='ctg_opt')" + ] + }, + { + "cell_type": "markdown", + "id": "252f5cd1-5932-4de6-8076-4a357d50ebad", + "metadata": {}, + "source": [ + "#### Constructing a parametric quantum circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: creg\n", + " warnings.warn(\n", + "/home/andrea/python_envs/3.11/lib/python3.11/site-packages/quimb/tensor/circuit.py:215: SyntaxWarning: Unsupported operation ignored: measure\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "{'nqubits': 4,\n", + " 'backend': qibotn (quimb),\n", + " 'measures': Counter({'1101': 14,\n", + " '1000': 12,\n", + " '0010': 11,\n", + " '0011': 11,\n", + " '0110': 9,\n", + " '0000': 8,\n", + " '1010': 7,\n", + " '1110': 6,\n", + " '0100': 5,\n", + " '1111': 5,\n", + " '1011': 5,\n", + " '0101': 4,\n", + " '0111': 1,\n", + " '0001': 1,\n", + " '1100': 1}),\n", + " 'measured_probabilities': {'1101': np.float64(0.12331159869893284),\n", + " '1000': np.float64(0.11330883548333684),\n", + " '0010': np.float64(0.0946686048198943),\n", + " '0011': np.float64(0.07571277233522157),\n", + " '0110': np.float64(0.051460648073692314),\n", + " '0000': np.float64(0.08390937969317334),\n", + " '1010': np.float64(0.03872758515126775),\n", + " '1110': np.float64(0.07174919872960006),\n", + " '0100': np.float64(0.07142939529687146),\n", + " '1111': np.float64(0.10184806171791994),\n", + " '1011': np.float64(0.053499396925872716),\n", + " '0101': np.float64(0.05622305772698606),\n", + " '0111': np.float64(0.040291850747292815),\n", + " '0001': np.float64(0.004677011195208322),\n", + " '1100': np.float64(0.013605984872668443)},\n", + " 'prob_type': 'default',\n", + " 'statevector': Array([[ 0.08809626-0.27595j ],\n", + " [-0.05174781+0.04471214j],\n", + " [ 0.00470146+0.30764672j],\n", + " [-0.27208942+0.04098931j],\n", + " [ 0.18807825+0.1898841j ],\n", + " [ 0.22377063+0.07842041j],\n", + " [-0.18900302+0.12545316j],\n", + " [ 0.17105258-0.10503745j],\n", + " [ 0.24859732-0.22695422j],\n", + " [-0.04117391-0.0623003j ],\n", + " [ 0.17371394-0.09247189j],\n", + " [-0.22748126+0.04185291j],\n", + " [ 0.09444097+0.06846087j],\n", + " [-0.21784975-0.2754144j ],\n", + " [-0.17359754+0.20399287j],\n", + " [-0.01729751-0.31866732j]], 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", + " {'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", + "\n", + "State:\n", + " [[ 0.08809626-0.27595j ]\n", + " [-0.05174781+0.04471214j]\n", + " [ 0.00470146+0.30764672j]\n", + " [-0.27208942+0.04098931j]\n", + " [ 0.18807825+0.1898841j ]\n", + " [ 0.22377063+0.07842041j]\n", + " [-0.18900302+0.12545316j]\n", + " [ 0.17105258-0.10503745j]\n", + " [ 0.24859732-0.22695422j]\n", + " [-0.04117391-0.0623003j ]\n", + " [ 0.17371394-0.09247189j]\n", + " [-0.22748126+0.04185291j]\n", + " [ 0.09444097+0.06846087j]\n", + " [-0.21784975-0.2754144j ]\n", + " [-0.17359754+0.20399287j]\n", + " [-0.01729751-0.31866732j]]\n", + "\n" + ] + } + ], + "source": [ + "print(f\"Probabilities:\\n {outcome.probabilities()}\\n\")\n", + "print(f\"State:\\n {outcome.state()}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "9531f9d6", + "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": "647f2073", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax\n", + "from qibo.backends import construct_backend\n", + "from qibo import Circuit, gates" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74c63a41", + "metadata": {}, + "outputs": [], + "source": [ + "# construct qibotn backend\n", + "quimb_backend = construct_backend(backend=\"qibotn\", platform=\"quimb\")\n", + "\n", + "quimb_backend.setup_backend_specifics(\n", + " quimb_backend =\"jax\", \n", + " contractions_optimizer='auto-hq'\n", + " )\n", + "\n", + "quimb_backend.configure_tn_simulation(\n", + " max_bond_dimension=10\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b2a0decb", + "metadata": {}, + "outputs": [], + "source": [ + "from qibo.symbols import X, Z, Y\n", + "from qibo.hamiltonians import