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_expectation_inputs(self):
"""Make sure the the expectation op fails gracefully on bad inputs."""
n_qubits = 5
batch_size = 5
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])
pauli_sums = util.random_pauli_sums(qubits, 3, batch_size)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'programs must be rank 1'):
# Circuit tensor has too many dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor([circuit_batch]), symbol_names,
symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_names must be rank 1.'):
# symbol_names tensor has too many dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch),
np.array([symbol_names]), symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_values must be rank 2.'):
# symbol_values_array tensor has too many dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
np.array([symbol_values_array]),
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_values must be rank 2.'):
# symbol_values_array tensor has too few dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array[0],
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'pauli_sums must be rank 2.'):
# pauli_sums tensor has too few dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array,
util.convert_to_tensor([x for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'pauli_sums must be rank 2.'):
# pauli_sums tensor has too many dimensions.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array,
util.convert_to_tensor([[[x]] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Unparseable proto'):
# circuit tensor has the right type but invalid values.
tfq_simulate_ops.tfq_simulate_expectation(
['junk'] * batch_size, symbol_names, symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Could not find symbol in parameter map'):
# symbol_names tensor has the right type but invalid values.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), ['junk'],
symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'qubits not found in circuit'):
# pauli_sums tensor has the right type but invalid values.
new_qubits = [cirq.GridQubit(5, 5), cirq.GridQubit(9, 9)]
new_pauli_sums = util.random_pauli_sums(new_qubits, 2, batch_size)
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array,
util.convert_to_tensor([[x] for x in new_pauli_sums]))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Unparseable proto'):
# pauli_sums tensor has the right type but invalid values 2.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array, [['junk']] * batch_size)
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# circuits tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_expectation(
[1.0] * batch_size, symbol_names, symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# symbol_names tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), [0.1234],
symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(tf.errors.UnimplementedError, ''):
# symbol_values tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
[['junk']] * batch_size,
util.convert_to_tensor([[x] for x in pauli_sums]))
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# pauli_sums tensor has the wrong type.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array, [[1.0]] * batch_size)
with self.assertRaisesRegex(TypeError, 'missing'):
# we are missing an argument.
# pylint: disable=no-value-for-parameter
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array)
# pylint: enable=no-value-for-parameter
with self.assertRaisesRegex(TypeError, 'positional arguments'):
# pylint: disable=too-many-function-args
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor(circuit_batch), symbol_names,
symbol_values_array,
util.convert_to_tensor([[x] for x in pauli_sums]), [])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
expected_regex='do not match'):
# wrong op size.
tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor([cirq.Circuit()]), symbol_names,
symbol_values_array.astype(np.float64),
util.convert_to_tensor([[x] for x in pauli_sums]))
res = tfq_simulate_ops.tfq_simulate_expectation(
util.convert_to_tensor([cirq.Circuit() for _ in pauli_sums]),
symbol_names, symbol_values_array.astype(np.float64),
util.convert_to_tensor([[x] for x in pauli_sums]))
self.assertDTypeEqual(res, np.float32)