def test_calculate_adj_grad_simple_case_single(self):
"""Make sure the adjoint gradient works on a one symbol for all gate."""
n_qubits = 2
batch_size = 1
symbol_names = ['alpha', 'beta', 'gamma']
qubits = cirq.GridQubit.rect(1, n_qubits)
circuit_batch, resolver_batch = \
[cirq.Circuit(cirq.X(qubits[0]) ** sympy.Symbol('alpha'),
cirq.Y(qubits[1]) ** sympy.Symbol('alpha'),
cirq.CNOT(qubits[0], qubits[1]),
cirq.FSimGate(
-0.56,
sympy.Symbol('alpha'))(qubits[0], qubits[1]))
], [{'alpha': 0.123, 'beta': 0.456, 'gamma': 0.789}]
op_batch = [[cirq.Z(qubits[0]), cirq.X(qubits[1])]
for _ in range(batch_size)]
symbol_values_array = np.array(
[[resolver[symbol] for symbol in symbol_names]
for resolver in resolver_batch])
prev_grads = tf.ones([batch_size, len(op_batch[0])])
out = tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch),
tf.convert_to_tensor(symbol_names),
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor(op_batch), prev_grads)
self.assertAllClose(out, np.array([[1.2993, 0, 0]]), atol=1e-3)
def test_calculate_adj_grad_empty(self):
"""Verify that the empty case is handled gracefully."""
out = tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor([cirq.Circuit()]),
tf.convert_to_tensor([], dtype=tf.dtypes.string),
tf.convert_to_tensor([[]]),
tf.convert_to_tensor([[]], dtype=tf.dtypes.string),
tf.convert_to_tensor([[]]))
self.assertShapeEqual(np.zeros((1, 0)), out)
def test_calculate_adj_grad_no_circuit(self):
"""Verify that the no circuit case is handled gracefully."""
out = tfq_adj_grad_op.tfq_adj_grad(
tf.raw_ops.Empty(shape=(0, ), dtype=tf.string),
tf.raw_ops.Empty(shape=(0, ), dtype=tf.string),
tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32),
tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string),
tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32),
)
self.assertShapeEqual(np.zeros((0, 0)), out)
def test_adj_grad_inputs(self):
"""Make sure that 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)
upstream_grads = np.ones((batch_size, len(symbol_names)))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'programs must be rank 1'):
# Circuit tensor has too many dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor([circuit_batch]), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_names must be rank 1.'):
# symbol_names tensor has too many dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch),
np.array([symbol_names]),
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_values must be rank 2.'):
# symbol_values_array tensor has too many dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(np.array([symbol_values_array])),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'symbol_values must be rank 2.'):
# symbol_values_array tensor has too few dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array[0]),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'pauli_sums must be rank 2.'):
# pauli_sums tensor has too few dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([x for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'pauli_sums must be rank 2.'):
# pauli_sums tensor has too many dimensions.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[[x]] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Unparseable proto'):
# circuit tensor has the right type but invalid values.
tfq_adj_grad_op.tfq_adj_grad(
['junk'] * batch_size, symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Could not find symbol in parameter map'):
# symbol_names tensor has the right type but invalid values.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), ['junk'],
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
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_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in new_pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Unparseable proto'):
# pauli_sums tensor has the right type but invalid values 2.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
[['junk']] * batch_size, tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# circuits tensor has the wrong type.
tfq_adj_grad_op.tfq_adj_grad(
[1.0] * batch_size, symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# symbol_names tensor has the wrong type.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), [0.1234],
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.UnimplementedError, ''):
# symbol_values tensor has the wrong type.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
[['junk']] * batch_size,
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(TypeError, 'Cannot convert'):
# pauli_sums tensor has the wrong type.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
[[1.0]] * batch_size, tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(TypeError, 'missing'):
# we are missing an argument.
# pylint: disable=no-value-for-parameter
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
tf.convert_to_tensor(upstream_grads))
# pylint: enable=no-value-for-parameter
with self.assertRaisesRegex(TypeError, 'positional arguments'):
# pylint: disable=too-many-function-args
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads), [])
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
expected_regex='do not match'):
# wrong op size.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor([cirq.Circuit()]), symbol_names,
symbol_values_array.astype(np.float64),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor(upstream_grads))
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
expected_regex='rank 2'):
# wrong grad shape.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor([upstream_grads]))
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
expected_regex='gradients and circuits do not match'):
# wrong grad batch size.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor([[0 for i in range(len(symbol_names))]]))
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
expected_regex='gradients and pauli sum dimension do not match'
):
# wrong grad inner size.
tfq_adj_grad_op.tfq_adj_grad(
util.convert_to_tensor(circuit_batch), symbol_names,
tf.convert_to_tensor(symbol_values_array),
util.convert_to_tensor([[x] for x in pauli_sums]),
tf.convert_to_tensor([[0, 0]
for _ in range(len(circuit_batch))]))
def differentiate_analytic(self, programs, symbol_names, symbol_values,
pauli_sums, forward_pass_vals, grad):
return tfq_adj_grad_op.tfq_adj_grad(programs, symbol_names,
symbol_values, pauli_sums, grad)
