def test_correct_padding(self):
"""Test the variable sized circuits are properly padded."""
symbol_names = []
batch_size = 2
n_qubits = 5
qubits1 = cirq.GridQubit.rect(1, n_qubits)
qubits2 = cirq.GridQubit.rect(1, n_qubits + 1)
circuit_batch1, resolver_batch1 = \
util.random_circuit_resolver_batch(
qubits1, batch_size, include_channels=True)
circuit_batch2, resolver_batch2 = \
util.random_circuit_resolver_batch(
qubits2, batch_size, include_channels=True)
p1 = [[resolver[symbol]
for symbol in symbol_names]
for resolver in resolver_batch1]
p2 = [[resolver[symbol]
for symbol in symbol_names]
for resolver in resolver_batch2]
symbol_values_array = np.array(p1 + p2)
n_samples = 10
op_samples = noisy_samples_op.samples(
util.convert_to_tensor(circuit_batch1 + circuit_batch2),
symbol_names, symbol_values_array, [n_samples]).to_list()
a_reps = np.asarray(op_samples[:2])
b_reps = np.asarray(op_samples[2:])
self.assertEqual(a_reps.shape, (2, 10, 5))
self.assertEqual(b_reps.shape, (2, 10, 6))
def test_single_channel(self, channel):
"""Individually test adding just a single channel type to circuits."""
symbol_names = []
batch_size = 3
n_qubits = 5
qubits = cirq.GridQubit.rect(1, n_qubits)
circuit_batch, resolver_batch = \
util.random_circuit_resolver_batch(
qubits, batch_size, include_channels=False)
for i in range(batch_size):
circuit_batch[i] = circuit_batch[i] + channel.on_each(*qubits)
symbol_values_array = np.array(
[[resolver[symbol]
for symbol in symbol_names]
for resolver in resolver_batch])
n_samples = (2**n_qubits) * 1000
op_samples = noisy_samples_op.samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array, [n_samples]).to_list()
op_hists = self._compute_hists(op_samples, n_qubits)
cirq_samples = batch_util.batch_sample(circuit_batch, resolver_batch,
n_samples,
cirq.DensityMatrixSimulator())
cirq_hists = self._compute_hists(cirq_samples, n_qubits)
for a, b in zip(op_hists, cirq_hists):
self.assertLess(stats.entropy(a + 1e-8, b + 1e-8), 0.15)
def test_simulate_consistency(self, batch_size, n_qubits, noisy):
"""Test consistency with batch_util.py simulation."""
symbol_names = ['alpha', 'beta']
qubits = cirq.GridQubit.rect(1, n_qubits)
circuit_batch, resolver_batch = \
util.random_symbol_circuit_resolver_batch(
qubits, symbol_names, batch_size, include_channels=noisy)
symbol_values_array = np.array(
[[resolver[symbol]
for symbol in symbol_names]
for resolver in resolver_batch])
n_samples = 10000
op_samples = noisy_samples_op.samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array, [n_samples]).to_list()
op_hists = self._compute_hists(op_samples, n_qubits)
cirq_samples = batch_util.batch_sample(
circuit_batch, resolver_batch, n_samples,
cirq.DensityMatrixSimulator() if noisy else cirq.Simulator())
cirq_hists = self._compute_hists(cirq_samples, n_qubits)
tol = 1.5 if noisy else 1.0
for a, b in zip(op_hists, cirq_hists):
self.assertLess(stats.entropy(a + 1e-8, b + 1e-8), tol)
def test_correctness_no_circuit(self):
"""Test the correctness with the empty tensor."""
empty_circuit = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)
empty_symbols = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)
empty_values = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32)
empty_n_samples = tf.convert_to_tensor([1], dtype=tf.int32)
out = noisy_samples_op.samples(empty_circuit, empty_symbols,
empty_values, empty_n_samples)
self.assertShapeEqual(np.zeros((0, 0, 0)), out.to_tensor())
def test_correctness_empty(self):
"""Test the expectation for empty circuits."""
empty_circuit = util.convert_to_tensor([cirq.Circuit()])
empty_symbols = tf.convert_to_tensor([], dtype=tf.dtypes.string)
empty_values = tf.convert_to_tensor([[]])
empty_n_samples = tf.convert_to_tensor([1], dtype=tf.int32)
out = noisy_samples_op.samples(empty_circuit, empty_symbols,
empty_values, empty_n_samples)
expected = np.array([[[]]], dtype=np.int8)
self.assertAllClose(out.to_tensor(), expected)
def test_simulate_samples_inputs(self):
"""Make sure the sample op fails gracefully on bad inputs."""
n_qubits = 5
batch_size = 5
num_samples = 10
symbol_names = ['alpha']
qubits = cirq.GridQubit.rect(1, n_qubits)
circuit_batch, resolver_batch = \
util.random_symbol_circuit_resolver_batch(
qubits, symbol_names, batch_size)
symbol_values_array = np.array(
[[resolver[symbol]
for symbol in symbol_names]
for resolver in resolver_batch])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'rank 1. Got rank 2'):
# programs tensor has the wrong shape.
noisy_samples_op.samples(util.convert_to_tensor([circuit_batch]),
symbol_names, symbol_values_array,
[num_samples])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'rank 1. Got rank 2'):
# symbol_names tensor has the wrong shape.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
np.array([symbol_names]),
symbol_values_array, [num_samples])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'rank 2. Got rank 3'):
# symbol_values tensor has the wrong shape.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names,
np.array([symbol_values_array]),
[num_samples])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'rank 2. Got rank 1'):
# symbol_values tensor has the wrong shape 2.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names, symbol_values_array[0],
[num_samples])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'rank 1. Got rank 2'):
# num_samples tensor has the wrong shape.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names, symbol_values_array,
[[num_samples]])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Unparseable proto'):
# programs tensor has the right type, but invalid value.
noisy_samples_op.samples(['junk'] * batch_size, symbol_names,
symbol_values_array, [num_samples])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Could not find symbol in parameter map'):
# symbol_names tensor has the right type, but invalid value.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
['junk'], symbol_values_array,
[num_samples])
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# programs tensor has the wrong type.
noisy_samples_op.samples([1] * batch_size, symbol_names,
symbol_values_array, [num_samples])
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# programs tensor has the wrong type.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch), [1],
symbol_values_array, [num_samples])
with self.assertRaisesRegex(tf.errors.UnimplementedError,
'Cast string to float is not supported'):
# programs tensor has the wrong type.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names, [['junk']] * batch_size,
[num_samples])
with self.assertRaisesRegex(Exception, 'junk'):
# num_samples tensor has the wrong type.
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names, symbol_values_array,
['junk'])
with self.assertRaisesRegex(TypeError, 'missing'):
# too few tensors.
# pylint: disable=no-value-for-parameter
noisy_samples_op.samples(util.convert_to_tensor(circuit_batch),
symbol_names, symbol_values_array)
# pylint: enable=no-value-for-parameter
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
expected_regex='do not match'):
# wrong symbol_values size.
noisy_samples_op.samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array[:int(batch_size * 0.5)], num_samples)