def test_save_nmeas_estimate(self):
K_coeff = 0.5646124437984263
nterms = 14
frame_meas = np.array(
[0.03362557, 0.03362557, 0.03362557, 0.03362557, 0.43011016])
save_nmeas_estimate(
nmeas=K_coeff,
nterms=nterms,
filename="hamiltonian_analysis.json",
frame_meas=frame_meas,
)
K_coeff_, nterms_, frame_meas_ = load_nmeas_estimate(
"hamiltonian_analysis.json")
assert K_coeff == K_coeff_
assert nterms == nterms_
assert frame_meas.tolist() == frame_meas_.tolist()
remove_file_if_exists("hamiltonian_analysis.json")
def grouped_hamiltonian_analysis(
groups: str,
expectation_values: Optional[str] = None,
):
"""Calculates the number of measurements required for computing
the expectation value of a qubit hamiltonian, where co-measurable terms
are grouped as a list of QubitOperators.
We are assuming the exact expectation values are provided
(i.e. infinite number of measurements or simulations without noise)
M ~ (\sum_{i} prec(H_i)) ** 2.0 / (epsilon ** 2.0)
where prec(H_i) is the precision (square root of the variance)
for each group of co-measurable terms H_{i}. It is computed as
prec(H_{i}) = \sum{ab} |h_{a}^{i}||h_{b}^{i}| cov(O_{a}^{i}, O_{b}^{i})
where h_{a}^{i} is the coefficient of the a-th operator, O_{a}^{i}, in the
i-th group. Covariances are assumed to be zero for a != b:
cov(O_{a}^{i}, O_{b}^{i}) = <O_{a}^{i} O_{b}^{i}> - <O_{a}^{i}> <O_{b}^{i}> = 0
ARGS:
groups (str): The name of a file containing a list of QubitOperator objects,
where each element in the list is a group of co-measurable terms.
expectation_values (str): The name of a file containing a single ExpectationValues
object with all expectation values of the operators contained in groups.
If absent, variances are assumed to be maximal, i.e. 1.
NOTE: YOU HAVE TO MAKE SURE THAT THE ORDER OF EXPECTATION VALUES MATCHES
THE ORDER OF THE TERMS IN THE *GROUPED* TARGET QUBIT OPERATOR,
OTHERWISE THIS FUNCTION WILL NOT RETURN THE CORRECT RESULT.
"""
grouped_operator = load_qubit_operator_set(groups)
if expectation_values is not None:
expecval = load_expectation_values(expectation_values)
else:
expecval = None
K_coeff, nterms, frame_meas = estimate_nmeas_for_frames(
grouped_operator, expecval)
save_nmeas_estimate(
nmeas=K_coeff,
nterms=nterms,
frame_meas=frame_meas,
filename="hamiltonian_analysis.json",
)
def hamiltonian_analysis(
qubit_operator: str,
decomposition_method: str = "greedy",
expectation_values: Optional[str] = None,
):
operator = load_qubit_operator(qubit_operator)
if expectation_values is not None:
expecval = load_expectation_values(expectation_values)
else:
expecval = None
K_coeff, nterms, frame_meas = estimate_nmeas_for_operator(
operator, decomposition_method, expecval)
save_nmeas_estimate(
nmeas=K_coeff,
nterms=nterms,
frame_meas=frame_meas,
filename="hamiltonian_analysis.json",
)
def hamiltonian_analysis(
qubit_operator: str,
decomposition_method: str = "greedy",
expectation_values: str = "None",
):
operator = load_qubit_operator(qubit_operator)
if decomposition_method != "greedy-sorted" and decomposition_method != "greedy":
raise ValueError(
f'Decomposition method {decomposition_method} is not supported')
if expectation_values != "None":
expecval = load_expectation_values(expectation_values)
else:
expecval = None
K_coeff, nterms, frame_meas = estimate_nmeas(operator,
decomposition_method,
expecval)
save_nmeas_estimate(nmeas=K_coeff,
nterms=nterms,
frame_meas=frame_meas,
filename='hamiltonian_analysis.json')