def test_serialize_deserialize_paulisum_consistency(self, sum_proto_pair):
"""Serialize and deserialize and ensure nothing changed."""
self.assertEqual(
serializer.serialize_paulisum(
serializer.deserialize_paulisum(sum_proto_pair[1])),
sum_proto_pair[1])
self.assertEqual(
serializer.deserialize_paulisum(
serializer.serialize_paulisum(sum_proto_pair[0])),
sum_proto_pair[0])
def _sample_expectation_worker_func(indices, programs, params, ops, n_samples):
x_np = _convert_simple_view_to_np(INFO_DICT['arr'], np.float32,
INFO_DICT['shape'])
simulator = INFO_DICT['sim']
# TODO: remove this when picklable.
for i in range(len(ops)):
for j in range(len(ops[i])):
ops[i][j] = serializer.deserialize_paulisum(ops[i][j])
for i, index_tuple in enumerate(indices):
batch_index = index_tuple[0]
op_index = index_tuple[1]
# (#679) Just ignore empty programs.
if len(programs[batch_index].all_qubits()) == 0:
continue
circuit = cirq.resolve_parameters(programs[batch_index],
params[batch_index])
sampler = TFQPauliSumCollector(
circuit,
ops[batch_index][op_index],
samples_per_term=n_samples[batch_index][op_index])
asyncio.set_event_loop(asyncio.new_event_loop())
sampler.collect(simulator, concurrency=1)
result = sampler.estimated_energy().real
_pointwise_update_simple_np(x_np, batch_index, op_index, result)
def _analytical_expectation_worker_func(indices, programs, params, ops):
"""Compute the expectation of the op[batch_index], w.r.t
circuit[batch_index] where batch_index is calculated from indices."""
x_np = _convert_simple_view_to_np(INFO_DICT['arr'], np.float32,
INFO_DICT['shape'])
simulator = INFO_DICT['sim']
# TODO: remove this when picklable.
for i in range(len(ops)):
for j in range(len(ops[i])):
ops[i][j] = serializer.deserialize_paulisum(ops[i][j])
old_batch_index = -2
state = -1
for i, index_tuple in enumerate(indices):
batch_index = index_tuple[0]
op_index = index_tuple[1]
# (#679) Just ignore empty programs.
if len(programs[batch_index].all_qubits()) == 0:
continue
if old_batch_index != batch_index:
# must compute a new state vector.
qubit_oder = dict(
zip(sorted(programs[batch_index].all_qubits()),
list(range(len(programs[batch_index].all_qubits())))))
state = simulator.simulate(programs[batch_index],
params[batch_index])
result = INFO_DICT['post_process'](ops[batch_index][op_index], state,
qubit_oder)
_pointwise_update_simple_np(x_np, batch_index, op_index, result)
old_batch_index = batch_index
def test_serialize_deserialize_identity(self):
"""Confirm that identity gates can be serialized and deserialized."""
q0 = cirq.GridQubit(0, 0)
q1 = cirq.GridQubit(0, 1)
paulisum_with_identity = cirq.PauliSum.from_pauli_strings([
cirq.PauliString(cirq.I(q0)),
cirq.PauliString(cirq.Z(q0), cirq.Z(q1)),
])
self.assertEqual(
paulisum_with_identity,
serializer.deserialize_paulisum(
serializer.serialize_paulisum(paulisum_with_identity)))
def _parse_single(item):
try:
if b'tfq_gate_set' in item:
# Return a circuit parsing
obj = cirq.google.api.v2.program_pb2.Program()
obj.ParseFromString(item)
out = serializer.deserialize_circuit(obj)
return out
# Return a PauliSum parsing.
obj = pauli_sum_pb2.PauliSum()
obj.ParseFromString(item)
out = serializer.deserialize_paulisum(obj)
return out
except Exception:
raise TypeError('Error decoding item: ' + str(item))
def test_deserialize_paulisum_simple(self, sum_proto_pair):
"""Ensure deserialization is correct."""
self.assertEqual(serializer.deserialize_paulisum(sum_proto_pair[1]),
sum_proto_pair[0])
def test_deserialize_paulisum_wrong_type(self, inp):
"""Attempt to deserialize invalid object types."""
with self.assertRaises(TypeError):
serializer.deserialize_paulisum(inp)
def cirq_sampled_expectation(programs, symbol_names, symbol_values,
pauli_sums, num_samples):
"""Calculate the sampled expectation value of circuits wrt some
operator(s).
Estimates the expectation value for all the `cirq.PauliSum`s in
`pauli_sums` on each `cirq.Circuit` in `programs`. Each circuit will
have the values in `symbol_values` resolved into the symbols in the
circuit (with the ordering defined by `symbol_names`).
```python
symbol_names = ['a', 'b', 'c']
programs = tfq.convert_to_tensor(
[cirq.Circuit(H(q0) ** sympy.Symbol('a'),
X(q1) ** sympy.Symbol('b'),
Y(q2) ** sympy.Symbol('c'))]
)
symbol_values = [[3,2,1]]
pauli_sums = tfq.convert_to_tensor(
[1.5 * cirq.Z(q0) * cirq.Z(q1)]
)
n_samples = [[100]]
cirq_sampled_expectation(
programs, symbol_names, sybmol_values, pauli_sums, n_samples)
```
Would place the values of 3 into the Symbol labeled 'a', 2 into the
symbol labeled 'b' and 1 into the symbol labeled 'c'. Then it would
estimate the ZZ expectation on this circuit by draw samples from the
circuit 100 times.
