def test_moment_preservation(self):
"""Test Moment-structure preservation."""
t = sympy.Symbol('t')
r = sympy.Symbol('r')
qubits = cirq.GridQubit.rect(1, 6)
circuit_batch = [
cirq.Circuit(
cirq.Moment([cirq.H(q) for q in qubits]),
cirq.Moment([
cirq.X(qubits[4]),
cirq.PhasedXPowGate(phase_exponent=np.random.random() *
t).on(qubits[5]),
cirq.ISwapPowGate(exponent=np.random.random() * t).on(
qubits[0], qubits[1]),
cirq.FSimGate(theta=np.random.random() * t,
phi=np.random.random() * r).on(
qubits[2], qubits[3])
]), cirq.Moment([cirq.H(q) for q in qubits]))
]
inputs = util.convert_to_tensor(circuit_batch)
outputs = tfq_ps_util_ops.tfq_ps_decompose(inputs)
decomposed_programs = util.from_tensor(outputs)
# Now all programs don't have ISWAP & PhasedXPowGate because ISWAP has
# 3 eigenvalues and PhasedXPowGate doesn't have _eigen_components.
# Check if two programs are identical.
rand_resolver = {'t': np.random.random(), 'r': np.random.random()}
self.assertAllClose(cirq.unitary(
cirq.resolve_parameters(circuit_batch[0], rand_resolver)),
cirq.unitary(
cirq.resolve_parameters(decomposed_programs[0],
rand_resolver)),
atol=1e-5)
# Check if the Moments are conserved.
max_decomposed_length = 3
n_non_decomposed_moments = 2
self.assertEqual(len(decomposed_programs[0]),
n_non_decomposed_moments + max_decomposed_length)
# Total length of Moments = 5
# The non-decomposed moments should be the same.
self.assertEqual(decomposed_programs[0][0], circuit_batch[0][0])
self.assertEqual(decomposed_programs[0][-1], circuit_batch[0][-1])
# Check paralellized decompose gates in Moment[1]~[3].
# The target ops are replaced by the first decomposition gates. It means
# the first Moment has exactly the same number of gate ops.
self.assertEqual(len(decomposed_programs[0][1]),
len(circuit_batch[0][1]))
# From the second Moments, the Moments only have decomposition gates.
# In this example, two ISwapPowGate & one PhasedXPowGate are located.
# Since PhasedXPowGate, ISwapPowGate, FSimGate has 3, 2, 3 result gates
# Moment[2] have 3 gate ops and Moment[3] have 2 gate ops.
self.assertEqual(len(decomposed_programs[0][2]), 3)
self.assertEqual(len(decomposed_programs[0][3]), 2)
def test_iswap_gate_test(self):
"""Test 1 ISwapPowGate decomposition."""
t = sympy.Symbol('t')
qubits = cirq.GridQubit.rect(1, 2)
circuit = cirq.Circuit(
cirq.ISwapPowGate(exponent=np.random.random() * t).on(*qubits))
inputs = util.convert_to_tensor([circuit])
outputs = tfq_ps_util_ops.tfq_ps_decompose(inputs)
decomposed_programs = util.from_tensor(outputs)
rand_resolver = {'t': np.random.random()}
self.assertAllClose(cirq.unitary(
cirq.resolve_parameters(circuit, rand_resolver)),
cirq.unitary(
cirq.resolve_parameters(decomposed_programs[0],
rand_resolver)),
atol=1e-5)
def test_more_complex_moment_preservation(self):
"""Test Moment-structure preservation."""
circuit_batch = _complex_test_circuit()
inputs = util.convert_to_tensor(circuit_batch)
outputs = tfq_ps_util_ops.tfq_ps_decompose(inputs)
decomposed_programs = util.from_tensor(outputs)
# Now all programs don't have ISWAP & PhasedXPowGate because ISWAP has
# 3 eigenvalues and PhasedXPowGate doesn't have _eigen_components.
# Check if two programs are identical.
rand_resolver = {'t': np.random.random(), 'r': np.random.random()}
for i in range(3):
self.assertAllClose(cirq.unitary(
cirq.resolve_parameters(circuit_batch[i], rand_resolver)),
cirq.unitary(
cirq.resolve_parameters(
decomposed_programs[i],
rand_resolver)),
atol=1e-5)