XXZ\n", + "\n", + "# define Hamiltonian\n", + "hamiltonian = XXZ(4, dense=False, backend=quimb_backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bd734be8", + "metadata": {}, + "outputs": [], + "source": [ + "# define circuit\n", + "def build_circuit(nqubits, nlayers):\n", + " circ = Circuit(nqubits)\n", + " for layer 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", + "def build_circuit_problematic(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.CNOT(q%nqubits, (q+1)%nqubits) for q in range(nqubits))]\n", + " circ.add(gates.M(*range(nqubits)))\n", + " return circ\n", + "\n", + "\n", + "nqubits = 4\n", + "circuit = build_circuit(nqubits=nqubits, nlayers=3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fe63ff24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "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 +} diff --git a/poetry.lock b/poetry.lock index 3d2448d..4da7ea1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. 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cuda = ["cupy-cuda11x", "cuquantum-python-cu11"] -qmatchatea = ["qmatchatea"] +qmatchatea = ["qiskit", "qmatchatea", "qtealeaves"] [metadata] lock-version = "2.1" python-versions = ">=3.11,<3.14" -content-hash = "59dbbb322cfae3b41d61d66b6be16e55c5f76b9ffaefb32957df6a94edc24e55" +content-hash = "c1bf663439296dcfd349b951f3654792e62a1032d6a4c8dcf6162bc05ff8ff5e" diff --git a/pyproject.toml b/pyproject.toml index 80e9450..cfdff8b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,16 +21,18 @@ packages = [{ include = "qibotn", from = "src" }] [tool.poetry.dependencies] python = ">=3.11,<3.14" -qibo = "^0.2.17" +qibo = { git="https://github.com/qiboteam/qibo", branch="expectation"} quimb = { version = "^1.10.0", extras = ["tensor"] } cupy-cuda11x = { version = "^13.1.0", optional = true } cuquantum-python-cu11 = { version = "^24.1.0", optional = true } qmatchatea = { version = "^1.4.3", optional = true } +qiskit = { version = "^1.4.0", optional = true } +qtealeaves = { version = "^1.5.20", optional = true } [tool.poetry.extras] cuda = ["cupy-cuda11x", "cuquantum-python-cu11", "mpi4py"] -qmatchatea = ["qmatchatea"] +qmatchatea = ["qmatchatea", "qtealeaves", "qiskit"] [tool.poetry.group.docs] optional = true diff --git a/src/qibotn/backends/__init__.py b/src/qibotn/backends/__init__.py index 1547a3d..b9d10a1 100644 --- a/src/qibotn/backends/__init__.py +++ b/src/qibotn/backends/__init__.py @@ -4,7 +4,6 @@ from qibo.config import raise_error from qibotn.backends.abstract import QibotnBackend from qibotn.backends.cutensornet import CuTensorNet # pylint: disable=E0401 -from qibotn.backends.quimb import QuimbBackend # pylint: disable=E0401 PLATFORMS = ("cutensornet", "qutensornet", "qmatchatea") @@ -13,7 +12,7 @@ class MetaBackend: """Meta-backend class which takes care of loading the qibotn backends.""" @staticmethod - def load(platform: str, runcard: dict = None) -> QibotnBackend: + def load(platform: str, runcard: dict = None, **kwargs) -> QibotnBackend: """Loads the backend. Args: @@ -26,7 +25,13 @@ class MetaBackend: if platform == "cutensornet": # pragma: no cover return CuTensorNet(runcard) elif platform == "quimb": # pragma: no cover - return QuimbBackend(runcard) + import qibotn.backends.quimb as qmb + + quimb_backend = kwargs.get("quimb_backend", "numpy") + contraction_optimizer = kwargs.get("contraction_optimizer", "auto-hq") + return qmb.BACKENDS[quimb_backend]( + quimb_backend=quimb_backend, contraction_optimizer=contraction_optimizer + ) elif platform == "qmatchatea": # pragma: no cover from qibotn.backends.qmatchatea import QMatchaTeaBackend diff --git a/src/qibotn/backends/qmatchatea.py b/src/qibotn/backends/qmatchatea.py index 27239ec..a5261f4 100644 --- a/src/qibotn/backends/qmatchatea.py +++ b/src/qibotn/backends/qmatchatea.py @@ -23,6 +23,9 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend): self.name = "qibotn" self.platform = "qmatchatea" + # Default