def test_symbol_values_type(self, symbol_type):
"""Tests all three ops for the different types. """
qubit = cirq.GridQubit(0, 0)
circuits = util.convert_to_tensor([cirq.Circuit(cirq.H(qubit))])
symbol_names = ['symbol']
symbol_values = tf.convert_to_tensor([[1]], dtype=symbol_type)
pauli_sums = util.random_pauli_sums([qubit], 3, 1)
pauli_sums = util.convert_to_tensor([[x] for x in pauli_sums])
result = tfq_simulate_ops.tfq_simulate_state(circuits, symbol_names,
symbol_values)
self.assertDTypeEqual(result, np.complex64)
result = tfq_simulate_ops.tfq_simulate_expectation(
circuits, symbol_names, symbol_values, pauli_sums)
self.assertDTypeEqual(result, np.float32)
result = tfq_simulate_ops.tfq_simulate_samples(circuits, symbol_names,
symbol_values, [100])
self.assertDTypeEqual(result, np.int8)
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(ValueError, 'rank 1 but is rank 2'):
# programs tensor has the wrong shape.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor([circuit_batch]), symbol_names,
symbol_values_array, [num_samples])
with self.assertRaisesRegex(ValueError, 'rank 1 but is rank 2'):
# symbol_names tensor has the wrong shape.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch),
np.array([symbol_names]), symbol_values_array, [num_samples])
with self.assertRaisesRegex(ValueError, 'rank 2 but is rank 3'):
# symbol_values tensor has the wrong shape.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), symbol_names,
np.array([symbol_values_array]), [num_samples])
with self.assertRaisesRegex(ValueError, 'rank 2 but is rank 1'):
# symbol_values tensor has the wrong shape 2.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array[0], [num_samples])
with self.assertRaisesRegex(ValueError, 'rank 0 but is rank 1'):
# num_samples tensor has the wrong shape.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), ['junk'],
symbol_values_array, [num_samples])
with self.assertRaisesRegex(TypeError, 'Expected string'):
# programs tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_samples([1] * batch_size,
symbol_names,
symbol_values_array,
[num_samples])
with self.assertRaisesRegex(TypeError, 'Expected string'):
# programs tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), symbol_names,
[['junk']] * batch_size, [num_samples])
with self.assertRaisesRegex(Exception, 'not supported'):
# num_samples tensor has the wrong shape.
tfq_simulate_ops.tfq_simulate_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
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array)
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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_samples([1] * batch_size,
symbol_names,
symbol_values_array,
[num_samples])
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# programs tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_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 shape.
tfq_simulate_ops.tfq_simulate_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
tfq_simulate_ops.tfq_simulate_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.
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array[:int(batch_size * 0.5)], num_samples)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
expected_regex='cirq.Channel'):
# attempting to use noisy circuit.
noisy_circuit = cirq.Circuit(cirq.depolarize(0.3).on_each(*qubits))
tfq_simulate_ops.tfq_simulate_samples(
util.convert_to_tensor([noisy_circuit for _ in circuit_batch]),
symbol_names, symbol_values_array, [num_samples])