def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator,
permutations: np.ndarray,
gates: np.ndarray) -> List[int]:
"""
Collects and returns those 'heavy' bitstrings which are output with greater than median
probability among all possible bitstrings on the given qubits.
The method uses the provided wfn_sim to calculate the probability of measuring each bitstring
from the output of the circuit comprised of the given permutations and gates.
:param wfn_sim: a NumpyWavefunctionSimulator that can simulate the provided program
:param permutations: array of depth-many arrays of size n_qubits indicating a qubit permutation
:param gates: depth by num_gates_per_layer many matrix representations of 2q gates.
The first row of matrices is the earliest-time layer of 2q gates applied.
:return: a list of the heavy outputs of the circuit, represented as ints
"""
wfn_sim.reset()
for layer_idx, (perm, layer) in enumerate(zip(permutations, gates)):
for gate_idx, gate in enumerate(layer):
wfn_sim.do_gate_matrix(gate, (perm[gate_idx], perm[gate_idx + 1]))
# Note that probabilities are ordered lexicographically with qubit 0 leftmost.
probabilities = np.abs(wfn_sim.wf.reshape(-1))**2
median_prob = median(probabilities)
# store the integer indices, which implicitly represent the bitstring outcome.
heavy_outputs = [
idx for idx, prob in enumerate(probabilities) if prob > median_prob
]
return heavy_outputs
def single_q_tomo_fixture():
qubits = [0]
qc = get_test_qc(n_qubits=len(qubits))
# Generate random unitary
u_rand = haar_rand_unitary(2 ** 1, rs=np.random.RandomState(52))
state_prep = Program().defgate("RandUnitary", u_rand)
state_prep.inst([("RandUnitary", qubits[0])])
# True state
wfn = NumpyWavefunctionSimulator(n_qubits=1)
psi = wfn.do_gate_matrix(u_rand, qubits=[0]).wf.reshape(-1)
rho_true = np.outer(psi, psi.T.conj())
# Get data from QVM
tomo_expt = generate_state_tomography_experiment(state_prep, qubits)
results = list(measure_observables(qc=qc, tomo_experiment=tomo_expt, n_shots=4000))
return results, rho_true