precision + self.precision = "double" + # Set default configurations self.configure_tn_simulation() self._setup_backend_specifics() @@ -87,7 +90,6 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend): # TODO: once MPI is available for Python, integrate it here self.qmatchatea_backend = qmatchatea.QCBackend( - backend="PY", # The only alternative is Fortran, but we use Python here precision=qmatchatea_precision, device=qmatchatea_device, ansatz=self.ansatz, diff --git a/src/qibotn/backends/quimb.py b/src/qibotn/backends/quimb.py index fdbb387..b2199d7 100644 --- a/src/qibotn/backends/quimb.py +++ b/src/qibotn/backends/quimb.py @@ -1,136 +1,387 @@ from collections import Counter +from typing import Optional +import quimb as qu import quimb.tensor as qtn -from qibo.backends import NumpyBackend from qibo.config import raise_error -from qibo.result import QuantumState +from qibo.gates.abstract import ParametrizedGate +from qibo.models import Circuit from qibotn.backends.abstract import QibotnBackend from qibotn.result import TensorNetworkResult +GATE_MAP = { + "h": "H", + "x": "X", + "y": "Y", + "z": "Z", + "s": "S", + "t": "T", + "rx": "RX", + "ry": "RY", + "rz": "RZ", + "u3": "U3", + "cx": "CX", + "cnot": "CNOT", + "cy": "CY", + "cz": "CZ", + "iswap": "ISWAP", + "swap": "SWAP", + "ccx": "CCX", + "ccy": "CCY", + "ccz": "CCZ", + "toffoli": "TOFFOLI", + "cswap": "CSWAP", + "fredkin": "FREDKIN", + "fsim": "fsim", + "measure": "measure", +} -class QuimbBackend(QibotnBackend, NumpyBackend): - def __init__(self): - super().__init__() +def __init__(self, quimb_backend="numpy", contraction_optimizer="auto-hq"): + super(self.__class__, self).__init__() - self.name = "qibotn" - self.platform = "quimb" + self.name = "qibotn" + self.platform = "quimb" + self.backend = quimb_backend - self.configure_tn_simulation() - self.setup_backend_specifics() + self.ansatz = None + self.max_bond_dimension = None + self.svd_cutoff = None + self.n_most_frequent_states = None - def configure_tn_simulation( - self, - ansatz: str = "MPS", - max_bond_dimension: int = 10, - n_most_frequent_states: int = 100, - ): - """ - Configure tensor network simulation. + self.configure_tn_simulation() + self.setup_backend_specifics( + quimb_backend=quimb_backend, contractions_optimizer=contraction_optimizer + ) - Args: - ansatz : str, optional - The tensor network ansatz to use. Currently, only "MPS" is supported. Default is "MPS". + +def configure_tn_simulation( + self, + ansatz: str = "mps", + max_bond_dimension: Optional[int] = None, + svd_cutoff: Optional[float] = 1e-10, + n_most_frequent_states: int = 100, +): + """ + Configure tensor network simulation. + + Args: + ansatz : str, optional + The tensor network ansatz to use. Default is `None` and, in this case, a + generic Circuit Quimb class is used. max_bond_dimension : int, optional - The maximum bond dimension for the MPS ansatz. Default is 10. + The maximum bond dimension for the MPS ansatz. Default is 10. - Notes: - - The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported. - - The `max_bond_dimension` parameter controls the maximum allowed bond dimension for the MPS ansatz. - """ - self.ansatz = ansatz - self.max_bond_dimension = max_bond_dimension - self.n_most_frequent_states = n_most_frequent_states + Notes: + - The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported. + - The `max_bond_dimension` parameter controls the maximum allowed bond dimension for the MPS ansatz. + """ + self.ansatz = ansatz + self.max_bond_dimension = max_bond_dimension + self.svd_cutoff = svd_cutoff + self.n_most_frequent_states = n_most_frequent_states - def setup_backend_specifics(self, qimb_backend="numpy"): - """Setup backend specifics. - Args: - qimb_backend: str - The backend to use for the quimb tensor network simulation. - """ - self.backend = qimb_backend - def execute_circuit( - self, - circuit, - initial_state=None, - nshots=None, - return_array=False, - **prob_kwargs, - ): - """ - Execute a quantum circuit using the specified tensor network ansatz and initial state. +@property +def circuit_ansatz(self): + if self.ansatz == "mps": + return qtn.CircuitMPS + return qtn.Circuit - Args: - circuit : QuantumCircuit - The quantum circuit to be executed. - initial_state : array-like, optional - The initial state of the quantum system. Only supported for Matrix Product States (MPS) ansatz. - nshots : int, optional - The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used. - return_array : bool, optional - If True, returns the statevector as a dense array. Default is False. - n_most_frequent_states : int, optional - The number of most frequent computational basis states to return. Default is 100. - **prob_kwargs : dict, optional - Additional keyword arguments for probability computation (currently unused). - Returns: - TensorNetworkResult - An object containing the results of the circuit execution, including: - - nqubits: Number of qubits in the circuit. - - backend: The backend used for execution. - - measures: The measurement frequencies if nshots is specified, otherwise None. - - measured_probabilities: A dictionary of computational basis states and their probabilities. - - prob_type: The type of probability computation used (currently "default"). - - statevector: The final statevector as a dense array if return_array is True, otherwise None. +def setup_backend_specifics( + self, quimb_backend="numpy", contractions_optimizer="auto-hq" +): + """Setup backend specifics. + Args: + quimb_backend: str + The backend to use for the quimb tensor network simulation. + contractions_optimizer: str, optional + The contractions_optimizer to use for the quimb tensor network simulation. + """ + # this is not really working because it does not change the inheritance + if quimb_backend == "jax": + import jax.numpy as jnp - Raises: - ValueError - If an initial state is provided but the ansatz is not "MPS". + self.np = jnp + elif quimb_backend == "numpy": + import numpy as np - Notes: - - The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported. - - If `initial_state` is provided, it must be compatible with the MPS ansatz. - - The `nshots` parameter enables sampling from the circuit's output distribution. If not specified, the full statevector is computed. - """ + self.np = np + elif quimb_backend == "torch": + import torch - if initial_state is not None and self.ansatz == "MPS": - initial_state = qtn.tensor_1d.MatrixProductState.from_dense( - initial_state, 2 - ) # 2 is the physical dimension - elif initial_state is not None: - raise_error( - ValueError, "Initial state not None supported only for MPS ansatz." - ) + self.np = torch + else: + raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}") - circ_ansatz = ( - qtn.circuit.CircuitMPS if self.ansatz == "MPS" else qtn.circuit.Circuit - ) - circ_quimb = circ_ansatz.from_openqasm2_str( - circuit.to_qasm(), psi0=initial_state - ) + self.backend = quimb_backend + self.contractions_optimizer = contractions_optimizer - frequencies = Counter(circ_quimb.sample(nshots)) if nshots is not None else None + +def execute_circuit( + self, + circuit: Circuit, + initial_state=None, + nshots=None, + return_array=False, +): + """ + Execute a quantum circuit using the specified tensor network ansatz and initial state. + + Args: + circuit : QuantumCircuit + The quantum circuit to be executed. + initial_state : array-like, optional + The initial state of the quantum system. Only supported for Matrix Product States (MPS) ansatz. + nshots : int, optional + The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used. + return_array : bool, optional + If True, returns the statevector as a dense array. Default is False. + + Returns: + TensorNetworkResult + An object containing the results of the circuit execution, including: + - nqubits: Number of qubits in the circuit. + - backend: The