# Check if the Moments are conserved.
# Circuit 1.
max_decomposed_length = 3
n_non_decomposed_moments = 2
self.assertEqual(len(decomposed_programs[0]),
n_non_decomposed_moments + max_decomposed_length)
# Total length of Moments = 5
# The non-decomposed moments should be the same.
self.assertEqual(decomposed_programs[0][0], circuit_batch[0][0])
self.assertEqual(decomposed_programs[0][-1], circuit_batch[0][-1])
# Check paralellized decompose gates in Moment[1]~[3].
# The target ops are replaced by the first decomposition gates. It means
# the first Moment has exactly the same number of gate ops.
self.assertEqual(len(decomposed_programs[0][1]),
len(circuit_batch[0][1]))
# From the second Moments, the Moments only have decomposition gates.
# In this example, two ISwapPowGate & one PhasedXPowGate are located.
# Since PhasedXPowGate, ISwapPowGate, FSimGate has 3, 2, 3 result gates
# Moment[2] have 3 gate ops and Moment[3] have 2 gate ops.
self.assertEqual(len(decomposed_programs[0][2]), 3)
self.assertEqual(len(decomposed_programs[0][3]), 2)
# Circuit 2. two FSimGates.
self.assertEqual(len(decomposed_programs[1]),
2 * max_decomposed_length)
# Circuit 3. one PXP between two ISwapPowGates.
self.assertEqual(len(decomposed_programs[2]), max_decomposed_length)
def test_phased_x_pow_gate_test(self):
"""Test 1 PhasedXPowGate decomposition."""
t = sympy.Symbol('t')
r = sympy.Symbol('r')
q = cirq.GridQubit(0, 0)
circuit = cirq.Circuit(
cirq.PhasedXPowGate(phase_exponent=np.random.random() * r,
exponent=np.random.random() * t).on(q))
inputs = util.convert_to_tensor([circuit])
outputs = tfq_ps_util_ops.tfq_ps_decompose(inputs)
decomposed_programs = util.from_tensor(outputs)
rand_resolver = {'t': np.random.random(), 'r': np.random.random()}
self.assertAllClose(cirq.unitary(
cirq.resolve_parameters(circuit, rand_resolver)),
cirq.unitary(
cirq.resolve_parameters(decomposed_programs[0],
rand_resolver)),
atol=1e-5)
def test_decompose_with_complex_circuit(self):
"""Test decompose with complex circuit."""
names = ['CLAE', 'HRYV', 'IRKB', 'LKRV', 'PJOU', 'CJKX', 'NASW']
# Test circuit has a Moment with 1) FSimGate & PhasedXPowGate,
# 2) PhasedXPowGate & ISwapPowGate and 3) FSimGate & ISwapPowGate.
# Be careful, they are not decomposed if not parameterized.
circuit_batch = [
cirq.Circuit([
cirq.Moment(operations=[
cirq.FSimGate(theta=0.10338130973488413 *
sympy.Symbol('CLAE'),
phi=0.10338130973488413 *
sympy.Symbol('IRKB')).
