Adding feature to pass MPS parameters in quimb

This commit is contained in:
vinitha-balachandran
2024-02-23 14:48:30 +08:00
parent b6bd9bdfd5
commit de92060180
3 changed files with 20 additions and 14 deletions

View File

@@ -16,9 +16,11 @@ class QuimbBackend(NumpyBackend):
mps_enabled_value = runcard.get("MPS_enabled")
if mps_enabled_value is True:
self.MPS_enabled = True
self.mps_opts = {"method": "svd", "cutoff": 1e-6, "cutoff_mod": "abs"}
elif mps_enabled_value is False:
self.MPS_enabled = 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")
@@ -74,7 +76,7 @@ class QuimbBackend(NumpyBackend):
)
state = eval.dense_vector_tn_qu(
circuit.to_qasm(), initial_state, is_mps=self.MPS_enabled, backend="numpy"
circuit.to_qasm(), initial_state, self.mps_opts, backend="numpy"
)
if return_array:

View File

@@ -10,7 +10,7 @@ def init_state_tn(nqubits, init_state_sv):
return qtn.tensor_1d.MatrixProductState.from_dense(init_state_sv, dims)
def dense_vector_tn_qu(qasm: str, initial_state, is_mps, backend="numpy"):
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``.
@@ -20,14 +20,9 @@ def dense_vector_tn_qu(qasm: str, initial_state, is_mps, backend="numpy"):
nqubits = int(np.log2(len(initial_state)))
initial_state = init_state_tn(nqubits, initial_state)
if is_mps:
gate_opt = {}
gate_opt["method"] = "svd"
gate_opt["cutoff"] = 1e-6
gate_opt["cutoff_mode"] = "abs"
if mps_opts:
circ_quimb = qtn.circuit.CircuitMPS.from_openqasm2_str(
qasm, psi0=initial_state, gate_opts=gate_opt
qasm, psi0=initial_state, gate_opts=mps_opts
)
else:

View File

@@ -50,6 +50,15 @@ def test_eval(nqubits: int, tolerance: float, is_mps: bool):
qasm_circ = qibo_circ.to_qasm()
# Test quimb
if is_mps:
gate_opt = {}
gate_opt["method"] = "svd"
gate_opt["cutoff"] = 1e-6
gate_opt["cutoff_mode"] = "abs"
result_tn = qibotn.eval_qu.dense_vector_tn_qu(
qasm_circ, init_state_tn, gate_opt, backend=config.quimb.backend
).flatten()
else:
result_tn = qibotn.eval_qu.dense_vector_tn_qu(
qasm_circ, init_state_tn, is_mps, backend=config.quimb.backend
).flatten()