backend used for execution. + - measures: The measurement frequencies if nshots is specified, otherwise None. + - measured_probabilities: A dictionary of computational basis states and their probabilities. + - prob_type: The type of probability computation used (currently "default"). + - statevector: The final statevector as a dense array if return_array is True, otherwise None. + + Raises: + ValueError + If an initial state is provided but the ansatz is not "MPS". + + Notes: + - The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported. + - If `initial_state` is provided, it must be compatible with the MPS ansatz. + - The `nshots` parameter enables sampling from the circuit's output distribution. If not specified, the full statevector is computed. + """ + if initial_state is not None and self.ansatz == "MPS": + initial_state = qtn.tensor_1d.MatrixProductState.from_dense( + initial_state, 2 + ) # 2 is the physical dimension + elif initial_state is not None: + raise_error(ValueError, "Initial state not None supported only for MPS ansatz.") + + circ_quimb = self.circuit_ansatz.from_openqasm2_str( + circuit.to_qasm(), psi0=initial_state + ) + + if nshots: + frequencies = Counter(circ_quimb.sample(nshots)) main_frequencies = { state: count for state, count in frequencies.most_common(self.n_most_frequent_states) } - computational_states = [state for state in main_frequencies.keys()] + computational_states = list(main_frequencies.keys()) amplitudes = { state: circ_quimb.amplitude(state) for state in computational_states } measured_probabilities = { state: abs(amplitude) ** 2 for state, amplitude in amplitudes.items() } + else: + frequencies = None + measured_probabilities = None - statevector = circ_quimb.to_dense() if return_array else None - return TensorNetworkResult( - nqubits=circuit.nqubits, - backend=self, - measures=frequencies, - measured_probabilities=measured_probabilities, - prob_type="default", - statevector=statevector, + statevector = ( + circ_quimb.to_dense(backend=self.backend, optimize=self.contractions_optimizer) + if return_array + else None + ) + return TensorNetworkResult( + nqubits=circuit.nqubits, + backend=self, + measures=frequencies, + measured_probabilities=measured_probabilities, + prob_type="default", + statevector=statevector, + ) + + +def expectation_observable_symbolic( + self, circuit, operators_list, sites_list, coeffs_list, nqubits +): + """ + Compute the expectation value of a symbolic Hamiltonian on a quantum circuit using tensor network contraction. + This method takes a Qibo circuit, converts it to a Quimb tensor network circuit, and evaluates the expectation value + of a Hamiltonian specified by three lists of strings: operators, sites, and coefficients. + The expectation value is computed by summing the contributions from each term in the Hamiltonian, where each term's + expectation is calculated using Quimb's `local_expectation` function. + Each operator string must act on all different qubits, i.e., for each term, the corresponding sites tuple must contain unique qubit indices. + Example: operators_list = ['xyz', 'xyz'], sites_list = [(1,2,3), (1,2,3)], coeffs_list = [1, 2] + + + Parameters + ---------- + circuit : qibo.models.Circuit + The quantum circuit to evaluate, provided as a Qibo circuit object. + operators_list : list of str + List of operator strings representing the symbolic Hamiltonian terms. + sites_list : list of tuple of int + Tuples each specifying the qubits (sites) the corresponding operator acts on. + coeffs_list : list of real/complex + The coefficients for each Hamiltonian term. + Returns + ------- + float + The real part of the expectation value of the Hamiltonian on the given circuit state. + """ + # Validate that no term acts multiple times on the same qubit (no repeated indices in a sites tuple) + for sites in sites_list: + if len(sites) != len(set(sites)): + raise_error( + ValueError, + f"Invalid Hamiltonian