diff --git a/examples/quimb_intro/quimb_introduction.ipynb b/examples/quimb_intro/quimb_introduction.ipynb new file mode 100644 index 0000000..70fdf45 --- /dev/null +++ b/examples/quimb_intro/quimb_introduction.ipynb @@ -0,0 +1,543 @@ +{ + "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": 2, + "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": 3, + "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(qimb_backend=\"jax\")" + ] + }, + { + "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/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({'1010': 9,\n", + " '0100': 8,\n", + " '1101': 15,\n", + " '1011': 4,\n", + " '1111': 12,\n", + " '1000': 13,\n", + " '0000': 8,\n", + " '0010': 6,\n", + " '0011': 6,\n", + " '0101': 8,\n", + " '1110': 5,\n", + " '0110': 5,\n", + " '0111': 1}),\n", + " 'measured_probabilities': {'1101': np.float64(0.12331159869893256),\n", + " '1000': np.float64(0.11330883548333587),\n", + " '1111': np.float64(0.10184806171791962),\n", + " '1010': np.float64(0.03872758515126756),\n", + " '0100': np.float64(0.07142939529687138),\n", + " '0000': np.float64(0.08390937969317269),\n", + " '0101': np.float64(0.05622305772698622),\n", + " '0010': np.float64(0.09466860481989385),\n", + " '0011': np.float64(0.07571277233522114),\n", + " '1110': np.float64(0.07174919872959985),\n", + " '0110': np.float64(0.05146064807369214),\n", + " '1011': np.float64(0.053499396925872744),\n", + " '0111': np.float64(0.04029185074729259)},\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", + " {'1101': np.float64(0.12331159869893256), '1000': np.float64(0.11330883548333587), '1111': np.float64(0.10184806171791962), '1010': np.float64(0.03872758515126756), '0100': np.float64(0.07142939529687138), '0000': np.float64(0.08390937969317269), '0101': np.float64(0.05622305772698622), '0010': np.float64(0.09466860481989385), '0011': np.float64(0.07571277233522114), '1110': np.float64(0.07174919872959985), '0110': np.float64(0.05146064807369214), '1011': np.float64(0.053499396925872744), '0111': np.float64(0.04029185074729259)}\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": "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", + " qimb_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": 19, + "id": "b2a0decb", + "metadata": {}, + "outputs": [], + "source": [ + "# define Hamiltonian\n", + "operators = [\"xzy\", \"yxzy\", \"zy\"]\n", + "qubits = [\"011\", \"0112\", \"01\"]\n", + "coefficients = [\"1\", \"2\", \"j\"]\n", + "hamiltonian = (operators, qubits, coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "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: 0.0\n", + "Elapsed time: 0.1071 seconds\n" + ] + } + ], + "source": [ + "start = time.time()\n", + "expval = quimb_backend.expectation(\n", + " circuit=circuit,\n", + " operators_list=hamiltonian[0],\n", + " sites_list=hamiltonian[1],\n", + " coeffs_list=hamiltonian[2]\n", + " )\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": 22, + "id": "fb1436c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expectation value: 1.5\n", + "Elapsed time: 0.0501 seconds\n" + ] + } + ], + "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", + "sym_hamiltonian = hamiltonians.SymbolicHamiltonian(form)\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": "stdout", + "output_type": "stream", + "text": [ + "[ 8.19939339e-10 -3.14190913e-08 -2.99498648e-09 -1.03641796e-07\n", + " 8.48652704e-10 1.00297093e-07 -6.75429277e-08 -9.78565140e-09\n", + " -5.11915417e-08 1.29225235e-08 -7.44280655e-08 -3.49115048e-08\n", + " -4.98508879e-09 6.80729357e-08 -3.29755920e-08 4.20008526e-08\n", + " -2.89742630e-08 1.18602941e-07 -2.88252178e-08 5.57985391e-09\n", + " -3.17434115e-08 -1.03342952e-08 1.34079716e-08 -7.05437886e-09\n", + " -4.34059650e-08 -2.18019203e-08 -5.36932561e-08 -6.38544009e-08\n", + " 5.85312279e-08 8.45709067e-08 -1.12777876e-09 -6.41545981e-08\n", + " 7.25317406e-08 4.10035668e-08 -1.29046382e-08 6.07501676e-08]\n" + ] + } + ], + "source": [ + "def f(circuit, hamiltonian, params):\n", + " circuit.set_parameters(params)\n", + " return quimb_backend.expectation(\n", + " circuit=circuit,\n", + " operators_list=hamiltonian[0],\n", + " sites_list=hamiltonian[1],\n", + " coeffs_list=hamiltonian[2]\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" + ] + } + ], + "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 +} diff --git a/poetry.lock b/poetry.lock index 3d2448d..41fb100 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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"sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, - {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, + {file = "wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1"}, + {file = "wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605"}, ] [extras] 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..4ec932c 100644 --- a/src/qibotn/backends/__init__.py +++ b/src/qibotn/backends/__init__.py @@ -4,7 +4,7 @@ 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 +from qibotn.backends.quimb import QuimbBackend PLATFORMS = ("cutensornet", "qutensornet", "qmatchatea") @@ -13,7 +13,7 @@ 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 +26,11 @@ class MetaBackend: if platform == "cutensornet": # pragma: no cover return CuTensorNet(runcard) elif platform == "quimb": # pragma: no cover - return QuimbBackend(runcard) + quimb_backend = kwargs.get("quimb_backend", "numpy") + contraction_optimizer = kwargs.get("contraction_optimizer", "auto-hq") + return QuimbBackend( + 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..667d4fe 100644 --- a/src/qibotn/backends/quimb.py +++ b/src/qibotn/backends/quimb.py @@ -1,29 +1,67 @@ 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", # TODO: check + "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__() +if not __name__ == "__main__": + + def __init__(self, quimb_backend="numpy", contraction_optimizer="auto-hq"): + super(self.