on(cirq.GridQubit(0, 2), cirq.GridQubit(0, 3)),
cirq.PhasedXPowGate(phase_exponent=1.0,
exponent=0.86426029696045281 *
sympy.Symbol('HRYV')).on(
cirq.GridQubit(0, 1)),
]),
cirq.Moment(operations=[
cirq.Y.on(cirq.GridQubit(0, 3)),
cirq.Z.on(cirq.GridQubit(0, 0)),
cirq.FSimGate(theta=1, phi=1).on(cirq.GridQubit(0, 1),
cirq.GridQubit(0, 2)),
]),
cirq.Moment(operations=[
(cirq.CNOT**(0.92874230274398684 * sympy.Symbol('IRKB'))
).on(cirq.GridQubit(0, 1), cirq.GridQubit(0, 2)),
]),
cirq.Moment(operations=[
cirq.PhasedXPowGate(phase_exponent=sympy.Symbol('PJOU'),
exponent=0.2081415255258906 *
sympy.Symbol('LKRV')).on(
cirq.GridQubit(0, 2)),
(cirq.ISWAP**(0.32860954996781722 * sympy.Symbol('PJOU'))
).on(cirq.GridQubit(0, 1), cirq.GridQubit(0, 3)),
]),
cirq.Moment(operations=[
cirq.PhasedXPowGate(phase_exponent=sympy.Symbol('CJKX')).on(
cirq.GridQubit(0, 1)),
cirq.ZZ.on(cirq.GridQubit(0, 0), cirq.GridQubit(0, 3)),
(cirq.X**(0.6826594585474709 *
sympy.Symbol('HRYV'))).on(cirq.GridQubit(0, 2)),
]),
cirq.Moment(operations=[
(cirq.ZZ**(0.18781276022427218 * sympy.Symbol('PJOU'))
).on(cirq.GridQubit(0, 0), cirq.GridQubit(0, 3)),
]),
cirq.Moment(operations=[
cirq.Y.on(cirq.GridQubit(0, 0)),
]),
cirq.Moment(operations=[
cirq.FSimGate(theta=0.13793763138552417 *
sympy.Symbol('CJKX'),
phi=0.13793763138552417 *
sympy.Symbol('PJOU')).
on(cirq.GridQubit(0, 2), cirq.GridQubit(0, 3)),
(cirq.ISWAP**(0.028165738453673095 * sympy.Symbol('NASW'))
).on(cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)),
]),
cirq.Moment(operations=[
cirq.FSimGate(theta=0.74356520426349459 *
sympy.Symbol('CJKX'),
phi=0.74356520426349459 *
sympy.Symbol('NASW')).
on(cirq.GridQubit(0, 3), cirq.GridQubit(0, 0)),
]),
cirq.Moment(operations=[
cirq.CNOT.on(cirq.GridQubit(0, 0), cirq.GridQubit(0, 2)),
cirq.SWAP.on(cirq.GridQubit(0, 3), cirq.GridQubit(0, 1)),
]),
cirq.Moment(operations=[
cirq.H.on(cirq.GridQubit(0, 3)),
cirq.H.on(cirq.GridQubit(0, 2)),
cirq.CNOT.on(cirq.GridQubit(0, 1), cirq.GridQubit(0, 0)),
]),
cirq.Moment(operations=[
cirq.CNOT.on(cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)),
cirq.YY.on(cirq.GridQubit(0, 2), cirq.GridQubit(0, 3)),
]),
cirq.Moment(operations=[
cirq.CZ.on(cirq.GridQubit(0, 1), cirq.GridQubit(0, 0)),
cirq.CNOT.on(cirq.GridQubit(0, 2), cirq.GridQubit(0, 3)),
]),
cirq.Moment(operations=[
cirq.FSimGate(theta=1, phi=1).on(cirq.GridQubit(0, 0),
cirq.GridQubit(0, 2)),
cirq.CNOT.on(cirq.GridQubit(0, 3), cirq.GridQubit(0, 1)),
]),
cirq.Moment(operations=[
cirq.FSimGate(theta=1, phi=1).on(cirq.GridQubit(0, 0),
cirq.GridQubit(0, 3)),
cirq.SWAP.on(cirq.GridQubit(0, 2), cirq.GridQubit(0, 1)),
]),
cirq.Moment(operations=[
cirq.Y.on(cirq.GridQubit(0, 0)),
cirq.PhasedXPowGate(
phase_exponent=1.0).on(cirq.GridQubit(0, 2)),
cirq.FSimGate(theta=1, phi=1).on(cirq.GridQubit(0, 1),
cirq.GridQubit(0, 3)),
]),
])
]
# Decompose programs.
inputs = util.convert_to_tensor(circuit_batch)
outputs = tfq_ps_util_ops.tfq_ps_decompose(inputs)
decomposed_programs = util.from_tensor(outputs)
self.assertEqual(len(decomposed_programs), len(circuit_batch))
# Original programs has parameterized ISP, PXP, FSIM, but this result
# has no such gates at all. All parameterized gates have at most two
# eigenvalues. There are still ISwap and PhasedX(1.0) because they are
# not parameterized, which doesn't affect ParameterShift differentiation
# at all.
for program in decomposed_programs:
for moment in program:
for gate_op in moment:
# Consider parameterized gates only
if cirq.is_parameterized(gate_op.gate):
# Check I. The gate should have _eigen_components.
self.assertTrue(
hasattr(gate_op.gate, '_eigen_components'))
# Check II. The gate should have two eigen values.
self.assertEqual(len(gate_op.gate._eigen_components()),
2, gate_op.gate)
