jaunatisblue/final-qibotn
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #112 from mattia-robbiano/main

Browse Source 
Quimb backend: refactor and implementation of expectation function
This commit is contained in:
BrunoLiegiBastonLiegi
2025-10-06 09:34:20 +02:00
committed by GitHub
parent a632c32a65 789963312f
commit 9eeee4a95f
6 changed files with 2141 additions and 1210 deletions
Download Patch File Download Diff File Expand all files Collapse all files

543
examples/quimb_intro/quimb_introduction.ipynb Normal file
View File

@@ -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
}

2500
poetry.lock generated
View File

File diff suppressed because it is too large Load Diff

6
pyproject.toml
View File

@@ -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

10
src/qibotn/backends/__init__.py
View File

@@ -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

4
src/qibotn/backends/qmatchatea.py
View File

@@ -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,

266
src/qibotn/backends/quimb.py
View File

@@ -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,
):
"""
@@ -31,7 +69,8 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
Args:
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
The maximum bond dimension for the MPS ansatz. Default is 10.
@@ -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
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
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)