Merge pull request #99 from mattia-robbiano/quimb_backend_refactor
refactor: standardized quimb interface with qmatchatea
This commit is contained in:
Matteo Robbiati
@@ -25,7 +25,7 @@ class MetaBackend:
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if platform == "cutensornet": # pragma: no cover
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return CuTensorNet(runcard)
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elif platform == "qutensornet": # pragma: no cover
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elif platform == "quimb": # pragma: no cover
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return QuimbBackend(runcard)
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elif platform == "qmatchatea": # pragma: no cover
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from qibotn.backends.qmatchatea import QMatchaTeaBackend
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@@ -1,75 +1,136 @@
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from collections import Counter
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import quimb.tensor as qtn
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from qibo.backends import NumpyBackend
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from qibo.config import raise_error
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from qibo.result import QuantumState
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from qibotn.backends.abstract import QibotnBackend
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from qibotn.result import TensorNetworkResult
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class QuimbBackend(QibotnBackend, NumpyBackend):
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def __init__(self, runcard):
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def __init__(self):
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super().__init__()
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import quimb # pylint: disable=import-error
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if runcard is not None:
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self.MPI_enabled = runcard.get("MPI_enabled", False)
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self.NCCL_enabled = runcard.get("NCCL_enabled", False)
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self.expectation_enabled = runcard.get("expectation_enabled", False)
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mps_enabled_value = runcard.get("MPS_enabled")
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if mps_enabled_value is True:
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self.mps_opts = {"method": "svd", "cutoff": 1e-6, "cutoff_mode": "abs"}
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elif mps_enabled_value is False:
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self.mps_opts = None
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elif isinstance(mps_enabled_value, dict):
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self.mps_opts = mps_enabled_value
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else:
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raise TypeError("MPS_enabled has an unexpected type")
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else:
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self.MPI_enabled = False
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self.MPS_enabled = False
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self.NCCL_enabled = False
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self.expectation_enabled = False
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self.mps_opts = None
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self.name = "qibotn"
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self.quimb = quimb
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self.platform = "QuimbBackend"
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self.versions["quimb"] = self.quimb.__version__
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self.platform = "quimb"
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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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): # pragma: no cover
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"""Executes a quantum circuit.
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self.configure_tn_simulation()
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self.setup_backend_specifics()
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def configure_tn_simulation(
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self,
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ansatz: str = "MPS",
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max_bond_dimension: int = 10,
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n_most_frequent_states: int = 100,
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):
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"""
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Configure tensor network simulation.
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Args:
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circuit (:class:`qibo.models.circuit.Circuit`): Circuit to execute.
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initial_state (:class:`qibo.models.circuit.Circuit`): Circuit to prepare the initial state.
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If ``None`` the default ``|00...0>`` state is used.
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ansatz : str, optional
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The tensor network ansatz to use. Currently, only "MPS" is supported. Default is "MPS".
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max_bond_dimension : int, optional
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The maximum bond dimension for the MPS ansatz. Default is 10.
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Notes:
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- The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported.
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- The `max_bond_dimension` parameter controls the maximum allowed bond dimension for the MPS ansatz.
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"""
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self.ansatz = ansatz
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self.max_bond_dimension = max_bond_dimension
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self.n_most_frequent_states = n_most_frequent_states
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def setup_backend_specifics(self, qimb_backend="numpy"):
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"""Setup backend specifics.
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Args:
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qimb_backend: str
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The backend to use for the quimb tensor network simulation.
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"""
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self.backend = qimb_backend
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def execute_circuit(
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self,
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circuit,
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initial_state=None,
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nshots=None,
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return_array=False,
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**prob_kwargs,
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):
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"""
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Execute a quantum circuit using the specified tensor network ansatz and initial state.
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Args:
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circuit : QuantumCircuit
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The quantum circuit to be executed.
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initial_state : array-like, optional
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The initial state of the quantum system. Only supported for Matrix Product States (MPS) ansatz.
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nshots : int, optional
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The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used.
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return_array : bool, optional
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If True, returns the statevector as a dense array. Default is False.
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n_most_frequent_states : int, optional
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The number of most frequent computational basis states to return. Default is 100.
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**prob_kwargs : dict, optional
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Additional keyword arguments for probability computation (currently unused).
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Returns:
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QuantumState or numpy.ndarray: If `return_array` is False, returns a QuantumState object representing the quantum state. If `return_array` is True, returns a numpy array representing the quantum state.
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TensorNetworkResult
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An object containing the results of the circuit execution, including:
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- nqubits: Number of qubits in the circuit.
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- backend: The backend used for execution.
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- measures: The measurement frequencies if nshots is specified, otherwise None.
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- measured_probabilities: A dictionary of computational basis states and their probabilities.
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- prob_type: The type of probability computation used (currently "default").
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- statevector: The final statevector as a dense array if return_array is True, otherwise None.
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Raises:
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ValueError
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If an initial state is provided but the ansatz is not "MPS".
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Notes:
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- The ansatz determines the tensor network structure used for simulation. Currently, only "MPS" is supported.
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- If `initial_state` is provided, it must be compatible with the MPS ansatz.
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- The `nshots` parameter enables sampling from the circuit's output distribution. If not specified, the full statevector is computed.
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"""
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import qibotn.eval_qu as eval
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if self.MPI_enabled == True:
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raise_error(NotImplementedError, "QiboTN quimb backend cannot support MPI.")
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if self.NCCL_enabled == True:
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if initial_state is not None and self.ansatz == "MPS":
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initial_state = qtn.tensor_1d.MatrixProductState.from_dense(
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initial_state, 2
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) # 2 is the physical dimension
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elif initial_state is not None:
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raise_error(
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NotImplementedError, "QiboTN quimb backend cannot support NCCL."
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)
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if self.expectation_enabled == True:
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raise_error(
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NotImplementedError, "QiboTN quimb backend cannot support expectation"
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ValueError, "Initial state not None supported only for MPS ansatz."
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)
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state = eval.dense_vector_tn_qu(
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circuit.to_qasm(), initial_state, self.mps_opts, backend="numpy"
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circ_ansatz = (
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qtn.circuit.CircuitMPS if self.ansatz == "MPS" else qtn.circuit.Circuit
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)
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circ_quimb = circ_ansatz.from_openqasm2_str(
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circuit.to_qasm(), psi0=initial_state
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)
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if return_array:
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return state.flatten()
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else:
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return QuantumState(state.flatten())
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frequencies = Counter(circ_quimb.sample(nshots)) if nshots is not None else None
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main_frequencies = {
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state: count
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for state, count in frequencies.most_common(self.n_most_frequent_states)
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}
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computational_states = [state for state in main_frequencies.keys()]
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amplitudes = {
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state: circ_quimb.amplitude(state) for state in computational_states
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}
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measured_probabilities = {
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state: abs(amplitude) ** 2 for state, amplitude in amplitudes.items()
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}
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statevector = circ_quimb.to_dense() if return_array else None
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return TensorNetworkResult(
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nqubits=circuit.nqubits,
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backend=self,
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measures=frequencies,
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measured_probabilities=measured_probabilities,
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prob_type="default",
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statevector=statevector,
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)
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@@ -44,8 +44,8 @@ class TensorNetworkResult:
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measured_probabilities[self.prob_type][bitstring] = prob[1] - prob[0]
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probabilities = measured_probabilities[self.prob_type]
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else:
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probabilities = self.measured_probabilities[self.prob_type]
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return self.backend.cast(list(probabilities.values()), dtype="double")
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probabilities = self.measured_probabilities
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return probabilities
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def frequencies(self):
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"""Return frequencies if a certain number of shots has been set."""
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