def apply(box, *inputs, offset=None):
for node in inputs:
if not isinstance(node, Node):
raise TypeError(messages.type_err(Node, node))
if len(inputs) != len(box.dom):
raise AxiomError("Expected {} inputs, got {} instead.".format(
len(box.dom), len(inputs)))
depth = len(box_nodes)
box_node = Node("box", box=box, depth=depth, offset=offset)
box_nodes.append(box_node)
graph.add_node(box_node)
for i, obj in enumerate(box.dom):
if inputs[i].obj != obj:
raise AxiomError(
"Expected {} as input, got {} instead.".format(
obj, inputs[i].obj))
dom_node = Node("dom", obj=obj, i=i, depth=depth)
graph.add_edge(inputs[i], dom_node)
graph.add_edge(dom_node, box_node)
outputs = []
for i, obj in enumerate(box.cod):
cod_node = Node("cod", obj=obj, i=i, depth=depth)
graph.add_edge(box_node, cod_node)
outputs.append(cod_node)
return untuplify(*outputs)
def decorator(func):
from discopy import messages
from discopy.cat import AxiomError
from discopy.cartesian import tuplify, untuplify
graph, box_nodes = nx.DiGraph(), []
def apply(box, *inputs, offset=None):
for node in inputs:
if not isinstance(node, Node):
raise TypeError(messages.type_err(Node, node))
if len(inputs) != len(box.dom):
raise AxiomError("Expected {} inputs, got {} instead.".format(
len(box.dom), len(inputs)))
depth = len(box_nodes)
box_node = Node("box", box=box, depth=depth, offset=offset)
box_nodes.append(box_node)
graph.add_node(box_node)
for i, obj in enumerate(box.dom):
if inputs[i].obj != obj:
raise AxiomError(
"Expected {} as input, got {} instead.".format(
obj, inputs[i].obj))
dom_node = Node("dom", obj=obj, i=i, depth=depth)
graph.add_edge(inputs[i], dom_node)
graph.add_edge(dom_node, box_node)
outputs = []
for i, obj in enumerate(box.cod):
cod_node = Node("cod", obj=obj, i=i, depth=depth)
graph.add_edge(box_node, cod_node)
outputs.append(cod_node)
return untuplify(*outputs)
for box in boxes:
box._apply = apply
inputs = []
for i, obj in enumerate(dom):
node = Node("input", obj=obj, i=i)
inputs.append(node)
graph.add_node(node)
outputs = tuplify(func(*inputs))
for i, obj in enumerate(cod):
if outputs[i].obj != obj:
raise AxiomError(
"Expected {} as output, got {} instead.".format(
obj, outputs[i].obj))
node = Node("output", obj=obj, i=i)
graph.add_edge(outputs[i], node)
for box in boxes:
del box._apply
result = nx2diagram(graph, ob_factory=type(dom), id_factory=id_factory)
if result.cod != cod:
raise AxiomError(
"Expected diagram.cod == {}, got {} instead.".format(
cod, result.cod)) # pragma: no cover
return result
def __init__(self, left, right):
if not isinstance(left, Ty):
raise TypeError(messages.type_err(Ty, left))
if not isinstance(right, Ty):
raise TypeError(messages.type_err(Ty, right))
if len(left) != 1 or len(right) != 1:
raise ValueError(messages.cap_vs_caps(left, right))
if left != right.r and left.r != right:
raise AxiomError(messages.are_not_adjoints(left, right))
if left.r == right:
raise AxiomError(messages.wrong_adjunction(left, right, cup=False))
self.left, self.right, self.draw_as_wire = left, right, True
super().__init__("Cap({}, {})".format(left, right), Ty(), left @ right)
def __init__(self, name, dim=2, z=0):
super().__init__(name)
if z != 0:
raise AxiomError("circuit.Ob are self-dual.")
if not isinstance(dim, int) or dim < 2:
raise ValueError("Dimension should be an int greater than 1.")
