59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
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",
|
|
):
|
|
"""Create a tensor network representation of the circuit."""
|
|
|
|
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):
|
|
"""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, is_mps, backend="numpy"):
|
|
"""Evaluate QASM with Quimb.
|
|
|
|
backend (quimb): numpy, cupy, jax. Passed to ``opt_einsum``.
|
|
"""
|
|
circuit = QiboCircuit.from_qasm(qasm)
|
|
if initial_state is not None:
|
|
initial_state = init_state_tn(circuit.nqubits, initial_state)
|
|
circ_quimb = from_qibo(circuit, is_mps, psi0=initial_state)
|
|
interim = circ_quimb.psi.full_simplify(seq="DRC")
|
|
amplitudes = interim.to_dense(backend=backend)
|
|
|
|
return amplitudes
|