term sites {sites}: repeated qubit indices are not allowed " + "within a single term (e.g. (0,0,0) is invalid).", + ) + quimb_circuit = self._qibo_circuit_to_quimb( + circuit, + quimb_circuit_type=self.circuit_ansatz, + gate_opts={"max_bond": self.max_bond_dimension, "cutoff": self.svd_cutoff}, + ) + + expectation_value = 0.0 + for opstr, sites, coeff in zip(operators_list, sites_list, coeffs_list): + + ops = self._string_to_quimb_operator(opstr) + coeff = coeff.real + + exp_values = quimb_circuit.local_expectation( + ops, + where=sites, + backend=self.backend, + optimize=self.contractions_optimizer, + simplify_sequence="R", ) + + expectation_value = expectation_value + coeff * exp_values + + return self.np.real(expectation_value) + + +def _qibo_circuit_to_quimb( + self, qibo_circ, quimb_circuit_type=qtn.Circuit, **circuit_kwargs +): + """ + Convert a Qibo Circuit to a Quimb Circuit. Measurement gates are ignored. If are given gates not supported by Quimb, an error is raised. + + Parameters + ---------- + qibo_circ : qibo.models.circuit.Circuit + The circuit to convert. + quimb_circuit_type : type + The Quimb circuit class to use (Circuit, CircuitMPS, etc). + circuit_kwargs : dict + Extra arguments to pass to the Quimb circuit constructor. + + Returns + ------- + circ : quimb.tensor.circuit.Circuit + The converted circuit. + """ + nqubits = qibo_circ.nqubits + circ = quimb_circuit_type(nqubits, **circuit_kwargs) + + for gate in qibo_circ.queue: + gate_name = getattr(gate, "name", None) + quimb_gate_name = GATE_MAP.get(gate_name, None) + if quimb_gate_name == "measure": + continue + if quimb_gate_name is None: + raise_error(ValueError, f"Gate {gate_name} not supported in Quimb backend.") + + params = getattr(gate, "parameters", ()) + qubits = getattr(gate, "qubits", ()) + + is_parametrized = isinstance(gate, ParametrizedGate) and getattr( + gate, "trainable", True + ) + if is_parametrized: + circ.apply_gate( + quimb_gate_name, *params, *qubits, parametrized=is_parametrized + ) + else: + circ.apply_gate( + quimb_gate_name, + *params, + *qubits, + ) + return circ + + +def _string_to_quimb_operator(self, op_str): + """ + Convert a Pauli string (e.g. 'xzy') to a Quimb operator using '&' chaining. + + Parameters + ---------- + op_str : str + A string like 'xzy', where each character is one of 'x', 'y', 'z', 'i'. + + Returns + ------- + qu_op : quimb.Qarray + The corresponding Quimb operator. + """ + op_str = op_str.lower() + op = qu.pauli(op_str[0]) + for c in op_str[1:]: + op = op & qu.pauli(c) + return op + + +CLASSES_ROOTS = {"numpy": "Numpy", "torch": "PyTorch", "jax": "Jax"} + +METHODS = { + "__init__": __init__, + "configure_tn_simulation": configure_tn_simulation, + "setup_backend_specifics": setup_backend_specifics, + "execute_circuit": execute_circuit, + "expectation_observable_symbolic": expectation_observable_symbolic, + "_qibo_circuit_to_quimb": _qibo_circuit_to_quimb, + "_string_to_quimb_operator": _string_to_quimb_operator, + "circuit_ansatz": circuit_ansatz, +} + + +def _generate_backend(quimb_backend: str = "numpy"): + bases = (QibotnBackend,) + + if quimb_backend == "numpy": + from qibo.backends import NumpyBackend + + bases += (NumpyBackend,) + elif quimb_backend == "torch": + from qiboml.backends import PyTorchBackend + + bases += (PyTorchBackend,) + elif quimb_backend == "jax": + from qiboml.backends import JaxBackend + + bases += (JaxBackend,) + else: + raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}") + + return type(f"Quimb{CLASSES_ROOTS[quimb_backend]}Backend", bases, METHODS) + + +BACKENDS = {} +for k, v in CLASSES_ROOTS.items(): + backend_name = f"Quimb{v}Backend" + try: + backend = _generate_backend(k) + BACKENDS[k] = backend + globals()[backend_name] = backend + except ImportError: + continue + + +def __getattr__(name): + try: + return BACKENDS[name] + except KeyError: + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") from None