self._dim = dim
def __add__(self, other):
if other == 0:
return self
if not isinstance(other, Tensor):
raise TypeError(messages.type_err(Tensor, other))
if (self.dom, self.cod) != (other.dom, other.cod):
raise AxiomError(messages.cannot_add(self, other))
return Tensor(self.dom, self.cod, self.array + other.array)
def cups(left, right):
if not isinstance(left, Dim):
raise TypeError(messages.type_err(Dim, left))
if not isinstance(right, Dim):
raise TypeError(messages.type_err(Dim, right))
if left.r != right:
raise AxiomError(messages.are_not_adjoints(left, right))
return Tensor(left @ right, Dim(1), Id(left).array)
def then(self, other):
if not isinstance(other, Tensor):
raise TypeError(messages.type_err(Tensor, other))
if self.cod != other.dom:
raise AxiomError(messages.does_not_compose(self, other))
array = np.tensordot(self.array, other.array, len(self.cod))\
if self.array.shape and other.array.shape\
else self.array * other.array
return Tensor(self.dom, other.cod, array)
def __init__(self, x, y):
if not isinstance(x, Ty):
raise TypeError(messages.type_err(Ty, x))
if not isinstance(y, Ty):
raise TypeError(messages.type_err(Ty, y))
if x != y.r and x.r != y:
raise AxiomError(messages.are_not_adjoints(x, y))
if len(x) != 1 or len(y) != 1:
raise ValueError(messages.cap_vs_caps(x, y))
if x.r == y:
raise NotImplementedError(messages.pivotal_not_implemented())
super().__init__('CAP', Ty(), x @ y)
def then(self, *others):
if len(others) != 1 or any(isinstance(other, Sum) for other in others):
return monoidal.Diagram.then(self, *others)
other, = others
if not isinstance(other, Tensor):
raise TypeError(messages.type_err(Tensor, other))
if self.cod != other.dom:
raise AxiomError(messages.does_not_compose(self, other))
array = Tensor.np.tensordot(self.array, other.array, len(self.cod))\
if self.array.shape and other.array.shape\
else self.array * other.array
return Tensor(self.dom, other.cod, array)
def __init__(self, left, right):
if not isinstance(left, Ty):
raise TypeError(messages.type_err(Ty, left))
if not isinstance(right, Ty):
raise TypeError(messages.type_err(Ty, right))
if len(left) != 1 or len(right) != 1:
raise ValueError(messages.cup_vs_cups(left, right))
if left.r != right and left != right.r:
raise AxiomError(messages.are_not_adjoints(left, right))
self.left, self.right = left, right
super().__init__("Cup({}, {})".format(left, right), left @ right, Ty())
self.draw_as_wires = True
def __init__(self, left, right):
if not isinstance(left, Ty):
raise TypeError(messages.type_err(Ty, left))
if not isinstance(right, Ty):
raise TypeError(messages.type_err(Ty, right))
if len(left) != 1 or len(right) != 1:
raise ValueError(messages.cap_vs_caps(left, right))
if left != right.r and left.r != right:
raise AxiomError(messages.are_not_adjoints(left, right))
monoidal.BinaryBoxConstructor.__init__(self, left, right)
Box.__init__(
self, "Cap({}, {})".format(left, right), Ty(), left @ right)
self.draw_as_wires = True
def cups(left, right, ar_factory=Diagram, cup_factory=Cup, reverse=False):
""" Constructs a diagram of nested cups. """
for typ in left, right:
if not isinstance(typ, Ty):
raise TypeError(messages.type_err(Ty, typ))
if left.r != right and right.r != left:
raise AxiomError(messages.are_not_adjoints(left, right))
result = ar_factory.id(left @ right)
for i in range(len(left)):
j = len(left) - i - 1
cup = cup_factory(left[j:j + 1], right[i:i + 1])
layer = ar_factory.id(left[:j]) @ cup @ ar_factory.id(right[i + 1:])
result = result << layer if reverse else result >> layer
return result
def then(self, other):
"""
Implements the sequential composition of Python functions.
>>> copy = Function(1, 2, lambda *x: x + x)
>>> swap = Function(2, 2, lambda x, y: (y, x))
>>> assert (copy >> swap)(1) == copy(1)
>>> assert (swap >> swap)(1, 2) == (1, 2)
"""
if not isinstance(other, Function):
raise TypeError(messages.type_err(Function, other))
if len(self.cod) != len(other.dom):
raise AxiomError(messages.does_not_compose(self, other))
return Function(self.dom, other.cod,
lambda *vals: other(*tuplify(self(*vals))))
def apply(state):
dom, cod = Dim(*(len(self.dom) * [2])), Dim(*(len(self.cod) * [2]))
if (state.dom, state.cod) != (Dim(1), dom):
raise AxiomError("Non-linear gates can only be applied "
"to states, not processes.")
return Tensor(Dim(1), cod, self.data(state.array))
def ba(left, right):
""" Backward application. """
if right.left != left:
raise AxiomError(messages.are_not_adjoints(left, right))
return BA(right)
def fa(left, right):
""" Forward application. """
if left.right != right:
raise AxiomError(messages.are_not_adjoints(left, right))
return FA(left)
def __add__(self, other):
if other == 0:
return self
if (self.dom, self.cod) != (other.dom, other.cod):
raise AxiomError(messages.cannot_add(self, other))
return CQMap(self.dom, self.cod, self.array + other.array)
def eval(self, *others, backend=None, mixed=False, **params):
"""
Evaluate a circuit on a backend, or simulate it with numpy.