Args:
programs: `tf.Tensor` of strings with shape [batch_size] containing
the string representations of the circuits to be executed.
symbol_names: `tf.Tensor` of strings with shape [n_params], 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
[batch_size, n_params] specifying parameter values to resolve
into the circuits specified by programs, following the ordering
dictated by `symbol_names`.
pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
containing the string representation of the operators that will
be used on all of the circuits in the expectation calculations.
num_samples: `tf.Tensor` with `n_samples[i][j]` is equal to the
number of samples to draw in each term of `pauli_sums[i][j]`
when estimating the expectation.
Returns:
`tf.Tensor` with shape [batch_size, n_ops] that holds the
expectation value for each circuit with each op applied to it
(after resolving the corresponding parameters in).
"""
_input_check_helper(programs, symbol_names, symbol_values)
if not (pauli_sums.dtype == tf.dtypes.string):
raise TypeError('pauli_sums tensor must be of type string.')
if not (pauli_sums.shape[0] == programs.shape[0]) or \
len(pauli_sums.shape) != 2:
raise TypeError('pauli_sums tensor must have the same batch shape '
'as programs tensor.')
if not (num_samples.dtype == tf.dtypes.int32 or
num_samples.dtype == tf.dtypes.int64):
raise TypeError('num_samples tensor must be of type int32 of '
'int64.')
if not (num_samples.shape == pauli_sums.shape):
raise TypeError('num_samples tensor must have the same shape '
'as pauli_sums tensor. got: {} expected: {}'.format(
num_samples.shape, pauli_sums.shape))
if tf.less_equal(num_samples, 0).numpy().any():
raise TypeError('num_samples contains sample value <= 0.')
programs, resolvers = _batch_deserialize_helper(programs, symbol_names,
symbol_values)
num_samples = num_samples.numpy().tolist()
sum_inputs = []
for sub_list in pauli_sums.numpy():
to_append = []
for x in sub_list:
obj = pauli_sum_pb2.PauliSum()
obj.ParseFromString(x)
to_append.append(serializer.deserialize_paulisum(obj))
sum_inputs.append(to_append)
expectations = batch_util.batch_calculate_sampled_expectation(
programs, resolvers, sum_inputs, num_samples, sampler)
return expectations
def cirq_analytical_expectation(programs, symbol_names, symbol_values,
pauli_sums):
"""Calculate the expectation value of circuits wrt some operator(s).
Calculate the expectation value for all the `cirq.PauliSum`s in
`pauli_sums` on each `cirq.Circuit` in `programs`. Each circuit will
have the values in `symbol_values` resolved into the symbols in the
circuit (with the ordering defined by `symbol_names`).
```python
symbol_names = ['a', 'b', 'c']
programs = tfq.convert_to_tensor(
[cirq.Circuit(H(q0) ** sympy.Symbol('a'),
X(q1) ** sympy.Symbol('b'),
Y(q2) ** sympy.Symbol('c'))]
)
symbol_values = [[3,2,1]]
pauli_sums = tfq.convert_to_tensor(
[1.5 * cirq.Z(q0) * cirq.Z(q1)]
)
cirq_analytical_expectation(
programs, symbol_names, sybmol_values, pauli_sums)
```
Would place the values of 3 into the Symbol labeled 'a', 2 into the
symbol labeled 'b' and 1 into the symbol labeled 'c'. Then it would
calculate the ZZ expectation on this circuit.
Args:
programs: `tf.Tensor` of strings with shape [batch_size] containing
the string representations of the circuits to be executed.
symbol_names: `tf.Tensor` of strings with shape [n_params], 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
[batch_size, n_params] specifying parameter values to resolve
into the circuits specified by programs, following the ordering
dictated by `symbol_names`.
pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
containing the string representation of the operators that will
be used on all of the circuits in the expectation calculations.
Returns:
`tf.Tensor` with shape [batch_size, n_ops] that holds the
expectation value for each circuit with each op applied to it
(after resolving the corresponding parameters in).
"""
_input_check_helper(programs, symbol_names, symbol_values)
if not (pauli_sums.dtype == tf.dtypes.string):
raise TypeError('pauli_sums tensor must be of type string.')
if not (pauli_sums.shape[0] == programs.shape[0]):
raise TypeError('pauli_sums tensor must have the same batch shape '
'as programs tensor.')
programs, resolvers = _batch_deserialize_helper(programs, symbol_names,
symbol_values)
sum_inputs = []
for sub_list in pauli_sums.numpy():
to_append = []
for x in sub_list:
obj = pauli_sum_pb2.PauliSum()
obj.ParseFromString(x)
to_append.append(serializer.deserialize_paulisum(obj))
sum_inputs.append(to_append)
expectations = batch_util.batch_calculate_expectation(
programs, resolvers, sum_inputs, simulator)
return expectations
