def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
opm = self.op.makemats(index_groups[1:])
newdict = {}
for indices in opm:
newindices = tuple(flatten([index_groups[0], indices]))
newdict[newindices] = CMat(opm[indices])
return newdict
def feed_indices(self, state: StateType,
index_groups: Sequence[Sequence[int]],
n: int) -> Tuple[int, object]:
# Get indices and make measurement
indices = numpy.array(flatten(index_groups), dtype=numpy.int32)
probs = state.measure_probabilities(indices, top_k=self.top_k)[:]
return 0, probs
def __call__(self, *inputs: Qubit,
**kwargs) -> Union[Qubit, Tuple[Qubit, ...]]:
# Extract consumed ops in reverse order
input_list = list(inputs)
ops_and_qubits = []
for opconstructor, consumed_indices in reversed(self.wrapper_funcs):
# Remove in reverse order to preserve index values.
consumed_qubits = reversed(
[input_list.pop(i) for i in reversed(consumed_indices)])
ops_and_qubits.append(
(opconstructor, consumed_qubits, consumed_indices))
ops_and_qubits = list(reversed(ops_and_qubits))
# Qubits left in input_list are destined to go to circuit func
# Use func to construct the circuit from args
with QubitWrapperContext(*flatten(ops_and_qubits)) as context:
outputs = self.op(*input_list, **kwargs)
if isinstance(outputs, Qubit):
outputs = (outputs, )
in_context_qubits = context.put_qubits_in_local_context_order(
*outputs)
if len(in_context_qubits) == 1:
return in_context_qubits[0]
else:
return in_context_qubits
def feed_indices(self, state: StateType,
index_groups: Sequence[Sequence[int]],
n: int) -> Tuple[int, Any]:
# Get indices and make measurement
indices = numpy.array(flatten(index_groups), dtype=numpy.int32)
bits, prob = state.measure(indices)
# We measure but don't remove any qubits.
return 0, (bits, prob)
def feed(self, qbitindex, node):
indices = flatten([qbitindex[qubit] for qubit in node.inputs])
if len(indices) > 0:
nodestr = repr(node)
paren_pos = nodestr.find('(')
if paren_pos > 0:
nodestr = nodestr[:paren_pos]
if nodestr not in PrintFeeder.BLACKLIST:
# print node at relevant positions
default_line = [self.qubit_line] * self.n
self.outputfn((" " * self.linespacing).join(default_line))
for index in indices:
default_line[index] = "-" * (2 * self.opwidth + 1)
self.outputfn((" " * self.linespacing).join(default_line))
for nodechr in nodestr:
default_line = [self.qubit_line] * self.n
for index in indices:
default_line[index] = "|" + (" " * (self.opwidth - 1)) +\
nodechr + (" " * (self.opwidth - 1)) + "|"
self.outputfn((" " * self.linespacing).join(default_line))
max_len = max(len(str(i)) for i in range(len(indices)))
index_strs = []
for i in range(len(indices)):
index_str = str(i)
difflen = max_len - len(index_str)
if difflen > 0:
index_str = " " * difflen + index_str
index_strs.append(index_str)
for l in range(max_len):
for i, index in enumerate(indices):
default_line[index] = "|" + (
" " * (self.opwidth - 1)) + index_strs[i][l] + (
" " * (self.opwidth - 1)) + "|"
self.outputfn((" " * self.linespacing).join(default_line))
for index in indices:
default_line[index] = "-" * (2 * self.opwidth + 1)
self.outputfn((" " * self.linespacing).join(default_line))
def run_graph(frontier: Sequence[PipelineObject],
graphnodes: AbstractSet[PipelineObject],
graphacc: GraphAccumulator):
"""
Apply the feed function from graphacc to each node in the graph in the order necessary to run the circuit.
:param frontier: top level nodes of graph
:param graphnodes: all nodes in graph
:param graphacc: graph accumulator class with "feed(qbitindex, node) -> void" function.
"""