Parameters
----------
others : Circuit
Other circuits to process in batch.
backend : pytket.Backend, optional
Backend on which to run the circuit, if none then we apply
:class:`discopy.tensor.Functor` or :class:`CQMapFunctor` instead.
mixed : bool, optional
Whether to apply :class:`discopy.tensor.Functor`
or :class:`CQMapFunctor`.
params : kwargs, optional
Get passed to Circuit.get_counts.
Returns
-------
tensor : :class:`discopy.tensor.Tensor`
If :code:`backend is not None` or :code:`mixed=False`.
cqmap : :class:`CQMap`
Otherwise.
Examples
--------
We can evaluate a pure circuit (i.e. with :code:`not circuit.is_mixed`)
as a unitary :class:`discopy.tensor.Tensor` or as a :class:`CQMap`:
>>> from discopy.quantum import *
>>> H.eval().round(2)
Tensor(dom=Dim(2), cod=Dim(2), array=[0.71, 0.71, 0.71, -0.71])
>>> H.eval(mixed=True).round(1) # doctest: +ELLIPSIS
CQMap(dom=Q(Dim(2)), cod=Q(Dim(2)), array=[0.5, ..., 0.5])
We can evaluate a mixed circuit as a :class:`CQMap`:
>>> assert Measure().eval()\\
... == CQMap(dom=Q(Dim(2)), cod=C(Dim(2)),
... array=[1, 0, 0, 0, 0, 0, 0, 1])
>>> circuit = Bits(1, 0) @ Ket(0) >> Discard(bit ** 2 @ qubit)
>>> assert circuit.eval() == CQMap(dom=CQ(), cod=CQ(), array=[1])
We can execute any circuit on a `pytket.Backend`:
>>> circuit = Ket(0, 0) >> sqrt(2) @ H @ X >> CX >> Measure() @ Bra(0)
>>> from unittest.mock import Mock
>>> backend = Mock()
>>> backend.get_counts.return_value = {(0, 1): 512, (1, 0): 512}
>>> assert circuit.eval(backend, n_shots=2**10).round()\\
... == Tensor(dom=Dim(1), cod=Dim(2), array=[0., 1.])
"""
from discopy import cqmap
from discopy.quantum.gates import Bits, scalar, ClassicalGate
if len(others) == 1 and not isinstance(others[0], Circuit):
# This allows the syntax :code:`circuit.eval(backend)`
return self.eval(backend=others[0], mixed=mixed, **params)
if backend is None:
if others:
return [
circuit.eval(mixed=mixed, **params)
for circuit in (self, ) + others
]
post_processes = Id(self.cod)
for left, box, right in self.layers:
if isinstance(box, ClassicalGate) and not box.is_linear:
if left.count(bit) or right.count(bit):
raise AxiomError("You can't tensor non-linear gates.")
post_processes = (post_processes or Id(box.dom)) >> box
elif post_processes and not isinstance(box, ClassicalGate):
raise AxiomError("You can't do anything quantum "
"after non-linear gates.")
circuit = self[:-len(post_processes) or len(self)]
functor = cqmap.Functor() if mixed or self.is_mixed\
else tensor.Functor(lambda x: 2, lambda f: f.array)
result = functor(circuit)
for process in post_processes.boxes:
result = process(result)
return result
circuits = [circuit.to_tk() for circuit in (self, ) + others]
results, counts = [], circuits[0].get_counts(*circuits[1:],
backend=backend,
**params)
for i, circuit in enumerate(circuits):
n_bits = len(circuit.post_processing.dom)
result = Tensor.zeros(Dim(1), Dim(*(n_bits * (2, ))))
for bitstring, count in counts[i].items():
result += (scalar(count) @ Bits(*bitstring)).eval()
for process in circuit.post_processing.boxes:
result = process(result)
results.append(result)
return results if len(results) > 1 else results[0]
