diff --git a/.envrc b/.envrc new file mode 100644 index 00000000..01f5f41d --- /dev/null +++ b/.envrc @@ -0,0 +1,9 @@ +if ! has nix_direnv_version || ! nix_direnv_version 2.2.1; then + source_url "https://raw.githubusercontent.com/nix-community/nix-direnv/2.2.1/direnvrc" "sha256-zelF0vLbEl5uaqrfIzbgNzJWGmLzCmYAkInj/LNxvKs=" +fi + +nix_direnv_watch_file flake.nix +nix_direnv_watch_file flake.lock +if ! use flake . --impure; then + echo "devenv could not be built. The devenv environment was not loaded. Make the necessary changes to devenv.nix and hit enter to try again." >&2 +fi diff --git a/.github/workflows/rules.yml b/.github/workflows/rules.yml index d1c06323..4ad1427f 100644 --- a/.github/workflows/rules.yml +++ b/.github/workflows/rules.yml @@ -1,22 +1,38 @@ # A single CI script with github workflow name: Tests +env: + CUDA_PATH: + on: + workflow_dispatch: push: pull_request: types: [labeled] jobs: + check: + # job to check cuda availability + runs-on: ubuntu-latest + steps: + - id: step1 + run: echo "test=${{ env.CUDA_PATH != ''}}" >> "$GITHUB_OUTPUT" + - id: step2 + run: echo "test=${{ contains(github.event.pull_request.labels.*.name, 'run-workflow') || github.event_name == 'push' }}" >> "$GITHUB_OUTPUT" + outputs: + cuda_avail: ${{ fromJSON(steps.step1.outputs.test) && fromJSON(steps.step2.outputs.test) }} + build: - if: contains(github.event.pull_request.labels.*.name, 'run-workflow') || github.event_name == 'push' && {{ $CUDA_PATH != '' }} + # job to build + needs: check + if: ${{fromJSON(needs.check.outputs.cuda_avail)}} strategy: - matrix: - os: [ubuntu-latest] - python-version: [3.8, 3.9, "3.10"] - uses: qiboteam/workflows/.github/workflows/rules.yml@main + matrix: + os: [ubuntu-latest] + python-version: [3.8, 3.9, "3.10", "3.11"] + uses: qiboteam/workflows/.github/workflows/rules-poetry.yml@main with: os: ${{ matrix.os }} python-version: ${{ matrix.python-version }} - environment: "qibotn" - pip-extras: "analysis,tests" + poetry-extras: "--with analysis,tests" secrets: inherit diff --git a/.gitignore b/.gitignore index d903da02..7f051b80 100644 --- a/.gitignore +++ b/.gitignore @@ -159,3 +159,4 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +.devenv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e17e9aa9..71b0b4e5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,5 +1,5 @@ -# See https://pre-commit.com for more information -# See https://pre-commit.com/hooks.html for more hooks +ci: + autofix_prs: true repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 @@ -8,10 +8,9 @@ repos: - id: end-of-file-fixer - id: check-yaml - id: check-toml - - id: check-merge-conflict - id: debug-statements - repo: https://github.com/psf/black - rev: 24.2.0 + rev: 24.1.1 hooks: - id: black - repo: https://github.com/pycqa/isort @@ -19,7 +18,31 @@ repos: hooks: - id: isort args: ["--profile", "black"] + - repo: https://github.com/PyCQA/docformatter + rev: v1.7.5 + hooks: + - id: docformatter + additional_dependencies: [tomli] + args: [--in-place, --config, ./pyproject.toml] - repo: https://github.com/asottile/pyupgrade rev: v3.15.1 hooks: - id: pyupgrade + - repo: https://github.com/hadialqattan/pycln + rev: v2.4.0 + hooks: + - id: pycln + args: + - --config=pyproject.toml + - --all + - repo: https://github.com/adamchainz/blacken-docs + rev: 1.16.0 + hooks: + - id: blacken-docs + - repo: https://github.com/pycqa/pydocstyle + rev: 6.3.0 + hooks: + - id: pydocstyle + args: + - --select=D103,D200,D206,D300,D301 + files: ^src/ diff --git a/README.md b/README.md index 4628c1c1..d00f3159 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,118 @@ -Qibotn is the tensor-network translation module for Qibo to support large-scale simulation of quantum circuits and acceleration. +# Qibotn -To get started, `python setup.py install` to install the tools and dependencies. +The tensor network translation module for Qibo to support large-scale simulation of quantum circuits and acceleration. + +## Supported Computation + +Tensor Network Types: + +- Tensornet (TN) +- Matrix Product States (MPS) + +Tensor Network contractions to: + +- dense vectors +- expecation values of given Pauli string + +The supported HPC configurations are: + +- single-node CPU +- single-node GPU or GPUs +- multi-node multi-GPU with Message Passing Interface (MPI) +- multi-node multi-GPU with NVIDIA Collective Communications Library (NCCL) + +Currently, the supported tensor network libraries are: + +- [cuQuantum](https://github.com/NVIDIA/cuQuantum), an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows. +- [quimb](https://quimb.readthedocs.io/en/latest/), an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks. + +## Installation + +To get started: + +```sh +python setup.py install +``` + +to install the tools and dependencies. A few extras are provided, check `setup.py` in +case you need them. + + + +## Sample Codes + +### Single-Node Example + +The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo. + +```py +import numpy as np +from qibo import Circuit, gates +import qibo + +# Below shows how to set the computation_settings +# Note that for MPS_enabled and expectation_enabled parameters the accepted inputs are boolean or a dictionary with the format shown below. +# If computation_settings is not specified, the default setting is used in which all booleans will be False. +# This will trigger the dense vector computation of the tensornet. + +computation_settings = { + "MPI_enabled": False, + "MPS_enabled": { + "qr_method": False, + "svd_method": { + "partition": "UV", + "abs_cutoff": 1e-12, + }, + }, + "NCCL_enabled": False, + "expectation_enabled": False, +} + + +qibo.set_backend( + backend="qibotn", platform="cutensornet", runcard=computation_settings +) # cuQuantum +# qibo.set_backend(backend="qibotn", platform="QuimbBackend", runcard=computation_settings) #quimb + + +# Construct the circuit +c = Circuit(2) +# Add some gates +c.add(gates.H(0)) +c.add(gates.H(1)) + +# Execute the circuit and obtain the final state +result = c() + +print(result.state()) +``` + +Other examples of setting the computation_settings + +```py +# Expectation computation with specific Pauli String pattern +computation_settings = { + "MPI_enabled": False, + "MPS_enabled": False, + "NCCL_enabled": False, + "expectation_enabled": { + "pauli_string_pattern": "IXZ", + }, +} + +# Dense vector computation using multi node through MPI +computation_settings = { + "MPI_enabled": True, + "MPS_enabled": False, + "NCCL_enabled": False, + "expectation_enabled": False, +} +``` + +### Multi-Node Example + +Multi-node is enabled by setting either the MPI or NCCL enabled flag to True in the computation settings. Below shows the script to launch on 2 nodes with 2 GPUs each. $node_list contains the IP of the nodes assigned. + +```sh +mpirun -n 4 -hostfile $node_list python test.py +``` diff --git a/flake.lock b/flake.lock new file mode 100644 index 00000000..0f48ef05 --- /dev/null +++ b/flake.lock @@ -0,0 +1,323 @@ +{ + "nodes": { + "devenv": { + "inputs": { + "flake-compat": "flake-compat", + "nix": "nix", + "nixpkgs": "nixpkgs", + "pre-commit-hooks": "pre-commit-hooks" + }, + "locked": { + "lastModified": 1707004164, + "narHash": "sha256-9Hr8onWtvLk5A8vCEkaE9kxA0D7PR62povFokM1oL5Q=", + "owner": "cachix", + "repo": "devenv", + "rev": "0e68853bb27981a4ffd7a7225b59ed84f7180fc7", + "type": "github" + }, + "original": { + "owner": "cachix", + "repo": "devenv", + "type": "github" + } + }, + "flake-compat": { + "flake": false, + "locked": { + "lastModified": 1673956053, + "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", + "owner": "edolstra", + "repo": "flake-compat", + "rev": 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"pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[extras] +cuda = ["cupy", "cuquantum-python-cu11"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.9,<3.12" +content-hash = "b912fdfb4ed36b7a8475c7915d99c52af2944305c4c5b1ad9191a4cecf6a3862" diff --git a/pyproject.toml b/pyproject.toml index f2546e49..5a259248 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,59 @@ +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "qibotn" +version = "0.0.1" +description = "A tensor-network translation module for Qibo" +authors = ["The Qibo team"] +license = "Apache License 2.0" +readme = "README.md" +homepage = "https://qibo.science/" +repository = "https://github.com/qiboteam/qibotn/" +documentation = "https://qibo.science/docs/qibotn/stable" +keywords = [] +classifiers = [ + "Programming Language :: Python :: 3", + "Topic :: Scientific/Engineering :: Physics", +] +packages = [{ include = "qibotn", from = "src" }] + +[tool.poetry.dependencies] +python = "^3.9,<3.12" +qibo = "^0.2.4" +quimb = { version = "^1.6.0", extras = ["tensor"] } +cupy = { version = "^11.6.0", optional = true } +cuquantum-python-cu11 = { version = "^23.3.0", optional = true } + +[tool.poetry.extras] +cuda = ["cupy", "cuquantum-python-cu11"] + +[tool.poetry.group.dev.dependencies] +ipython = "^7.0.0" + +[tool.poetry.group.tests] +optional = true + +[tool.poetry.group.tests.dependencies] +pytest = "^8.0.0" +pytest-cov = "^4.1.0" +pytest-env = "^1.1.3" + +[tool.poetry.group.analysis] +optional = true + +[tool.poetry.group.analysis.dependencies] +pylint = "^3.0.3" + +[tool.poe.tasks] +test = "pytest" +lint = "pylint src --errors-only" +lint-warnings = "pylint src --exit-zero" + +[tool.pylint.main] +ignored-modules = ["cupy", "cuquantum", "mpi4py"] + [tool.pylint.reports] output-format = "colorized" diff --git a/setup.py b/setup.py deleted file mode 100644 index e44a9063..00000000 --- a/setup.py +++ /dev/null @@ -1,65 +0,0 @@ -import pathlib -import re - -from setuptools import find_packages, setup - -HERE = pathlib.Path(__file__).parent.absolute() -PACKAGE = "qibotn" - - -# Returns the qibotn version -def version(): - """Gets the version from the package's __init__ file - if there is some problem, let it happily fail""" - version_file = HERE / "src" / PACKAGE / "__init__.py" - version_regex = r"^__version__ = ['\"]([^'\"]*)['\"]" - - initfile = version_file.read_text(encoding="utf-8") - matched = re.search(version_regex, initfile, re.M) - - if matched is not None: - return matched.group(1) - return "0.0.0" - - -# load long description from README -setup( - name="qibotn", - version=version(), - description="A tensor-network translation module for quantum computing", - author="The Qibo team", - author_email="", - url="https://github.com/qiboteam/qibotn", - packages=find_packages("src"), - package_dir={"": "src"}, - package_data={"": ["*.out", "*.yml"]}, - include_package_data=True, - zip_safe=False, - classifiers=[ - "Programming Language :: Python :: 3", - "Topic :: Scientific/Engineering :: Physics", - ], - install_requires=[ - "qibo>=0.1.10", - "qibojit>=0.0.7", - "quimb[tensor]>=1.6.0", - ], - extras_require={ - "docs": [], - "tests": [ - "pytest>=7.2.0", - "pytest-cov>=4.0.0", - "pytest-env>=0.8.1", - ], - "analysis": [ - "pylint>=2.16.0", - ], - "cuda": [ - "cupy>=11.6.0", - "cuquantum-python-cu11>=23.3.0", - ], - }, - python_requires=">=3.8.0", - long_description=(HERE / "README.md").read_text(encoding="utf-8"), - long_description_content_type="text/markdown", -) diff --git a/src/qibotn/QiboCircuitConvertor.py b/src/qibotn/QiboCircuitConvertor.py deleted file mode 100644 index 8ebfe5fe..00000000 --- a/src/qibotn/QiboCircuitConvertor.py +++ /dev/null @@ -1,110 +0,0 @@ -import cupy as cp -import numpy as np - - -class QiboCircuitToEinsum: - """Convert a circuit to a Tensor Network (TN) representation. - The circuit is first processed to an intermediate form by grouping each gate - matrix with its corresponding qubit it is acting on to a list. It is then - converted to an equivalent TN expression through the class function - state_vector_operands() following the Einstein summation convention in the - interleave format. - - See document for detail of the format: https://docs.nvidia.com/cuda/cuquantum/python/api/generated/cuquantum.contract.html - - The output is to be used by cuQuantum's contract() for computation of the - state vectors of the circuit. - """ - - def __init__(self, circuit, dtype="complex128"): - self.backend = cp - self.dtype = getattr(self.backend, dtype) - self.init_basis_map(self.backend, dtype) - self.init_intermediate_circuit(circuit) - - def state_vector_operands(self): - input_bitstring = "0" * len(self.active_qubits) - - input_operands = self._get_bitstring_tensors(input_bitstring) - - ( - mode_labels, - qubits_frontier, - next_frontier, - ) = self._init_mode_labels_from_qubits(self.active_qubits) - - gate_mode_labels, gate_operands = self._parse_gates_to_mode_labels_operands( - self.gate_tensors, qubits_frontier, next_frontier - ) - - operands = input_operands + gate_operands - mode_labels += gate_mode_labels - - out_list = [] - for key in qubits_frontier: - out_list.append(qubits_frontier[key]) - - operand_exp_interleave = [x for y in zip(operands, mode_labels) for x in y] - operand_exp_interleave.append(out_list) - return operand_exp_interleave - - def _init_mode_labels_from_qubits(self, qubits): - n = len(qubits) - frontier_dict = {q: i for i, q in enumerate(qubits)} - mode_labels = [[i] for i in range(n)] - return mode_labels, frontier_dict, n - - def _get_bitstring_tensors(self, bitstring): - return [self.basis_map[ibit] for ibit in bitstring] - - def _parse_gates_to_mode_labels_operands( - self, gates, qubits_frontier, next_frontier - ): - mode_labels = [] - operands = [] - - for tensor, gate_qubits in gates: - operands.append(tensor) - input_mode_labels = [] - output_mode_labels = [] - for q in gate_qubits: - input_mode_labels.append(qubits_frontier[q]) - output_mode_labels.append(next_frontier) - qubits_frontier[q] = next_frontier - next_frontier += 1 - mode_labels.append(output_mode_labels + input_mode_labels) - return mode_labels, operands - - def op_shape_from_qubits(self, nqubits): - """Modify tensor to cuQuantum shape - (qubit_states,input_output) * qubits_involved - """ - return (2, 2) * nqubits - - def init_intermediate_circuit(self, circuit): - self.gate_tensors = [] - gates_qubits = [] - - for gate in circuit.queue: - gate_qubits = gate.control_qubits + gate.target_qubits - gates_qubits.extend(gate_qubits) - - # self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on - # https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32 - required_shape = self.op_shape_from_qubits(len(gate_qubits)) - self.gate_tensors.append( - ( - cp.asarray(gate.matrix(), dtype=self.dtype).reshape(required_shape), - gate_qubits, - ) - ) - - # self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit. - self.active_qubits = np.unique(gates_qubits) - - def init_basis_map(self, backend, dtype): - asarray = backend.asarray - state_0 = asarray([1, 0], dtype=dtype) - state_1 = asarray([0, 1], dtype=dtype) - - self.basis_map = {"0": state_0, "1": state_1} diff --git a/src/qibotn/__main__.py b/src/qibotn/__main__.py deleted file mode 100644 index 0476be5e..00000000 --- a/src/qibotn/__main__.py +++ /dev/null @@ -1,20 +0,0 @@ -import argparse - -import qibotn.quimb - - -def parser(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--nqubits", default=10, type=int, help="Number of quibits in the circuits." - ) - return parser.parse_args() - - -def main(args: argparse.Namespace): - print("Testing for %d nqubits" % (args.nqubits)) - qibotn.quimb.eval(args.nqubits, args.qasm_circ, args.init_state) - - -if __name__ == "__main__": - main(parser()) diff --git a/src/qibotn/backends/__init__.py b/src/qibotn/backends/__init__.py new file mode 100644 index 00000000..6f5abdd4 --- /dev/null +++ b/src/qibotn/backends/__init__.py @@ -0,0 +1,2 @@ +from qibotn.backends.cutensornet import CuTensorNet +from qibotn.backends.quimb import QuimbBackend diff --git a/src/qibotn/backends/cutensornet.py b/src/qibotn/backends/cutensornet.py new file mode 100644 index 00000000..fc010e97 --- /dev/null +++ b/src/qibotn/backends/cutensornet.py @@ -0,0 +1,188 @@ +import cuquantum # pylint: disable=import-error +import numpy as np +from qibo.backends.numpy import NumpyBackend +from qibo.config import raise_error +from qibo.result import QuantumState + +CUDA_TYPES = { + "complex64": ( + cuquantum.cudaDataType.CUDA_C_32F, + cuquantum.ComputeType.COMPUTE_32F, + ), + "complex128": ( + cuquantum.cudaDataType.CUDA_C_64F, + cuquantum.ComputeType.COMPUTE_64F, + ), +} + + +class CuTensorNet(NumpyBackend): # pragma: no cover + # CI does not test for GPU + + def __init__(self, runcard): + super().__init__() + from cuquantum import cutensornet as cutn # pylint: disable=import-error + + if runcard is not None: + self.MPI_enabled = runcard.get("MPI_enabled", False) + self.NCCL_enabled = runcard.get("NCCL_enabled", False) + + expectation_enabled_value = runcard.get("expectation_enabled") + if expectation_enabled_value is True: + self.expectation_enabled = True + self.pauli_string_pattern = "XXXZ" + elif expectation_enabled_value is False: + self.expectation_enabled = False + elif isinstance(expectation_enabled_value, dict): + self.expectation_enabled = True + expectation_enabled_dict = runcard.get("expectation_enabled", {}) + self.pauli_string_pattern = expectation_enabled_dict.get( + "pauli_string_pattern", None + ) + else: + raise TypeError("expectation_enabled has an unexpected type") + + mps_enabled_value = runcard.get("MPS_enabled") + if mps_enabled_value is True: + self.MPS_enabled = True + self.gate_algo = { + "qr_method": False, + "svd_method": { + "partition": "UV", + "abs_cutoff": 1e-12, + }, + } + elif mps_enabled_value is False: + self.MPS_enabled = False + elif isinstance(mps_enabled_value, dict): + self.MPS_enabled = True + self.gate_algo = mps_enabled_value + else: + raise TypeError("MPS_enabled has an unexpected type") + + else: + self.MPI_enabled = False + self.MPS_enabled = False + self.NCCL_enabled = False + self.expectation_enabled = False + + self.name = "qibotn" + self.cuquantum = cuquantum + self.cutn = cutn + self.platform = "cutensornet" + self.versions["cuquantum"] = self.cuquantum.__version__ + self.supports_multigpu = True + self.handle = self.cutn.create() + + def apply_gate(self, gate, state, nqubits): # pragma: no cover + raise_error(NotImplementedError, "QiboTN cannot apply gates directly.") + + def apply_gate_density_matrix(self, gate, state, nqubits): # pragma: no cover + raise_error(NotImplementedError, "QiboTN cannot apply gates directly.") + + def assign_measurements(self, measurement_map, circuit_result): + raise_error(NotImplementedError, "Not implemented in QiboTN.") + + def __del__(self): + if hasattr(self, "cutn"): + self.cutn.destroy(self.handle) + + def set_precision(self, precision): + if precision != self.precision: + super().set_precision(precision) + + def cuda_type(self, dtype="complex64"): + if dtype in CUDA_TYPES: + return CUDA_TYPES[dtype] + else: + raise TypeError("Type can be either complex64 or complex128") + + def execute_circuit( + self, circuit, initial_state=None, nshots=None, return_array=False + ): # pragma: no cover + """Executes a quantum circuit. + + Args: + circuit (:class:`qibo.models.circuit.Circuit`): Circuit to execute. + initial_state (:class:`qibo.models.circuit.Circuit`): Circuit to prepare the initial state. + If ``None`` the default ``|00...0>`` state is used. + + Returns: + xxx. + """ + + import qibotn.eval as eval + + if initial_state is not None: + raise_error(NotImplementedError, "QiboTN cannot support initial state.") + + if ( + self.MPI_enabled == False + and self.MPS_enabled == False + and self.NCCL_enabled == False + and self.expectation_enabled == False + ): + state = eval.dense_vector_tn(circuit, self.dtype) + elif ( + self.MPI_enabled == False + and self.MPS_enabled == True + and self.NCCL_enabled == False + and self.expectation_enabled == False + ): + state = eval.dense_vector_mps(circuit, self.gate_algo, self.dtype) + elif ( + self.MPI_enabled == True + and self.MPS_enabled == False + and self.NCCL_enabled == False + and self.expectation_enabled == False + ): + state, rank = eval.dense_vector_tn_MPI(circuit, self.dtype, 32) + if rank > 0: + state = np.array(0) + elif ( + self.MPI_enabled == False + and self.MPS_enabled == False + and self.NCCL_enabled == True + and self.expectation_enabled == False + ): + state, rank = eval.dense_vector_tn_nccl(circuit, self.dtype, 32) + if rank > 0: + state = np.array(0) + elif ( + self.MPI_enabled == False + and self.MPS_enabled == False + and self.NCCL_enabled == False + and self.expectation_enabled == True + ): + state = eval.expectation_pauli_tn( + circuit, self.dtype, self.pauli_string_pattern + ) + elif ( + self.MPI_enabled == True + and self.MPS_enabled == False + and self.NCCL_enabled == False + and self.expectation_enabled == True + ): + state, rank = eval.expectation_pauli_tn_MPI( + circuit, self.dtype, self.pauli_string_pattern, 32 + ) + if rank > 0: + state = np.array(0) + elif ( + self.MPI_enabled == False + and self.MPS_enabled == False + and self.NCCL_enabled == True + and self.expectation_enabled == True + ): + state, rank = eval.expectation_pauli_tn_nccl( + circuit, self.dtype, self.pauli_string_pattern, 32 + ) + if rank > 0: + state = np.array(0) + else: + raise_error(NotImplementedError, "Compute type not supported.") + + if return_array: + return state.flatten() + else: + return QuantumState(state.flatten()) diff --git a/src/qibotn/backends/quimb.py b/src/qibotn/backends/quimb.py new file mode 100644 index 00000000..0a34eefe --- /dev/null +++ b/src/qibotn/backends/quimb.py @@ -0,0 +1,85 @@ +from qibo.backends.numpy import NumpyBackend +from qibo.config import raise_error +from qibo.result import QuantumState + + +class QuimbBackend(NumpyBackend): + + def __init__(self, runcard): + super().__init__() + import quimb # pylint: disable=import-error + + if runcard is not None: + self.MPI_enabled = runcard.get("MPI_enabled", False) + self.NCCL_enabled = runcard.get("NCCL_enabled", False) + self.expectation_enabled = runcard.get("expectation_enabled", False) + + mps_enabled_value = runcard.get("MPS_enabled") + if mps_enabled_value is True: + self.mps_opts = {"method": "svd", "cutoff": 1e-6, "cutoff_mod": "abs"} + elif mps_enabled_value is False: + self.mps_opts = False + elif isinstance(mps_enabled_value, dict): + self.mps_opts = mps_enabled_value + else: + raise TypeError("MPS_enabled has an unexpected type") + + else: + self.MPI_enabled = False + self.MPS_enabled = False + self.NCCL_enabled = False + self.expectation_enabled = False + + self.name = "qibotn" + self.quimb = quimb + self.platform = "QuimbBackend" + self.versions["quimb"] = self.quimb.__version__ + + def apply_gate(self, gate, state, nqubits): # pragma: no cover + raise_error(NotImplementedError, "QiboTN cannot apply gates directly.") + + def apply_gate_density_matrix(self, gate, state, nqubits): # pragma: no cover + raise_error(NotImplementedError, "QiboTN cannot apply gates directly.") + + def assign_measurements(self, measurement_map, circuit_result): + raise_error(NotImplementedError, "Not implemented in QiboTN.") + + def set_precision(self, precision): + if precision != self.precision: + super().set_precision(precision) + + def execute_circuit( + self, circuit, initial_state=None, nshots=None, return_array=False + ): # pragma: no cover + """Executes a quantum circuit. + + Args: + circuit (:class:`qibo.models.circuit.Circuit`): Circuit to execute. + initial_state (:class:`qibo.models.circuit.Circuit`): Circuit to prepare the initial state. + If ``None`` the default ``|00...0>`` state is used. + + Returns: + xxx. + """ + + import qibotn.eval_qu as eval + + if self.MPI_enabled == True: + raise_error(NotImplementedError, "QiboTN quimb backend cannot support MPI.") + if self.NCCL_enabled == True: + raise_error( + NotImplementedError, "QiboTN quimb backend cannot support NCCL." + ) + if self.expectation_enabled == True: + raise_error( + NotImplementedError, "QiboTN quimb backend cannot support expectation" + ) + + state = eval.dense_vector_tn_qu( + circuit.to_qasm(), initial_state, self.mps_opts, backend="numpy" + ) + + if return_array: + return state.flatten() + else: + return QuantumState(state.flatten()) diff --git a/src/qibotn/circuit_convertor.py b/src/qibotn/circuit_convertor.py new file mode 100644 index 00000000..14af79de --- /dev/null +++ b/src/qibotn/circuit_convertor.py @@ -0,0 +1,206 @@ +import cupy as cp +import numpy as np + +# Reference: https://github.com/NVIDIA/cuQuantum/tree/main/python/samples/cutensornet/circuit_converter + + +class QiboCircuitToEinsum: + """Convert a circuit to a Tensor Network (TN) representation. + + The circuit is first processed to an intermediate form by grouping each gate matrix + with its corresponding qubit it is acting on to a list. It is then converted to an + equivalent TN expression through the class function state_vector_operands() + following the Einstein summation convention in the interleave format. + + See document for detail of the format: https://docs.nvidia.com/cuda/cuquantum/python/api/generated/cuquantum.contract.html + + The output is to be used by cuQuantum's contract() for computation of the + state vectors of the circuit. + """ + + def __init__(self, circuit, dtype="complex128"): + self.backend = cp + self.dtype = getattr(self.backend, dtype) + self.init_basis_map(self.backend, dtype) + self.init_intermediate_circuit(circuit) + self.circuit = circuit + + def state_vector_operands(self): + input_bitstring = "0" * len(self.active_qubits) + + input_operands = self._get_bitstring_tensors(input_bitstring) + + ( + mode_labels, + qubits_frontier, + next_frontier, + ) = self._init_mode_labels_from_qubits(self.active_qubits) + + gate_mode_labels, gate_operands = self._parse_gates_to_mode_labels_operands( + self.gate_tensors, qubits_frontier, next_frontier + ) + + operands = input_operands + gate_operands + mode_labels += gate_mode_labels + + out_list = [] + for key in qubits_frontier: + out_list.append(qubits_frontier[key]) + + operand_exp_interleave = [x for y in zip(operands, mode_labels) for x in y] + operand_exp_interleave.append(out_list) + return operand_exp_interleave + + def _init_mode_labels_from_qubits(self, qubits): + n = len(qubits) + frontier_dict = {q: i for i, q in enumerate(qubits)} + mode_labels = [[i] for i in range(n)] + return mode_labels, frontier_dict, n + + def _get_bitstring_tensors(self, bitstring): + return [self.basis_map[ibit] for ibit in bitstring] + + def _parse_gates_to_mode_labels_operands( + self, gates, qubits_frontier, next_frontier + ): + mode_labels = [] + operands = [] + + for tensor, gate_qubits in gates: + operands.append(tensor) + input_mode_labels = [] + output_mode_labels = [] + for q in gate_qubits: + input_mode_labels.append(qubits_frontier[q]) + output_mode_labels.append(next_frontier) + qubits_frontier[q] = next_frontier + next_frontier += 1 + mode_labels.append(output_mode_labels + input_mode_labels) + return mode_labels, operands + + def op_shape_from_qubits(self, nqubits): + """Modify tensor to cuQuantum shape (qubit_states,input_output) * + qubits_involved.""" + return (2, 2) * nqubits + + def init_intermediate_circuit(self, circuit): + self.gate_tensors = [] + gates_qubits = [] + + for gate in circuit.queue: + gate_qubits = gate.control_qubits + gate.target_qubits + gates_qubits.extend(gate_qubits) + + # self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on + # https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32 + required_shape = self.op_shape_from_qubits(len(gate_qubits)) + self.gate_tensors.append( + ( + cp.asarray(gate.matrix(), dtype=self.dtype).reshape(required_shape), + gate_qubits, + ) + ) + + # self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit. + self.active_qubits = np.unique(gates_qubits) + + def init_basis_map(self, backend, dtype): + asarray = backend.asarray + state_0 = asarray([1, 0], dtype=dtype) + state_1 = asarray([0, 1], dtype=dtype) + + self.basis_map = {"0": state_0, "1": state_1} + + def init_inverse_circuit(self, circuit): + self.gate_tensors_inverse = [] + gates_qubits_inverse = [] + + for gate in circuit.queue: + gate_qubits = gate.control_qubits + gate.target_qubits + gates_qubits_inverse.extend(gate_qubits) + + # self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on + # https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32 + required_shape = self.op_shape_from_qubits(len(gate_qubits)) + self.gate_tensors_inverse.append( + ( + cp.asarray(gate.matrix()).reshape(required_shape), + gate_qubits, + ) + ) + + # self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit. + self.active_qubits_inverse = np.unique(gates_qubits_inverse) + + def get_pauli_gates(self, pauli_map, dtype="complex128", backend=cp): + """Populate the gates for all pauli operators. + + Args: + pauli_map: A dictionary mapping qubits to pauli operators. + dtype: Data type for the tensor operands. + backend: The package the tensor operands belong to. + + Returns: + A sequence of pauli gates. + """ + asarray = backend.asarray + pauli_i = asarray([[1, 0], [0, 1]], dtype=dtype) + pauli_x = asarray([[0, 1], [1, 0]], dtype=dtype) + pauli_y = asarray([[0, -1j], [1j, 0]], dtype=dtype) + pauli_z = asarray([[1, 0], [0, -1]], dtype=dtype) + + operand_map = {"I": pauli_i, "X": pauli_x, "Y": pauli_y, "Z": pauli_z} + gates = [] + for qubit, pauli_char in pauli_map.items(): + operand = operand_map.get(pauli_char) + if operand is None: + raise ValueError("pauli string character must be one of I/X/Y/Z") + gates.append((operand, (qubit,))) + return gates + + def expectation_operands(self, pauli_string): + input_bitstring = "0" * self.circuit.nqubits + + input_operands = self._get_bitstring_tensors(input_bitstring) + pauli_string = dict(zip(range(self.circuit.nqubits), pauli_string)) + pauli_map = pauli_string + + ( + mode_labels, + qubits_frontier, + next_frontier, + ) = self._init_mode_labels_from_qubits(range(self.circuit.nqubits)) + + gate_mode_labels, gate_operands = self._parse_gates_to_mode_labels_operands( + self.gate_tensors, qubits_frontier, next_frontier + ) + + operands = input_operands + gate_operands + mode_labels += gate_mode_labels + + self.init_inverse_circuit(self.circuit.invert()) + + next_frontier = max(qubits_frontier.values()) + 1 + + pauli_gates = self.get_pauli_gates( + pauli_map, dtype=self.dtype, backend=self.backend + ) + + gates_inverse = pauli_gates + self.gate_tensors_inverse + + ( + gate_mode_labels_inverse, + gate_operands_inverse, + ) = self._parse_gates_to_mode_labels_operands( + gates_inverse, qubits_frontier, next_frontier + ) + mode_labels = ( + mode_labels + + gate_mode_labels_inverse + + [[qubits_frontier[ix]] for ix in range(self.circuit.nqubits)] + ) + operands = operands + gate_operands_inverse + operands[: self.circuit.nqubits] + + operand_exp_interleave = [x for y in zip(operands, mode_labels) for x in y] + + return operand_exp_interleave diff --git a/src/qibotn/QiboCircuitToMPS.py b/src/qibotn/circuit_to_mps.py similarity index 83% rename from src/qibotn/QiboCircuitToMPS.py rename to src/qibotn/circuit_to_mps.py index 58fdd70e..af8acd5a 100644 --- a/src/qibotn/QiboCircuitToMPS.py +++ b/src/qibotn/circuit_to_mps.py @@ -2,8 +2,8 @@ import numpy as np from cuquantum import cutensornet as cutn -from qibotn.MPSUtils import apply_gate, initial -from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum +from qibotn.circuit_convertor import QiboCircuitToEinsum +from qibotn.mps_utils import apply_gate, initial class QiboCircuitToMPS: @@ -21,7 +21,7 @@ def __init__( self.handle = cutn.create() self.dtype = dtype self.mps_tensors = initial(self.num_qubits, dtype=dtype) - circuitconvertor = QiboCircuitToEinsum(circ_qibo) + circuitconvertor = QiboCircuitToEinsum(circ_qibo, dtype=dtype) for gate, qubits in circuitconvertor.gate_tensors: # mapping from qubits to qubit indices diff --git a/src/qibotn/cutn.py b/src/qibotn/cutn.py deleted file mode 100644 index 6b8582fe..00000000 --- a/src/qibotn/cutn.py +++ /dev/null @@ -1,60 +0,0 @@ -import multiprocessing - -import cupy as cp -from cupy.cuda.runtime import getDeviceCount -from cuquantum import contract -from cuquantum import cutensornet as cutn - -from qibotn.mps_contraction_helper import MPSContractionHelper -from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum -from qibotn.QiboCircuitToMPS import QiboCircuitToMPS - - -def eval(qibo_circ, datatype): - myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) - return contract(*myconvertor.state_vector_operands()) - - -def eval_tn_MPI(qibo_circ, datatype, n_samples=8): - """Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI. - The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution. - The pathfinder looks at user defined number of samples (n_samples) iteratively to select the least costly contraction path. This is sped up with multi thread. - After pathfinding the optimal path is used in the actual contraction to give a dense vector representation of the TN. - """ - - from mpi4py import MPI # this line initializes MPI - - ncpu_threads = multiprocessing.cpu_count() // 2 - - comm = MPI.COMM_WORLD - rank = comm.Get_rank() - device_id = rank % getDeviceCount() - cp.cuda.Device(device_id).use() - - handle = cutn.create() - cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm)) - network_opts = cutn.NetworkOptions(handle=handle, blocking="auto") - - # Perform circuit conversion - myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) - operands_interleave = myconvertor.state_vector_operands() - - # Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object. - network = cutn.Network(*operands_interleave, options=network_opts) - network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads}) - - # Execution: To execute the contraction using the optimal path found previously - result = network.contract() - - cutn.destroy(handle) - - return result, rank - - -def eval_mps(qibo_circ, gate_algo, datatype): - myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype) - mps_helper = MPSContractionHelper(myconvertor.num_qubits) - - return mps_helper.contract_state_vector( - myconvertor.mps_tensors, {"handle": myconvertor.handle} - ) diff --git a/src/qibotn/eval.py b/src/qibotn/eval.py new file mode 100644 index 00000000..6375aa6a --- /dev/null +++ b/src/qibotn/eval.py @@ -0,0 +1,352 @@ +import cupy as cp +from cupy.cuda.runtime import getDeviceCount +from cuquantum import contract + +from qibotn.circuit_convertor import QiboCircuitToEinsum +from qibotn.circuit_to_mps import QiboCircuitToMPS +from qibotn.mps_contraction_helper import MPSContractionHelper + + +def dense_vector_tn(qibo_circ, datatype): + """Convert qibo circuit to tensornet (TN) format and perform contraction to + dense vector.""" + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + return contract(*myconvertor.state_vector_operands()) + + +def expectation_pauli_tn(qibo_circ, datatype, pauli_string_pattern): + """Convert qibo circuit to tensornet (TN) format and perform contraction to + expectation of given Pauli string.""" + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + return contract( + *myconvertor.expectation_operands( + pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern) + ) + ) + + +def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8): + """Convert qibo circuit to tensornet (TN) format and perform contraction + using multi node and multi GPU through MPI. + + The conversion is performed by QiboCircuitToEinsum(), after which it + goes through 2 steps: pathfinder and execution. The pathfinder looks + at user defined number of samples (n_samples) iteratively to select + the least costly contraction path. This is sped up with multi + thread. After pathfinding the optimal path is used in the actual + contraction to give a dense vector representation of the TN. + """ + + from cuquantum import Network + from mpi4py import MPI + + root = 0 + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + size = comm.Get_size() + + device_id = rank % getDeviceCount() + + # Perform circuit conversion + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + + operands = myconvertor.state_vector_operands() + + # Assign the device for each process. + device_id = rank % getDeviceCount() + + # Create network object. + network = Network(*operands, options={"device_id": device_id}) + + # Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction. + path, info = network.contract_path( + optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}} + ) + + # Select the best path from all ranks. + opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC) + + # Broadcast info from the sender to all other ranks. + info = comm.bcast(info, sender) + + # Set path and slices. + path, info = network.contract_path( + optimize={"path": info.path, "slicing": info.slices} + ) + + # Calculate this process's share of the slices. + num_slices = info.num_slices + chunk, extra = num_slices // size, num_slices % size + slice_begin = rank * chunk + min(rank, extra) + slice_end = ( + num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra) + ) + slices = range(slice_begin, slice_end) + + # Contract the group of slices the process is responsible for. + result = network.contract(slices=slices) + + # Sum the partial contribution from each process on root. + result = comm.reduce(sendobj=result, op=MPI.SUM, root=root) + + return result, rank + + +def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8): + """Convert qibo circuit to tensornet (TN) format and perform contraction + using multi node and multi GPU through NCCL. + + The conversion is performed by QiboCircuitToEinsum(), after which it + goes through 2 steps: pathfinder and execution. The pathfinder looks + at user defined number of samples (n_samples) iteratively to select + the least costly contraction path. This is sped up with multi + thread. After pathfinding the optimal path is used in the actual + contraction to give a dense vector representation of the TN. + """ + from cupy.cuda import nccl + from cuquantum import Network + from mpi4py import MPI + + root = 0 + comm_mpi = MPI.COMM_WORLD + rank = comm_mpi.Get_rank() + size = comm_mpi.Get_size() + + device_id = rank % getDeviceCount() + + cp.cuda.Device(device_id).use() + + # Set up the NCCL communicator. + nccl_id = nccl.get_unique_id() if rank == root else None + nccl_id = comm_mpi.bcast(nccl_id, root) + comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank) + + # Perform circuit conversion + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + operands = myconvertor.state_vector_operands() + + network = Network(*operands) + + # Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction. + path, info = network.contract_path( + optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}} + ) + + # Select the best path from all ranks. + opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC) + + # Broadcast info from the sender to all other ranks. + info = comm_mpi.bcast(info, sender) + + # Set path and slices. + path, info = network.contract_path( + optimize={"path": info.path, "slicing": info.slices} + ) + + # Calculate this process's share of the slices. + num_slices = info.num_slices + chunk, extra = num_slices // size, num_slices % size + slice_begin = rank * chunk + min(rank, extra) + slice_end = ( + num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra) + ) + slices = range(slice_begin, slice_end) + + # Contract the group of slices the process is responsible for. + result = network.contract(slices=slices) + + # Sum the partial contribution from each process on root. + stream_ptr = cp.cuda.get_current_stream().ptr + comm_nccl.reduce( + result.data.ptr, + result.data.ptr, + result.size, + nccl.NCCL_FLOAT64, + nccl.NCCL_SUM, + root, + stream_ptr, + ) + + return result, rank + + +def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_samples=8): + """Convert qibo circuit to tensornet (TN) format and perform contraction to + expectation of given Pauli string using multi node and multi GPU through + NCCL. + + The conversion is performed by QiboCircuitToEinsum(), after which it + goes through 2 steps: pathfinder and execution. The + pauli_string_pattern is used to generate the pauli string + corresponding to the number of qubits of the system. The pathfinder + looks at user defined number of samples (n_samples) iteratively to + select the least costly contraction path. This is sped up with multi + thread. After pathfinding the optimal path is used in the actual + contraction to give an expectation value. + """ + from cupy.cuda import nccl + from cuquantum import Network + from mpi4py import MPI + + root = 0 + comm_mpi = MPI.COMM_WORLD + rank = comm_mpi.Get_rank() + size = comm_mpi.Get_size() + + device_id = rank % getDeviceCount() + + cp.cuda.Device(device_id).use() + + # Set up the NCCL communicator. + nccl_id = nccl.get_unique_id() if rank == root else None + nccl_id = comm_mpi.bcast(nccl_id, root) + comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank) + + # Perform circuit conversion + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + operands = myconvertor.expectation_operands( + pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern) + ) + + network = Network(*operands) + + # Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction. + path, info = network.contract_path( + optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}} + ) + + # Select the best path from all ranks. + opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC) + + # Broadcast info from the sender to all other ranks. + info = comm_mpi.bcast(info, sender) + + # Set path and slices. + path, info = network.contract_path( + optimize={"path": info.path, "slicing": info.slices} + ) + + # Calculate this process's share of the slices. + num_slices = info.num_slices + chunk, extra = num_slices // size, num_slices % size + slice_begin = rank * chunk + min(rank, extra) + slice_end = ( + num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra) + ) + slices = range(slice_begin, slice_end) + + # Contract the group of slices the process is responsible for. + result = network.contract(slices=slices) + + # Sum the partial contribution from each process on root. + stream_ptr = cp.cuda.get_current_stream().ptr + comm_nccl.reduce( + result.data.ptr, + result.data.ptr, + result.size, + nccl.NCCL_FLOAT64, + nccl.NCCL_SUM, + root, + stream_ptr, + ) + + return result, rank + + +def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_samples=8): + """Convert qibo circuit to tensornet (TN) format and perform contraction to + expectation of given Pauli string using multi node and multi GPU through + MPI. + + The conversion is performed by QiboCircuitToEinsum(), after which it + goes through 2 steps: pathfinder and execution. The + pauli_string_pattern is used to generate the pauli string + corresponding to the number of qubits of the system. The pathfinder + looks at user defined number of samples (n_samples) iteratively to + select the least costly contraction path. This is sped up with multi + thread. After pathfinding the optimal path is used in the actual + contraction to give an expectation value. + """ + from cuquantum import Network + from mpi4py import MPI # this line initializes MPI + + root = 0 + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + size = comm.Get_size() + + device_id = rank % getDeviceCount() + + # Perform circuit conversion + myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) + + operands = myconvertor.expectation_operands( + pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern) + ) + + # Assign the device for each process. + device_id = rank % getDeviceCount() + + # Create network object. + network = Network(*operands, options={"device_id": device_id}) + + # Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction. + path, info = network.contract_path( + optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}} + ) + + # Select the best path from all ranks. + opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC) + + # Broadcast info from the sender to all other ranks. + info = comm.bcast(info, sender) + + # Set path and slices. + path, info = network.contract_path( + optimize={"path": info.path, "slicing": info.slices} + ) + + # Calculate this process's share of the slices. + num_slices = info.num_slices + chunk, extra = num_slices // size, num_slices % size + slice_begin = rank * chunk + min(rank, extra) + slice_end = ( + num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra) + ) + slices = range(slice_begin, slice_end) + + # Contract the group of slices the process is responsible for. + result = network.contract(slices=slices) + + # Sum the partial contribution from each process on root. + result = comm.reduce(sendobj=result, op=MPI.SUM, root=root) + + return result, rank + + +def dense_vector_mps(qibo_circ, gate_algo, datatype): + """Convert qibo circuit to matrix product state (MPS) format and perform + contraction to dense vector.""" + myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype) + mps_helper = MPSContractionHelper(myconvertor.num_qubits) + + return mps_helper.contract_state_vector( + myconvertor.mps_tensors, {"handle": myconvertor.handle} + ) + + +def pauli_string_gen(nqubits, pauli_string_pattern): + """Used internally to generate the string based on given pattern and number + of qubit. + + Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX + """ + if nqubits <= 0: + return "Invalid input. N should be a positive integer." + + result = "" + + for i in range(nqubits): + char_to_add = pauli_string_pattern[i % len(pauli_string_pattern)] + result += char_to_add + return result diff --git a/src/qibotn/eval_qu.py b/src/qibotn/eval_qu.py new file mode 100644 index 00000000..d6064e95 --- /dev/null +++ b/src/qibotn/eval_qu.py @@ -0,0 +1,31 @@ +import numpy as np +import quimb.tensor as qtn + + +def init_state_tn(nqubits, init_state_sv): + """Create a matrix product state directly from a dense vector.""" + + dims = tuple(2 * np.ones(nqubits, dtype=int)) + + return qtn.tensor_1d.MatrixProductState.from_dense(init_state_sv, dims) + + +def dense_vector_tn_qu(qasm: str, initial_state, mps_opts, backend="numpy"): + """Evaluate QASM with Quimb. + + backend (quimb): numpy, cupy, jax. Passed to ``opt_einsum``. + """ + + if initial_state is not None: + nqubits = int(np.log2(len(initial_state))) + initial_state = init_state_tn(nqubits, initial_state) + + circ_cls = qtn.circuit.CircuitMPS if mps_opts else qtn.circuit.Circuit + circ_quimb = circ_cls.from_openqasm2_str( + qasm, psi0=initial_state, gate_opts=mps_opts + ) + + interim = circ_quimb.psi.full_simplify(seq="DRC") + amplitudes = interim.to_dense(backend=backend) + + return amplitudes diff --git a/src/qibotn/mps_contraction_helper.py b/src/qibotn/mps_contraction_helper.py index 0366a34a..1c004de4 100644 --- a/src/qibotn/mps_contraction_helper.py +++ b/src/qibotn/mps_contraction_helper.py @@ -1,9 +1,10 @@ -from cuquantum import CircuitToEinsum, contract, contract_path, tensor +from cuquantum import contract, contract_path + +# Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb class MPSContractionHelper: - """ - A helper class to compute various quantities for a given MPS. + """A helper class to compute various quantities for a given MPS. Interleaved format is used to construct the input args for `cuquantum.contract`. A concrete example on how the modes are populated for a 7-site MPS is provided below: @@ -41,8 +42,8 @@ def __init__(self, num_qubits): ] def contract_norm(self, mps_tensors, options=None): - """ - Contract the corresponding tensor network to form the norm of the MPS. + """Contract the corresponding tensor network to form the norm of the + MPS. Args: mps_tensors: A list of rank-3 ndarray-like tensor objects. @@ -62,8 +63,8 @@ def contract_norm(self, mps_tensors, options=None): return self._contract(interleaved_inputs, options=options).real def contract_state_vector(self, mps_tensors, options=None): - """ - Contract the corresponding tensor network to form the state vector representation of the MPS. + """Contract the corresponding tensor network to form the state vector + representation of the MPS. Args: mps_tensors: A list of rank-3 ndarray-like tensor objects. @@ -84,8 +85,8 @@ def contract_state_vector(self, mps_tensors, options=None): def contract_expectation( self, mps_tensors, operator, qubits, options=None, normalize=False ): - """ - Contract the corresponding tensor network to form the state vector representation of the MPS. + """Contract the corresponding tensor network to form the expectation of + the MPS. Args: mps_tensors: A list of rank-3 ndarray-like tensor objects. diff --git a/src/qibotn/MPSUtils.py b/src/qibotn/mps_utils.py similarity index 90% rename from src/qibotn/MPSUtils.py rename to src/qibotn/mps_utils.py index 138fb965..e8068f74 100644 --- a/src/qibotn/MPSUtils.py +++ b/src/qibotn/mps_utils.py @@ -2,20 +2,18 @@ from cuquantum import contract from cuquantum.cutensornet.experimental import contract_decompose +# Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb + def initial(num_qubits, dtype): - """ - Generate the MPS with an initial state of |00...00> - """ + """Generate the MPS with an initial state of |00...00>""" state_tensor = cp.asarray([1, 0], dtype=dtype).reshape(1, 2, 1) mps_tensors = [state_tensor] * num_qubits return mps_tensors def mps_site_right_swap(mps_tensors, i, **kwargs): - """ - Perform the swap operation between the ith and i+1th MPS tensors. - """ + """Perform the swap operation between the ith and i+1th MPS tensors.""" # contraction followed by QR decomposition a, _, b = contract_decompose( "ipj,jqk->iqj,jpk", @@ -28,8 +26,7 @@ def mps_site_right_swap(mps_tensors, i, **kwargs): def apply_gate(mps_tensors, gate, qubits, **kwargs): - """ - Apply the gate operand to the MPS tensors in-place. + """Apply the gate operand to the MPS tensors in-place. Args: mps_tensors: A list of rank-3 ndarray-like tensor objects. diff --git a/src/qibotn/quimb.py b/src/qibotn/quimb.py deleted file mode 100644 index 0349f58a..00000000 --- a/src/qibotn/quimb.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -import quimb.tensor as qtn -from qibo.models import Circuit as QiboCircuit - - -def from_qibo( - circuit: QiboCircuit, - is_mps: False, - psi0=None, - method="svd", - cutoff=1e-6, - cutoff_mode="abs", -): - nqubits = circuit.nqubits - gate_opt = {} - if is_mps: - tncirc = qtn.CircuitMPS(nqubits, psi0=psi0) - gate_opt["method"] = method - gate_opt["cutoff"] = cutoff - gate_opt["cutoff_mode"] = cutoff_mode - else: - tncirc = qtn.Circuit(nqubits, psi0=psi0) - - for gate in circuit.queue: - tncirc.apply_gate( - gate.name, - *gate.parameters, - *gate.qubits, - parametrize=False if is_mps else (len(gate.parameters) > 0), - **gate_opt - ) - - return tncirc - - -def init_state_tn(nqubits, init_state_sv): - dims = tuple(2 * np.ones(nqubits, dtype=int)) - - return qtn.tensor_1d.MatrixProductState.from_dense(init_state_sv, dims) - - -def eval(qasm: str, init_state, is_mps, backend="numpy"): - """Evaluate QASM with Quimb - - backend (quimb): numpy, cupy, jax. Passed to ``opt_einsum``. - - """ - circuit = QiboCircuit.from_qasm(qasm) - init_state_mps = init_state_tn(circuit.nqubits, init_state) - circ_quimb = from_qibo(circuit, is_mps, psi0=init_state_mps) - interim = circ_quimb.psi.full_simplify(seq="DRC") - amplitudes = interim.to_dense(backend=backend).flatten() - - return amplitudes diff --git a/tests/test_cuquantum_cutensor_backend.py b/tests/test_cuquantum_cutensor_backend.py index b3a1c053..c8f1e199 100644 --- a/tests/test_cuquantum_cutensor_backend.py +++ b/tests/test_cuquantum_cutensor_backend.py @@ -32,14 +32,16 @@ def test_eval(nqubits: int, dtype="complex128"): dtype (str): The data type for precision, 'complex64' for single, 'complex128' for double. """ - import qibotn.cutn + import qibotn.eval # Test qibo qibo.set_backend(backend=config.qibo.backend, platform=config.qibo.platform) qibo_time, (qibo_circ, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True)) # Test Cuquantum - cutn_time, result_tn = time(lambda: qibotn.cutn.eval(qibo_circ, dtype).flatten()) + cutn_time, result_tn = time( + lambda: qibotn.eval.dense_vector_tn(qibo_circ, dtype).flatten() + ) assert 1e-2 * qibo_time < cutn_time < 1e2 * qibo_time assert np.allclose(result_sv, result_tn), "Resulting dense vectors do not match" @@ -55,7 +57,7 @@ def test_mps(nqubits: int, dtype="complex128"): dtype (str): The data type for precision, 'complex64' for single, 'complex128' for double. """ - import qibotn.cutn + import qibotn.eval # Test qibo qibo.set_backend(backend=config.qibo.backend, platform=config.qibo.platform) @@ -74,7 +76,7 @@ def test_mps(nqubits: int, dtype="complex128"): } cutn_time, result_tn = time( - lambda: qibotn.cutn.eval_mps(circ_qibo, gate_algo, dtype).flatten() + lambda: qibotn.eval.dense_vector_mps(circ_qibo, gate_algo, dtype).flatten() ) print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}") diff --git a/tests/test_qasm_quimb_backend.py b/tests/test_quimb_backend.py similarity index 68% rename from tests/test_qasm_quimb_backend.py rename to tests/test_quimb_backend.py index 6abfb7f1..e32aefe5 100644 --- a/tests/test_qasm_quimb_backend.py +++ b/tests/test_quimb_backend.py @@ -25,29 +25,41 @@ def qibo_qft(nqubits, init_state, swaps): [(1, 1e-6, True), (2, 1e-6, False), (5, 1e-3, True), (10, 1e-3, False)], ) def test_eval(nqubits: int, tolerance: float, is_mps: bool): + """Evaluate circuit with Quimb backend. + + Args: + nqubits (int): Total number of qubits in the system. + tolerance (float): Maximum limit allowed for difference in results + is_mps (bool): True if state is MPS and False for tensor network structure + """ # hack quimb to use the correct number of processes # TODO: remove completely, or at least delegate to the backend # implementation os.environ["QUIMB_NUM_PROCS"] = str(os.cpu_count()) - import qibotn.quimb + import qibotn.eval_qu init_state = create_init_state(nqubits=nqubits) init_state_tn = copy.deepcopy(init_state) # Test qibo qibo.set_backend(backend=config.qibo.backend, platform=config.qibo.platform) - # qibo_time, (qibo_circ, result_sv) = time( - # lambda: qibo_qft(nqubits, init_state, swaps=True) - # ) + qibo_circ, result_sv = qibo_qft(nqubits, init_state, swaps=True) # Convert to qasm for other backends qasm_circ = qibo_circ.to_qasm() # Test quimb - result_tn = qibotn.quimb.eval( - qasm_circ, init_state_tn, is_mps, backend=config.quimb.backend - ) + if is_mps: + gate_opt = {} + gate_opt["method"] = "svd" + gate_opt["cutoff"] = 1e-6 + gate_opt["cutoff_mode"] = "abs" + else: + gate_opt = None + result_tn = qibotn.eval_qu.dense_vector_tn_qu( + qasm_circ, init_state_tn, gate_opt, backend=config.quimb.backend + ).flatten() assert np.allclose( result_sv, result_tn, atol=tolerance