def assert_unitary_gate_converts_correctly(gate: cirq.Gate):
n = gate.num_qubits()
for pre, post in solve_tableau(gate).items():
# Create a circuit that measures pre before the gate times post after the gate.
# If the gate is translated correctly, the measurement will always be zero.
c = stim.Circuit()
c.append_operation("H", range(n))
for i in range(n):
c.append_operation("CNOT", [i, i + n])
c.append_operation("H", [2 * n])
for q, p in pre.items():
c.append_operation(f"C{p}", [2 * n, q.x])
qs = cirq.LineQubit.range(n)
conv_gate, _ = cirq_circuit_to_stim_data(cirq.Circuit(gate(*qs)),
q2i={q: q.x
for q in qs})
c += conv_gate
for q, p in post.items():
c.append_operation(f"C{p}", [2 * n, q.x])
if post.coefficient == -1:
c.append_operation("Z", [2 * n])
c.append_operation("H", [2 * n])
c.append_operation("M", [2 * n])
correct = not np.any(c.compile_sampler().sample_bit_packed(10))
assert correct, f"{gate!r} failed to turn {pre} into {post}.\nConverted to:\n{conv_gate}\n"
def test_noisy_gate_conversions_compiled_sampler(gate: cirq.Gate):
# Create test circuit that uses superdense coding to quantify arbitrary Pauli error mixtures.
n = gate.num_qubits()
qs = cirq.LineQubit.range(n)
circuit = cirq.Circuit(
cirq.H.on_each(qs),
[cirq.CNOT(q, q + n) for q in qs],
gate(*qs),
[cirq.CNOT(q, q + n) for q in qs],
cirq.H.on_each(qs),
)
expected_rates = cirq.final_density_matrix(circuit).diagonal().real
# Convert test circuit to Stim and sample from it.
stim_circuit, _ = cirq_circuit_to_stim_data(circuit + cirq.measure(
*sorted(circuit.all_qubits())[::-1]))
sample_count = 10000
samples = stim_circuit.compile_sampler().sample_bit_packed(
sample_count).flat
unique, counts = np.unique(samples, return_counts=True)
# Compare sample rates to expected rates.
for value, count in zip(unique, counts):
expected_rate = expected_rates[value]
actual_rate = count / sample_count
allowed_variation = 5 * (expected_rate *
(1 - expected_rate) / sample_count)**0.5
if not 0 <= expected_rate - allowed_variation <= 1:
raise ValueError(
"Not enough samples to bound results away from extremes.")
assert abs(expected_rate - actual_rate) < allowed_variation, (
f"Sample rate {actual_rate} is over 5 standard deviations away from {expected_rate}.\n"
f"Gate: {gate}\n"
f"Test circuit:\n{circuit}\n"
f"Converted circuit:\n{stim_circuit}\n")
def test_more_unitary_gate_conversions():
for p in [1, 1j, -1, -1j]:
assert_unitary_gate_converts_correctly(p *
cirq.DensePauliString("IXYZ"))
assert_unitary_gate_converts_correctly(
(p * cirq.DensePauliString("IXYZ")).controlled(1))
a, b = cirq.LineQubit.range(2)
c, _ = cirq_circuit_to_stim_data(
cirq.Circuit(cirq.H(a), cirq.CNOT(a, b), cirq.measure(a, b),
cirq.reset(a)))
assert (str(c).strip() == """
H 0
CX 0 1
M 0 1
R 0
""".strip())
def test_tableau_simulator_error_mechanisms(gate: cirq.Gate):
# Technically this be a test of the `stim` package itself, but it's so convenient to compare to cirq.
# Create test circuit that uses superdense coding to quantify arbitrary Pauli error mixtures.
n = gate.num_qubits()
qs = cirq.LineQubit.range(n)
circuit = cirq.Circuit(
cirq.H.on_each(qs),
[cirq.CNOT(q, q + n) for q in qs],
gate(*qs),
[cirq.CNOT(q, q + n) for q in qs],
cirq.H.on_each(qs),
)
expected_rates = cirq.final_density_matrix(circuit).diagonal().real
# Convert test circuit to Stim and sample from it.
stim_circuit, _ = cirq_circuit_to_stim_data(circuit + cirq.measure(
*sorted(circuit.all_qubits())[::-1]))
sample_count = 10000
samples = []
for _ in range(sample_count):
sim = stim.TableauSimulator()
sim.do(stim_circuit)
s = 0
for k, v in enumerate(sim.current_measurement_record()):
s |= v << k
samples.append(s)
unique, counts = np.unique(samples, return_counts=True)
# Compare sample rates to expected rates.
for value, count in zip(unique, counts):
expected_rate = expected_rates[value]
actual_rate = count / sample_count
allowed_variation = 5 * (expected_rate *
(1 - expected_rate) / sample_count)**0.5
if not 0 <= expected_rate - allowed_variation <= 1:
raise ValueError(
"Not enough samples to bound results away from extremes.")
assert abs(expected_rate - actual_rate) < allowed_variation, (
f"Sample rate {actual_rate} is over 5 standard deviations away from {expected_rate}.\n"
f"Gate: {gate}\n"
f"Test circuit:\n{circuit}\n"
f"Converted circuit:\n{stim_circuit}\n")