342 lines
12 KiB
Python
342 lines
12 KiB
Python
from collections import Counter
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import quimb as qu
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import quimb.tensor as qtn
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from qibo.config import raise_error
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from qibo.gates.abstract import ParametrizedGate
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from qibo.models import Circuit
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from qibotn.backends.abstract import QibotnBackend
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from qibotn.result import TensorNetworkResult
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GATE_MAP = {
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"h": "H",
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"x": "X",
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"y": "Y",
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"z": "Z",
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"s": "S",
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"t": "T",
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"rx": "RX",
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"ry": "RY",
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"rz": "RZ",
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"u3": "U3", # TODO: check
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"cx": "CX",
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"cnot": "CNOT",
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"cy": "CY",
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"cz": "CZ",
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"iswap": "ISWAP",
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"swap": "SWAP",
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"ccx": "CCX",
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"ccy": "CCY",
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"ccz": "CCZ",
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"toffoli": "TOFFOLI",
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"cswap": "CSWAP",
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"fredkin": "FREDKIN",
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"fsim": "fsim",
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"measure": "measure",
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}
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if not __name__ == "__main__":
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def __init__(self, quimb_backend="numpy", contraction_optimizer="auto-hq"):
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super(self.__class__, self).__init__()
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self.name = "qibotn"
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self.platform = "quimb"
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self.backend = quimb_backend
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self.configure_tn_simulation()
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self.setup_backend_specifics(
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quimb_backend=quimb_backend, contractions_optimizer=contraction_optimizer
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)
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def configure_tn_simulation(
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self,
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ansatz: str = None,
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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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ansatz : str, optional
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The tensor network ansatz to use. Default is `None` and, in this case, a
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generic Circuit Quimb class is used.
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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(
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self, quimb_backend="numpy", contractions_optimizer="auto-hq"
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):
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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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contractions_optimizer: str, optional
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The contractions_optimizer to use for the quimb tensor network simulation.
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"""
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# this is not really working because it does not change the inheritance
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if quimb_backend == "jax":
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import jax.numpy as jnp
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self.np = jnp
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elif quimb_backend == "numpy":
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import numpy as np
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self.np = np
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elif quimb_backend == "torch":
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import torch
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self.np = torch
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else:
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raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}")
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self.backend = quimb_backend
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self.contractions_optimizer = contractions_optimizer
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def execute_circuit(
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self,
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circuit: 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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):
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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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Returns:
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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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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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ValueError, "Initial state not None supported only for MPS ansatz."
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)
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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 nshots:
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frequencies = Counter(circ_quimb.sample(nshots))
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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 = list(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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else:
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frequencies = None
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measured_probabilities = None
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statevector = (
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circ_quimb.to_dense(
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backend=self.backend, optimize=self.contractions_optimizer
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)
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if return_array
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else None
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)
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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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def expectation_observable_symbolic_from_state(
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self, circuit, operators_list, sites_list, coeffs_list, nqubits
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):
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"""
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Compute the expectation value of a symbolic Hamiltonian on a quantum circuit using tensor network contraction.
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This method takes a Qibo circuit, converts it to a Quimb tensor network circuit, and evaluates the expectation value
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of a Hamiltonian specified by three lists of strings: operators, sites, and coefficients.
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The expectation value is computed by summing the contributions from each term in the Hamiltonian, where each term's
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expectation is calculated using Quimb's `local_expectation` function.
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Parameters
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----------
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circuit : qibo.models.Circuit
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The quantum circuit to evaluate, provided as a Qibo circuit object.
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operators_list : list of str
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List of operator strings representing the symbolic Hamiltonian terms.
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sites_list : list of str
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List of strings, each specifying the qubits (sites) the corresponding operator acts on.
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coeffs_list : list of str
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List of strings representing the coefficients for each Hamiltonian term.
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Returns
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-------
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float
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The real part of the expectation value of the Hamiltonian on the given circuit state.
