Merge pull request #113 from qiboteam/expectation
Added expectation value calculation with Quimb and general refactor.
This commit is contained in:
Mattia Robbiano
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examples/quimb_intro/quimb_introduction.ipynb
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572
examples/quimb_intro/quimb_introduction.ipynb
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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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]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
2868
poetry.lock
generated
2868
poetry.lock
generated
File diff suppressed because it is too large
Load Diff
@@ -21,16 +21,18 @@ packages = [{ include = "qibotn", from = "src" }]
|
|||||||
|
|
||||||
[tool.poetry.dependencies]
|
[tool.poetry.dependencies]
|
||||||
python = ">=3.11,<3.14"
|
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"] }
|
quimb = { version = "^1.10.0", extras = ["tensor"] }
|
||||||
cupy-cuda11x = { version = "^13.1.0", optional = true }
|
cupy-cuda11x = { version = "^13.1.0", optional = true }
|
||||||
cuquantum-python-cu11 = { version = "^24.1.0", optional = true }
|
cuquantum-python-cu11 = { version = "^24.1.0", optional = true }
|
||||||
qmatchatea = { version = "^1.4.3", 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]
|
[tool.poetry.extras]
|
||||||
cuda = ["cupy-cuda11x", "cuquantum-python-cu11", "mpi4py"]
|
cuda = ["cupy-cuda11x", "cuquantum-python-cu11", "mpi4py"]
|
||||||
qmatchatea = ["qmatchatea"]
|
qmatchatea = ["qmatchatea", "qtealeaves", "qiskit"]
|
||||||
|
|
||||||
[tool.poetry.group.docs]
|
[tool.poetry.group.docs]
|
||||||
optional = true
|
optional = true
|
||||||
|
|||||||
@@ -4,7 +4,6 @@ from qibo.config import raise_error
|
|||||||
|
|
||||||
from qibotn.backends.abstract import QibotnBackend
|
from qibotn.backends.abstract import QibotnBackend
|
||||||
from qibotn.backends.cutensornet import CuTensorNet # pylint: disable=E0401
|
from qibotn.backends.cutensornet import CuTensorNet # pylint: disable=E0401
|
||||||
from qibotn.backends.quimb import QuimbBackend # pylint: disable=E0401
|
|
||||||
|
|
||||||
PLATFORMS = ("cutensornet", "qutensornet", "qmatchatea")
|
PLATFORMS = ("cutensornet", "qutensornet", "qmatchatea")
|
||||||
|
|
||||||
@@ -13,7 +12,7 @@ class MetaBackend:
|
|||||||
"""Meta-backend class which takes care of loading the qibotn backends."""
|
"""Meta-backend class which takes care of loading the qibotn backends."""
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def load(platform: str, runcard: dict = None) -> QibotnBackend:
|
def load(platform: str, runcard: dict = None, **kwargs) -> QibotnBackend:
|
||||||
"""Loads the backend.
|
"""Loads the backend.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
@@ -26,7 +25,13 @@ class MetaBackend:
|
|||||||
if platform == "cutensornet": # pragma: no cover
|
if platform == "cutensornet": # pragma: no cover
|
||||||
return CuTensorNet(runcard)
|
return CuTensorNet(runcard)
|
||||||
elif platform == "quimb": # pragma: no cover
|
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
|
elif platform == "qmatchatea": # pragma: no cover
|
||||||
from qibotn.backends.qmatchatea import QMatchaTeaBackend
|
from qibotn.backends.qmatchatea import QMatchaTeaBackend
|
||||||
|
|
||||||
|
|||||||
@@ -23,6 +23,9 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
|
|||||||
self.name = "qibotn"
|
self.name = "qibotn"
|
||||||
self.platform = "qmatchatea"
|
self.platform = "qmatchatea"
|
||||||
|
|
||||||
|
# Default precision
|
||||||
|
self.precision = "double"
|
||||||
|
|
||||||
# Set default configurations
|
# Set default configurations
|
||||||
self.configure_tn_simulation()
|
self.configure_tn_simulation()
|
||||||
self._setup_backend_specifics()
|
self._setup_backend_specifics()
|
||||||
@@ -87,7 +90,6 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
|
|||||||
|
|
||||||
# TODO: once MPI is available for Python, integrate it here
|
# TODO: once MPI is available for Python, integrate it here
|
||||||
self.qmatchatea_backend = qmatchatea.QCBackend(
|
self.qmatchatea_backend = qmatchatea.QCBackend(
|
||||||
backend="PY", # The only alternative is Fortran, but we use Python here
|
|
||||||
precision=qmatchatea_precision,
|
precision=qmatchatea_precision,
|
||||||
device=qmatchatea_device,
|
device=qmatchatea_device,
|
||||||
ansatz=self.ansatz,
|
ansatz=self.ansatz,
|
||||||
|
|||||||
@@ -1,29 +1,66 @@
|
|||||||
from collections import Counter
|
from collections import Counter
|
||||||
|
from typing import Optional
|
||||||
|
|
||||||
|
import quimb as qu
|
||||||
import quimb.tensor as qtn
|
import quimb.tensor as qtn
|
||||||
from qibo.backends import NumpyBackend
|
|
||||||
from qibo.config import raise_error
|
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.backends.abstract import QibotnBackend
|
||||||
from qibotn.result import TensorNetworkResult
|
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):
|
def __init__(self, quimb_backend="numpy", contraction_optimizer="auto-hq"):
|
||||||
super().__init__()
|
super(self.__class__, self).__init__()
|
||||||
|
|
||||||
self.name = "qibotn"
|
self.name = "qibotn"
|
||||||
self.platform = "quimb"
|
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.configure_tn_simulation()
|
||||||
self.setup_backend_specifics()
|
self.setup_backend_specifics(
|
||||||
|
quimb_backend=quimb_backend, contractions_optimizer=contraction_optimizer
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
def configure_tn_simulation(
|
def configure_tn_simulation(
|
||||||
self,
|
self,
|
||||||
ansatz: str = "MPS",
|
ansatz: str = "mps",
|
||||||
max_bond_dimension: int = 10,
|
max_bond_dimension: Optional[int] = None,
|
||||||
|
svd_cutoff: Optional[float] = 1e-10,
|
||||||
n_most_frequent_states: int = 100,
|
n_most_frequent_states: int = 100,
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
@@ -31,7 +68,8 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
|
|||||||
|
|
||||||
Args:
|
Args:
|
||||||
ansatz : str, optional
|
ansatz : str, optional
|
||||||
The tensor network ansatz to use. Currently, only "MPS" is supported. Default is "MPS".
