def test_linear_combination_instantiate(self):
"""Test LinearCombinationDifferentiator type checking."""
linear_combination.LinearCombination([1, 1], [1, 0])
with self.assertRaisesRegex(TypeError,
expected_regex="weights must be"):
linear_combination.LinearCombination("junk", [1, 0])
with self.assertRaisesRegex(TypeError,
expected_regex="perturbations must be"):
linear_combination.LinearCombination([1, 1], "junk")
with self.assertRaisesRegex(TypeError,
expected_regex="weight in weights"):
linear_combination.LinearCombination([1, "junk"], [1, 0])
with self.assertRaisesRegex(
TypeError, expected_regex="perturbation in perturbations"):
linear_combination.LinearCombination([1, 1], [1, "junk"])
with self.assertRaisesRegex(ValueError, expected_regex="length"):
linear_combination.LinearCombination([1, 1, 1], [1, 0])
with self.assertRaisesRegex(ValueError, expected_regex="at least two"):
linear_combination.LinearCombination([1], [1])
with self.assertRaisesRegex(ValueError, expected_regex="unique"):
linear_combination.LinearCombination([1, 1], [1, 1])
def test_no_gradient_circuits(self):
"""Confirm LinearCombination differentiator has no gradient circuits."""
dif = linear_combination.LinearCombination([1, 1], [1, 0])
with self.assertRaisesRegex(NotImplementedError,
expected_regex="not currently available"):
_ = dif.get_gradient_circuits(None, None, None)
def test_get_gradient_circuits(self):
"""Test that the correct objects are returned."""
# Minimal linear combination.
input_weights = [1.0, -0.5]
input_perturbations = [1.0, -1.5]
diff = linear_combination.LinearCombination(input_weights,
input_perturbations)
# Circuits to differentiate.
symbols = [sympy.Symbol("s0"), sympy.Symbol("s1")]
q0 = cirq.GridQubit(0, 0)
q1 = cirq.GridQubit(1, 2)
input_programs = util.convert_to_tensor([
cirq.Circuit(cirq.X(q0)**symbols[0],
cirq.ry(symbols[1])(q1)),
cirq.Circuit(cirq.rx(symbols[0])(q0),
cirq.Y(q1)**symbols[1]),
])
input_symbol_names = tf.constant([str(s) for s in symbols])
input_symbol_values = tf.constant([[1.5, -2.7], [-0.3, 0.9]])
# For each program in the input batch: LinearCombination creates a copy
# of that program for each symbol in the batch; then for each symbol,
# the program is copied for each non-zero perturbation; finally, a
# single copy is added for the zero perturbation (no zero pert here).
expected_batch_programs = tf.stack([[input_programs[0]] * 4,
[input_programs[1]] * 4])
expected_new_symbol_names = input_symbol_names
# For each program in the input batch: first, the input symbol_values
# for the program are tiled to the number of copies in the output.
tiled_symbol_values = tf.stack([[input_symbol_values[0]] * 4,
[input_symbol_values[1]] * 4])
# Then we create the tensor of perturbations to apply to these symbol
# values: for each symbol we tile out the non-zero perturbations at that
# symbol's index, keeping all the other symbol perturbations at zero.
# Perturbations are the same for each program.
single_program_perturbations = tf.stack([[input_perturbations[0], 0.0],
[input_perturbations[1], 0.0],
[0.0, input_perturbations[0]],
[0.0,
input_perturbations[1]]])
tiled_perturbations = tf.stack(
[single_program_perturbations, single_program_perturbations])
# Finally we add the perturbations to the original symbol values.
expected_batch_symbol_values = tiled_symbol_values + tiled_perturbations
# The weights for LinearCombination is the same for every program.
individual_batch_weights = tf.stack(
[[input_weights[0], input_weights[1]],
[input_weights[0], input_weights[1]]])
expected_batch_weights = tf.stack(
[individual_batch_weights, individual_batch_weights])
# The mapper selects the expectations.
single_program_mapper = tf.constant([[0, 1], [2, 3]])
expected_batch_mapper = tf.tile(
tf.expand_dims(single_program_mapper, 0), [2, 1, 1])
(test_batch_programs, test_new_symbol_names, test_batch_symbol_values,
test_batch_weights, test_batch_mapper) = diff.get_gradient_circuits(
input_programs, input_symbol_names, input_symbol_values)
self.assertAllEqual(expected_batch_programs, test_batch_programs)
self.assertAllEqual(expected_new_symbol_names, test_new_symbol_names)
self.assertAllClose(expected_batch_symbol_values,
test_batch_symbol_values,
atol=1e-6)
self.assertAllClose(expected_batch_weights,
test_batch_weights,
atol=1e-6)
self.assertAllEqual(expected_batch_mapper, test_batch_mapper)