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"""
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quimb_circuit = self._qibo_circuit_to_quimb(
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circuit,
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quimb_circuit_type=qtn.CircuitMPS if self.ansatz == "MPS" else qtn.Circuit,
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gate_opts={"max_bond": self.max_bond_dimension},
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)
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expectation_value = 0.0
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for opstr, sites, coeff in zip(operators_list, sites_list, coeffs_list):
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ops = self._string_to_quimb_operator(opstr)
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coeff = coeff.real
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exp_values = quimb_circuit.local_expectation(
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ops,
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where=sites,
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backend=self.backend,
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optimize=self.contractions_optimizer,
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simplify_sequence="R",
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)
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expectation_value = expectation_value + coeff * exp_values
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return self.np.real(expectation_value)
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def _qibo_circuit_to_quimb(
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self, qibo_circ, quimb_circuit_type=qtn.Circuit, **circuit_kwargs
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):
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"""
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Convert a Qibo Circuit to a Quimb Circuit. Measurement gates are ignored. If are given gates not supported by Quimb, an error is raised.
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Parameters
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----------
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qibo_circ : qibo.models.circuit.Circuit
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The circuit to convert.
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quimb_circuit_type : type
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The Quimb circuit class to use (Circuit, CircuitMPS, etc).
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circuit_kwargs : dict
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Extra arguments to pass to the Quimb circuit constructor.
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Returns
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-------
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circ : quimb.tensor.circuit.Circuit
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The converted circuit.
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"""
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nqubits = qibo_circ.nqubits
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circ = quimb_circuit_type(nqubits, **circuit_kwargs)
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for gate in qibo_circ.queue:
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gname = getattr(gate, "name", None)
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qname = GATE_MAP.get(gname, None)
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if qname == "measure":
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continue
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if qname is None:
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raise_error(ValueError, f"Gate {gname} not supported in Quimb backend.")
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params = getattr(gate, "parameters", ())
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qubits = getattr(gate, "qubits", ())
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is_parametrized = isinstance(gate, ParametrizedGate) and getattr(
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gate, "trainable", True
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)
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if is_parametrized:
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circ.apply_gate(qname, *params, *qubits, parametrized=is_parametrized)
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else:
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circ.apply_gate(
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qname,
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*params,
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*qubits,
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)
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return circ
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def _string_to_quimb_operator(self, op_str):
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"""
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Convert a Pauli string (e.g. 'xzy') to a Quimb operator using '&' chaining.
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Parameters
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----------
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op_str : str
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A string like 'xzy', where each character is one of 'x', 'y', 'z', 'i'.
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Returns
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-------
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qu_op : quimb.Qarray
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The corresponding Quimb operator.
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"""
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op_str = op_str.lower()
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# breakpoint()
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op = qu.pauli(op_str[0])
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for c in op_str[1:]:
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op = op & qu.pauli(c)
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return op
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def QuimbBackend(
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quimb_backend: str = "numpy", contraction_optimizer="auto-hq"
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) -> QibotnBackend:
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bases = (QibotnBackend,)
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methods = {
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"__init__": __init__,
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"configure_tn_simulation": configure_tn_simulation,
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"setup_backend_specifics": setup_backend_specifics,
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"execute_circuit": execute_circuit,
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"expectation_observable_symbolic_from_state": expectation_observable_symbolic_from_state,
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"_qibo_circuit_to_quimb": _qibo_circuit_to_quimb,
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"_string_to_quimb_operator": _string_to_quimb_operator,
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}
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if quimb_backend == "numpy":
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from qibo.backends import NumpyBackend
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bases += (NumpyBackend,)
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elif quimb_backend == "torch":
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from qiboml.backends import PyTorchBackend
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bases += (PyTorchBackend,)
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elif quimb_backend == "jax":
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from qiboml.backends import JaxBackend
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bases += (JaxBackend,)
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else:
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raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}")
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return type("QuimbBackend", bases, methods)(quimb_backend, contraction_optimizer)
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