|
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
|
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.
|
||||||
|
|
||||||
@@ -41,23 +79,53 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
|
|||||||
"""
|
"""
|
||||||
self.ansatz = ansatz
|
self.ansatz = ansatz
|
||||||
self.max_bond_dimension = max_bond_dimension
|
self.max_bond_dimension = max_bond_dimension
|
||||||
|
self.svd_cutoff = svd_cutoff
|
||||||
self.n_most_frequent_states = n_most_frequent_states
|
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.
|
"""Setup backend specifics.
|
||||||
Args:
|
Args:
|
||||||
qimb_backend: str
|
quimb_backend: str
|
||||||
The backend to use for the quimb tensor network simulation.
|
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(
|
def execute_circuit(
|
||||||
self,
|
self,
|
||||||
circuit,
|
circuit: Circuit,
|
||||||
initial_state=None,
|
initial_state=None,
|
||||||
nshots=None,
|
nshots=None,
|
||||||
return_array=False,
|
return_array=False,
|
||||||
**prob_kwargs,
|
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
Execute a quantum circuit using the specified tensor network ansatz and initial state.
|
Execute a quantum circuit using the specified tensor network ansatz and initial state.
|
||||||
@@ -71,10 +139,6 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
|
|||||||
The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used.
|
The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used.
|
||||||
return_array : bool, optional
|
return_array : bool, optional
|
||||||
If True, returns the statevector as a dense array. Default is False.
|
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:
|
Returns:
|
||||||
TensorNetworkResult
|
TensorNetworkResult
|
||||||
@@ -95,37 +159,39 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
|
|||||||
- If `initial_state` is provided, it must be compatible with the MPS ansatz.
|
- 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.
|
- 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":
|
if initial_state is not None and self.ansatz == "MPS":
|
||||||
initial_state = qtn.tensor_1d.MatrixProductState.from_dense(
|
initial_state = qtn.tensor_1d.MatrixProductState.from_dense(
|
||||||
initial_state, 2
|
initial_state, 2
|
||||||
) # 2 is the physical dimension
|
) # 2 is the physical dimension
|
||||||
elif initial_state is not None:
|
elif initial_state is not None:
|
||||||
raise_error(
|
raise_error(ValueError, "Initial state not None supported only for MPS ansatz.")
|
||||||
ValueError, "Initial state not None supported only for MPS ansatz."
|
|
||||||
)
|
|
||||||
|
|
||||||
circ_ansatz = (
|
circ_quimb = self.circuit_ansatz.from_openqasm2_str(
|
||||||
qtn.circuit.CircuitMPS if self.ansatz == "MPS" else qtn.circuit.Circuit
|
|
||||||
)
|
|
||||||
circ_quimb = circ_ansatz.from_openqasm2_str(
|
|
||||||
circuit.to_qasm(), psi0=initial_state
|
circuit.to_qasm(), psi0=initial_state
|
||||||
)
|
)
|
||||||
|
|
||||||
frequencies = Counter(circ_quimb.sample(nshots)) if nshots is not None else None
|
if nshots:
|
||||||
|
frequencies = Counter(circ_quimb.sample(nshots))
|
||||||
main_frequencies = {
|
main_frequencies = {
|
||||||
state: count
|
state: count
|
||||||
for state, count in frequencies.most_common(self.n_most_frequent_states)
|
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 = {
|
amplitudes = {
|
||||||
state: circ_quimb.amplitude(state) for state in computational_states
|
state: circ_quimb.amplitude(state) for state in computational_states
|
||||||
}
|
}
|
||||||
measured_probabilities = {
|
measured_probabilities = {
|
||||||
state: abs(amplitude) ** 2 for state, amplitude in amplitudes.items()
|
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(
|
return TensorNetworkResult(
|
||||||
nqubits=circuit.nqubits,
|
nqubits=circuit.nqubits,
|
||||||
backend=self,
|
backend=self,
|
||||||
@@ -134,3 +200,188 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
|
|||||||
prob_type="default",
|
prob_type="default",
|
||||||
statevector=statevector,
|
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
|
||||||
|
|||||||
Reference in New Issue
Block a user