def test_floaty():
""" More tests using a precomputed permanent """
np.random.seed(1234)
m = np.random.uniform(0, 1, 16) + 1j*np.random.uniform(0, 1, 16)
m = m.reshape(4,4)
p = permanent(m)
assert np.allclose(p, -8.766131870776363+1.072095650303524j)
def simulate(input_state, unitary, patterns, mode="probability", **kwargs):
""" Simulates a given circuit, for a given input state, looking at certain terms in the output state """
output_state = defaultdict(complex)
for cols, amplitude in input_state.items():
cols = list(cols)
n1 = normalization(cols)
for rows in patterns:
n2 = normalization(rows)
perm = permanent(unitary[list(rows)][:, cols])
value = amplitude * perm / sqrt(n1 * n2)
output_state[rows] += value
return output_state
def get_amplitudes(input_state, unitary, patterns):
""" Simulates a given circuit, for a given input state, looking at certain terms in the output state """
output_state = State()
for cols, amplitude in input_state.items():
cols = list(cols)
n1 = normalization(cols)
for rows in patterns:
n2 = normalization(rows)
perm = permanent(unitary[list(rows)][:, cols])
value = amplitude * perm / np.sqrt(n1 * n2)
output_state[rows] += value
return output_state
def get_amplitudes(input_state, unitary, patterns):
""" Simulates a given circuit, for a given input state, looking at certain terms in the output state """
output_state = defaultdict(complex)
for cols, amplitude in input_state.items():
cols = list(cols)
n1 = normalization(cols)
for rows in patterns:
n2 = normalization(rows)
perm = permanent(unitary[list(rows)][:, cols])
value = amplitude * perm / np.sqrt(n1 * n2)
if np.abs(value) > precision:
output_state[rows] += value
return output_state
def simulate(input_state, unitary, patterns, mode="probability", **kwargs):
""" Simulates a given circuit, for a given input state, looking at certain terms in the output state """
output_state = defaultdict(complex)
u2 = np.abs(unitary) ** 2
for cols, amplitude in input_state.items():
cols = list(cols)
n1 = normalization(cols)
for rows in patterns:
n2 = normalization(rows)
perm = permanent(unitary[list(rows)][:, cols])
value = amplitude * perm / np.sqrt(n1 * n2)
if value != 0:
output_state[rows] += value
if mode == "probability":
for key, value in output_state.items():
output_state[key] = np.abs(value) ** 2
return output_state
def test_permanent():
""" Dumb tests """
m = np.eye(10, dtype=complex)
assert permanent(m) == 1
m = np.zeros((10, 10), dtype=complex)
assert permanent(m) == 0
def test_error():
""" Should raise a TypeError as we are using the wrong dtype """
m = np.eye(10, dtype=float)
permanent(m)
import permanent
print permanent.permanent('awd')
