def _positivity_result(evals, qpt=False):
"""Check if eigenvalues are positive"""
cond = np.sum(np.abs(evals[evals < 0]))
is_pos = bool(np.isclose(cond, 0))
name = "completely_positive" if qpt else "positive"
result = AnalysisResultData(name, is_pos)
if not is_pos:
result.extra = {"delta": cond}
return result
def _tp_result(evals, evecs):
"""Check if QPT channel is trace preserving"""
size = len(evals)
dim = int(np.sqrt(size))
mats = np.reshape(evecs.T, (size, dim, dim), order="F")
kraus_cond = np.einsum("i,ija,ijb->ab", evals, mats.conj(), mats)
cond = np.sum(np.abs(la.eigvalsh(kraus_cond - np.eye(dim))))
is_tp = bool(np.isclose(cond, 0))
result = AnalysisResultData("trace_preserving", is_tp)
if not is_tp:
result.extra = {"delta": cond}
return result
def _tp_result(
state_result: AnalysisResultData,
input_dim: int = 1,
) -> AnalysisResultData:
"""Check if QPT channel is trace preserving"""
evals = state_result.extra["eigvals"]
evecs = state_result.extra["eigvecs"]
size = len(evals)
output_dim = size // input_dim
mats = np.reshape(evecs.T, (size, output_dim, input_dim), order="F")
kraus_cond = np.einsum("i,ija,ijb->ab", evals, mats.conj(), mats)
cond = np.sum(np.abs(la.eigvalsh(kraus_cond - np.eye(input_dim))))
is_tp = bool(np.isclose(cond, 0))
result = AnalysisResultData("trace_preserving", is_tp)
if not is_tp:
result.extra = {"delta": cond}
return result