# Condition is that if a node is in the frontier, either all its inputs are
# in the feed dict, or it itself is.
qbitindex = {}
seen = set()
n = 0
for qbit in sorted(frontier, key=lambda q: q.qid):
qbitindex[qbit] = [i for i in range(n, n + qbit.n)]
n += qbit.n
while len(frontier) > 0:
frontier = list(sorted(frontier, key=lambda q: q.qid))
node, frontier = frontier[0], frontier[1:]
graphacc.feed(qbitindex, node)
# Manage measurements
qbitindex = node.remap_index(qbitindex, n)
seen.add(node)
# Iterate sink for special cases where "cloning" takes place (i.e. splitting qubits after operation)
for nextnode in node.sink:
if nextnode in graphnodes and nextnode not in seen and nextnode not in frontier:
all_deps = True
for prevnode in nextnode.inputs:
if prevnode not in seen:
all_deps = False
break
if all_deps:
frontier.append(nextnode)
qbitindex[nextnode] = nextnode.select_index(
flatten(qbitindex[j] for j in nextnode.inputs))
def QFFT(*inputs: Qubit, rev: bool = True) -> Union[Qubit, Tuple[Qubit, ...]]:
qarr = flatten([inp.split(range(inp.n)) for inp in inputs])
recQFFT(qarr)
if rev:
half = int(len(qarr) / 2)
for i in range(half):
qarr[i], qarr[-1 - i] = Swap(qarr[i], qarr[-1 - i])
if len(inputs) < len(qarr):
outputqarr = []
index = 0
for qubit in inputs:
outputqarr.append(Qubit(*(qarr[index:index + qubit.n])))
index += qubit.n
else:
outputqarr = qarr
if len(outputqarr) > 1:
return tuple(outputqarr)
else:
return outputqarr[0]
def get_context() -> Sequence[Tuple[OpConstructor, Sequence[Qubit],
Optional[Sequence[int]]]]:
"""Get full set of contexts."""
return flatten(QubitWrapperContext.CONTEXT_STACK)
def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
return {i: (1/numpy.sqrt(2))*numpy.array([[1, 1], [1, -1]])
for i in flatten(index_groups)}
def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
return {i: numpy.array([[1.0, 0.0],[0.0j, -1.0]])
for i in flatten(index_groups)}
def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
return {i: numpy.flip(numpy.eye(2), 0)
for i in flatten(index_groups)}
def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
swapn = self.inputs[0].n
a_indices = index_groups[0]
b_indices = index_groups[1]
return {tuple(flatten([a_indices, b_indices])): SwapMat(swapn)}
def makemats(self, index_groups: Sequence[Sequence[int]]) -> Mapping[IndexType, MatrixType]:
return {i: numpy.array([[1, 0], [0, self.exponented]])
for i in flatten(index_groups)}
def run(*args: PipelineObject,
feed: Mapping[Union[PipelineObject, Tuple[PipelineObject, ...]],
InitialState] = None,
strict: bool = False,
backend_constructor: Callable[
[int, Sequence[Tuple[int, ...]], Sequence[Sequence[complex]]],
StateType] = None,
statetype: Type = numpy.complex128,
**kwargs):
"""
Runs pipeline using all qubits in *args. Produces an output state based on input.
:param args: list of qubits to evaluate
:param feed: feed of individual qbit states - qbit/qbits : state
:param statetype: type of state data (should be complex numpy value).
:param strict: requires all qubits to have states in feed, none implicitly defined as |0>
:param backend_constructor
:return: state handle, output dictionary
"""
if feed is None:
feed = {}
else:
# Copy to dictionary, convert non-tuples to tuples
feed = {(k if type(k) == tuple else (k, )): feed[k] for k in feed}
# Flatten keys for the ones which are qubits
all_qubits_in_feed = list(flatten(feed.keys()))
if backend_constructor is None:
backend_constructor = CythonBackend.make_state
# Frontier contains all qubits required for execution
# Assume 0 unless in feed
frontier, graphnodes = get_deps(*args, feed=feed)
for qubit in frontier:
if qubit not in all_qubits_in_feed and qubit.default is not None:
if type(qubit.default) == int:
if 0 < qubit.default < 2**qubit.n:
feed[(qubit, )] = numpy.zeros((2**qubit.n, ))
feed[(qubit, )][qubit.default] = 1.0
# if it's 0 then not defining it is faster
else:
feed[(qubit, )] = qubit.default
frontier = list(sorted(frontier, key=lambda q: q.qid))
if strict:
missing_qubits = [
qubit for qubit in frontier if qubit not in all_qubits_in_feed
]
if len(missing_qubits):
raise ValueError(
"Missing Qubit states for: {}".format(missing_qubits))
qbitindex = {}
n = 0
for qbit in frontier:
qbitindex[qbit] = [i for i in range(n, n + qbit.n)]
n += qbit.n
feed_index_groups = [sum((qbitindex[q] for q in qs), []) for qs in feed]
feed_states = [feed[qs] for qs in feed]
# In case they used the deprecated state argument
# Convert state into a single index group for all indices.
if 'state' in kwargs and kwargs['state']:
feed_index_groups = [tuple(range(n))]
feed_states = [kwargs['state']]
state = backend_constructor(n,
feed_index_groups,
feed_states,
statetype=statetype)
return feed_forward(frontier, state, graphnodes)
