def synthesize(self, utry: UnitaryMatrix, data: dict[str, Any]) -> Circuit:
"""Synthesize `utry` into a circuit, see SynthesisPass for more info."""
# 0. Skip any unitaries too small for the configured block.
if self.block_size_start > utry.get_size():
_logger.warning(
'Skipping synthesis: block size is larger than input unitary.',
)
return Circuit.from_unitary(utry)
# 1. Create empty circuit with same size and radixes as `utry`.
circuit = Circuit(utry.get_size(), utry.get_radixes())
# 2. Calculate block sizes
block_size_end = utry.get_size() - 1
if self.block_size_limit is not None:
block_size_end = self.block_size_limit
# 3. Calculate relevant coupling_graphs
# TODO: Look for topology info in `data`, use all-to-all otherwise.
model = MachineModel(utry.get_size())
locations = [
model.get_locations(i)
for i in range(self.block_size_start, block_size_end + 1)
]
# 3. Bottom-up synthesis: build circuit up one gate at a time
layer = 1
last_cost = 1.0
last_loc = None
while True:
remainder = utry.get_dagger() @ circuit.get_unitary()
sorted_locations = self.analyze_remainder(remainder, locations)
for loc in sorted_locations:
# Never predict the previous location
if loc == last_loc:
continue
_logger.info(f'Trying next predicted location {loc}.')
circuit.append_gate(VariableUnitaryGate(len(loc)), loc)
circuit.instantiate(
utry,
**self.instantiate_options, # type: ignore
)
cost = self.cost.calc_cost(circuit, utry)
_logger.info(f'Instantiated; layers: {layer}, cost: {cost:e}.')
if cost < self.success_threshold:
_logger.info(f'Circuit found with cost: {cost:e}.')
_logger.info('Successful synthesis.')
return circuit
progress_threshold = self.progress_threshold_a
progress_threshold += self.progress_threshold_r * np.abs(cost)
if last_cost - cost >= progress_threshold:
_logger.info('Progress has been made, depth increasing.')
last_loc = loc
last_cost = cost
layer += 1
break
_logger.info('Progress has not been made.')
circuit.pop((-1, loc[0]))
def apply_right(
self, utry: UnitaryMatrix,
location: Sequence[int], inverse: bool = False,
) -> None:
"""
Apply the specified unitary on the right of this UnitaryBuilder.
.-----. .------.
0 -| |---| |-
1 -| |---| utry |-
. . '------'
. .
. .
n-1 -| |------------
'-----'
Args:
utry (UnitaryMatrix): The unitary to apply.
location (Sequence[int]): The qudits to apply the unitary on.
inverse (bool): If true, apply the inverse of the unitary.
Notes:
Applying the unitary on the right is equivalent to multiplying
the unitary on the left of the tensor. This operation is
performed using tensor contraction.
"""
if not isinstance(utry, UnitaryMatrix):
raise TypeError('Expected UnitaryMatrix, got %s', type(utry))
if not CircuitLocation.is_location(location, self.get_size()):
raise TypeError('Invalid location.')
if len(location) != utry.get_size():
raise ValueError('Unitary and location size mismatch.')
for utry_radix, bldr_radix_idx in zip(utry.get_radixes(), location):
if utry_radix != self.get_radixes()[bldr_radix_idx]:
raise ValueError('Unitary and location radix mismatch.')
left_perm = list(location)
mid_perm = [x for x in range(self.get_size()) if x not in location]
right_perm = [x + self.get_size() for x in range(self.get_size())]
left_dim = int(np.prod([self.get_radixes()[x] for x in left_perm]))
utry = utry.get_dagger() if inverse else utry
utry_np = utry.get_numpy()
perm = left_perm + mid_perm + right_perm
self.tensor = self.tensor.transpose(perm)
self.tensor = self.tensor.reshape((left_dim, -1))
self.tensor = utry_np @ self.tensor
shape = list(self.get_radixes()) * 2
shape = [shape[p] for p in perm]
self.tensor = self.tensor.reshape(shape)
inv_perm = list(np.argsort(perm))
self.tensor = self.tensor.transpose(inv_perm)