feat: integrating qmatchatea SimulationResult in the circuit execution
This commit is contained in:
MatteoRobbiati
@@ -19,7 +19,7 @@ repos:
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- id: isort
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args: ["--profile", "black"]
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- repo: https://github.com/PyCQA/docformatter
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rev: master
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rev: v1.7.5
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hooks:
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- id: docformatter
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additional_dependencies: [tomli]
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@@ -1,103 +1,125 @@
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"""Implementation of Quantum Matcha Tea backend"""
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"""Implementation of Quantum Matcha Tea backend."""
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from dataclasses import dataclass
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from qibo.config import raise_error
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from qiskit import QuantumCircuit
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from qibotn.backends.abstract import QibotnBackend
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from qibo.config import raise_error
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@dataclass
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class QMatchaTeaBackend(QibotnBackend):
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def __init__(self):
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def __init__(self):
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super().__init__()
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import qiskit # pylint: disable=import-error
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import qmatchatea # pylint: disable=import-error
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import qiskit # pylint: disable=import-error
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import qtealeaves # pylint: disable=import-error
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import qtealeaves # pylint: disable=import-error
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self.qmatchatea = qmatchatea
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self.qiskit = qiskit
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self.qtleaves = qtealeaves
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# Set default configurations
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self.configure_tn_simulation()
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# TODO: update this function whenever ``set_device`` and ``set_precision``
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# are set (?)
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self._setup_qmatchatea_backend()
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self._observables = self.qtleaves.observables.TNObservables()
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@property
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def observables(self):
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"""Observables measured after TN execution."""
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return self._observables
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@observables.setter
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def observables(self, observables: list):
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"""Set the observables to be measured after TN execution.
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It accepts a list
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of objects among the ones proposed in ``qtealeaves.observables``.
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"""
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for obs in observables:
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if isinstance(obs, self.qtleaves.observables.tnobase._TNObsBase):
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self._observables = self.qtleaves.observables.TNObservables()
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self._observables += obs
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else:
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raise TypeError("Expected an instance of TNObservables")
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def execute_circuit(
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self, circuit, initial_state=None, nshots=None, return_array=False
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self, circuit, initial_state=None, nshots=None, return_array=False
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):
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"""Preserve the Qibo execution interface, but return Quantum Matcha Tea
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``SimulationResult`` object."""
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# TODO: verify if the QCIO mechanism of matcha is supported by Fortran only
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# as written in the docstrings or by Python too (see ``io_info`` argument of
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# as written in the docstrings or by Python too (see ``io_info`` argument of
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# ``qmatchatea.interface.run_simulation`` function)
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if initial_state is not None:
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raise_error(
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NotImplementedError,
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f"Backend {self.name}-{self.platform} currently does not support initial state."
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f"Backend {self.name}-{self.platform} currently does not support initial state.",
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)
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# TODO: do we want to keep it like this or we aim to implement a different
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# idea of "shots" here?
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nshots = None
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# TODO: do we want to keep it like this or we aim to implement a different
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# idea of "shots" here?
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circuit = self._qibocirc_to_qiskitcirc(circuit)
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run_qk_params = self.qmatchatea.preprocessing.qk_transpilation_params(False)
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results = self.qmatchatea.run_simulation(
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circ = circuit,
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convergence_parameters = self.convergence_params,
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transpilation_parameters = run_qk_params,
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backend = self.qmatchatea_backend,
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circ=circuit,
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convergence_parameters=self.convergence_params,
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transpilation_parameters=run_qk_params,
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backend=self.qmatchatea_backend,
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observables=self._observables,
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)
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# TODO: construct a proper TNResult object?
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# It does not make sense to reconstruct QuantumState here!
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return results.measure_probabilities
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# TODO: construct a proper TNResult in Qibo?
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return results
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def configure_tn_simulation(
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self,
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convergence_params = None,
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ansatz: str = "MPS",
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):
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"""
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Configure TN simulation given Quantum Matcha Tea interface.
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self,
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convergence_params=None,
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ansatz: str = "MPS",
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):
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"""Configure TN simulation given Quantum Matcha Tea interface.
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Args:
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ansatz (str): tensor network ansatz. It can be tree tensor network "TTN"
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or Matrix Product States "MPS" (default).
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convergence_params (qmatchatea.utils.QCConvergenceParameters):
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convergence parameters class adapted to the quantum computing
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convergence_params (qmatchatea.utils.QCConvergenceParameters):
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convergence parameters class adapted to the quantum computing
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execution. See https://baltig.infn.it/quantum_matcha_tea/py_api_quantum_matcha_tea/-/blob/master/qmatchatea/utils/utils.py?ref_type=heads#L540
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for more instructions. If not passed, the default values proposed
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for more instructions. If not passed, the default values proposed
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by Quantum Matcha Tea's authors are set.
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"""
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# Set configurationsor defaults
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self.convergence_params = convergence_params or self.qmatchatea.QCConvergenceParameters()
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self.convergence_params = (
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convergence_params or self.qmatchatea.QCConvergenceParameters()
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)
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self.ansatz = ansatz
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# Initializing the TNObservables according to qmatchatea
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self.observables = self.qtleaves.observables.TNObservables()
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def _setup_qmatchatea_backend(self):
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"""Configure qmatchatea QCBackend object."""
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self.qmatchatea_device = "cpu" if "CPU" in self.device else "gpu" if "GPU" in self.device else None
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self.qmatchatea_precision = "C" if self.precision == "single" else "Z" if self.precision == "double" else "A"
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self.qmatchatea_device = (
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"cpu" if "CPU" in self.device else "gpu" if "GPU" in self.device else None
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)
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self.qmatchatea_precision = (
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"C"
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if self.precision == "single"
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else "Z" if self.precision == "double" else "A"
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)
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# TODO: once MPI is available for Python, integrate it here
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self.qmatchatea_backend = self.qmatchatea.QCBackend(
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backend = "PY", # The only alternative is Fortran, but we use Python here
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precision = self.qmatchatea_precision,
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device = self.qmatchatea_device,
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ansatz = self.ansatz,
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backend="PY", # The only alternative is Fortran, but we use Python here
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precision=self.qmatchatea_precision,
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device=self.qmatchatea_device,
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ansatz=self.ansatz,
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)
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def _qibocirc_to_qiskitcirc(self, qibo_circuit) -> QuantumCircuit:
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@@ -106,11 +128,10 @@ class QMatchaTeaBackend(QibotnBackend):
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qasm_circuit = qibo_circuit.to_qasm()
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qiskit_circuit = QuantumCircuit.from_qasm_str(qasm_circuit)
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# Transpile the circuit to adapt it to the linear structure of the MPS,
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# Transpile the circuit to adapt it to the linear structure of the MPS,
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# with the constraint of having only the gates basis_gates
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qiskit_circuit = self.qmatchatea.preprocessing.preprocess(
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qiskit_circuit,
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qk_params=self.qmatchatea.preprocessing.qk_transpilation_params()
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qiskit_circuit,
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qk_params=self.qmatchatea.preprocessing.qk_transpilation_params(),
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)
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return qiskit_circuit
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return qiskit_circuit
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