# Now all programs don't have ISWAP & PhasedXPowGate because ISWAP has
# 3 eigenvalues and PhasedXPowGate doesn't have _eigen_components.
# Check if two programs are identical.
rand_resolver = {name: np.random.random() for name in names}
self.assertAllClose(cirq.unitary(
cirq.resolve_parameters(circuit_batch[0], rand_resolver)),
cirq.unitary(
cirq.resolve_parameters(decomposed_programs[0],
rand_resolver)),
atol=1e-5)
def parse_programs(programs,
symbol_names,
symbol_values,
n_symbols,
n_shifts=2):
"""Helper function to get parameter-shifted programs after parsing programs.
It follows:
1. Decomposes given programs with `tfq_ps_decompose` c++ op.
2. Construct new_programs with parameter-shifted copies of decomposed
programs by `tfq_ps_symbol_replace` c++ op.
3. Weights and shifts are also obtained by `tfq_ps_weights_from_symbols`
3. Transpose the results to fed them into TensorFlow Quantum simulator.
Args:
programs: `tf.Tensor` of strings with shape [n_programs] containing
the string representations of the circuits to be executed.
symbol_names: `tf.Tensor` of strings with shape [n_symbols], which
is used to specify the order in which the values in
`symbol_values` should be placed inside of the circuits in
`programs`.
symbol_values: `tf.Tensor` of real numbers with shape
[n_programs, n_symbols] specifying parameter values to resolve
into the circuits specified by programs, following the ordering
dictated by `symbol_names`.
n_symbols: `tf.Tensor` of a positive integer representing the number of
symbols.
n_shifts: `tf.Tensor` of a positive integer representing the number of
parameter-shift terms. Defaults to 2.
Returns:
new_programs: the new programs whose program has only one gate with
impurity parameter-shift symbol name.
[n_symbols, n_programs, n_param_gates, n_shifts]
weights: parameter-shift coefficients of estimated observables.
[n_symbols, n_programs, n_param_gates, n_shifts]
shifts: parameter-shifted values (= matrix of symbol_values +/-shift)
[n_symbols, n_programs, n_param_gates, n_shifts]
n_param_gates: bypass of input n_param_gates to export it outside
"""
decomposed_programs = tfq_ps_util_ops.tfq_ps_decompose(programs)
delta_eig = 2.0
# Collecting doped programs with impurity sympy.Symbol from all programs
# with parameterized gates.
impurity = tf.tile(tf.convert_to_tensor([PARAMETER_IMPURITY_NAME]),
[n_symbols])
symbols = tf.convert_to_tensor(symbol_names)
# Doping impurity sympy.Symbol into programs per gate per symbol.
new_programs = tf.tile(
tf.expand_dims(tf.transpose(
tfq_ps_util_ops.tfq_ps_symbol_replace(decomposed_programs, symbols,
impurity), [1, 0, 2]),
axis=-1), [1, 1, 1, n_shifts])
n_param_gates = tf.cast(tf.gather(tf.shape(new_programs), 2),
dtype=tf.int32)
# This is a tensor of the `exponent_scalar`s of the shifted gates.
coeff = tf.expand_dims(tf.transpose(
tfq_ps_util_ops.tfq_ps_weights_from_symbols(decomposed_programs,
symbols), [1, 0, 2]),
axis=-1)
weights_plus = coeff * np.pi * 0.5 * 0.5 * delta_eig
weights = tf.concat([weights_plus, -weights_plus], axis=-1)
shifts_plus = tf.math.divide_no_nan(tf.math.divide(1.0, delta_eig), coeff)
val = tf.tile(
tf.expand_dims(tf.expand_dims(tf.transpose(symbol_values, [1, 0]),
axis=-1),
axis=-1), [1, 1, n_param_gates, n_shifts])
shifts = val + tf.concat([shifts_plus, -shifts_plus], axis=-1)
return new_programs, weights, shifts, n_param_gates