__class__, self).__init__() self.name = "qibotn" self.platform = "quimb" + self.backend = quimb_backend + + self.ansatz = None + self.max_bond_dimension = None + self.svd_cutoff = None + self.n_most_frequent_states = None self.configure_tn_simulation() - self.setup_backend_specifics() + self.setup_backend_specifics( + quimb_backend=quimb_backend, contractions_optimizer=contraction_optimizer + ) def configure_tn_simulation( self, - ansatz: str = "MPS", - max_bond_dimension: int = 10, + ansatz: str = "mps", + max_bond_dimension: Optional[int] = None, + svd_cutoff: Optional[float] = 1e-10, n_most_frequent_states: int = 100, ): """ @@ -31,8 +69,9 @@ class QuimbBackend(QibotnBackend, NumpyBackend): Args: ansatz : str, optional - The tensor network ansatz to use. Currently, only "MPS" is supported. Default is "MPS". - max_bond_dimension : int, 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. Notes: @@ -41,23 +80,50 @@ class QuimbBackend(QibotnBackend, NumpyBackend): """ 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"): + @property + def circuit_ansatz(self): + if self.ansatz == "mps": + return qtn.CircuitMPS + return qtn.Circuit + + def setup_backend_specifics( + self, quimb_backend="numpy", contractions_optimizer="auto-hq" + ): """Setup backend specifics. Args: qimb_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. """ - self.backend = qimb_backend + # this is not really working because it does not change the inheritance + if quimb_backend == "jax": + import jax.numpy as jnp + + self.np = jnp + elif quimb_backend == "numpy": + import numpy as np + + self.np = np + elif quimb_backend == "torch": + import torch + + self.np = torch + else: + raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}") + + self.backend = quimb_backend + self.contractions_optimizer = contractions_optimizer def execute_circuit( self, - circuit, + circuit: Circuit, initial_state=None, nshots=None, return_array=False, - **prob_kwargs, ): """ Execute a quantum circuit using the specified tensor network ansatz and initial state. @@ -71,10 +137,6 @@ class QuimbBackend(QibotnBackend, NumpyBackend): 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 @@ -95,7 +157,6 @@ class QuimbBackend(QibotnBackend, NumpyBackend): - 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 @@ -105,27 +166,34 @@ class QuimbBackend(QibotnBackend, NumpyBackend): ValueError, "Initial state not None supported only for MPS ansatz." ) - circ_ansatz = ( - qtn.circuit.CircuitMPS if self.ansatz == "MPS" else qtn.circuit.Circuit - ) - circ_quimb = circ_ansatz.from_openqasm2_str( + circ_quimb = self.circuit_ansatz.from_openqasm2_str( circuit.to_qasm(), psi0=initial_state ) - frequencies = Counter(circ_quimb.sample(nshots)) if nshots is not None else None - 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()] - amplitudes = { - state: circ_quimb.amplitude(state) for state in computational_states - } - measured_probabilities = { - state: abs(amplitude) ** 2 for state, amplitude in amplitudes.items() - } + 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 = 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 + statevector = ( + circ_quimb.to_dense( + backend=self.backend, optimize=self.contractions_optimizer + ) + if return_array + else None + ) return TensorNetworkResult( nqubits=circuit.nqubits, backend=self, @@ -134,3 +202,151 @@ class QuimbBackend(QibotnBackend, NumpyBackend): prob_type="default", statevector=statevector, ) + + def expectation_observable_symbolic_from_state( + 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. + + 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 str + List of strings, each specifying the qubits (sites) the corresponding operator acts on. + coeffs_list : list of str + List of strings representing the coefficients for each Hamiltonian term. + Returns + ------- + float + The real part of the expectation value of the Hamiltonian on the given circuit state. + """ + 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: + gname = getattr(gate, "name", None) + qname = GATE_MAP.get(gname, None) + if qname == "measure": + continue + if qname is None: + raise_error(ValueError, f"Gate {gname} 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(qname, *params, *qubits, parametrized=is_parametrized) + else: + circ.apply_gate( + qname, + *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 + + +def QuimbBackend( + quimb_backend: str = "numpy", contraction_optimizer="auto-hq" +) -> QibotnBackend: + bases = (QibotnBackend,) + methods = { + "__init__": __init__, + "configure_tn_simulation": configure_tn_simulation, + "setup_backend_specifics": setup_backend_specifics, + "execute_circuit": execute_circuit, + "expectation_observable_symbolic_from_state": expectation_observable_symbolic_from_state, + "_qibo_circuit_to_quimb": _qibo_circuit_to_quimb, + "_string_to_quimb_operator": _string_to_quimb_operator, + "circuit_ansatz": circuit_ansatz, + } + 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("QuimbBackend", bases, methods)(quimb_backend, contraction_optimizer)