def measure(self):
"""
Measures a circuit on the computational basis.
Returns
-------
array : np.ndarray
with real entries and the same shape as :code:`self.eval().array`.
Examples
--------
>>> m = X.measure()
>>> list(np.round(m.flatten()))
[0.0, 1.0, 1.0, 0.0]
>>> assert (Ket(0) >> X >> Bra(1)).measure() == m[0, 1]
"""
def bitstring(i, length):
return map(int, '{{:0{}b}}'.format(length).format(i))
process = self.eval()
states, effects = [], []
states = [
Ket(*bitstring(i, len(self.dom))).eval()
for i in range(2**len(self.dom))
]
effects = [
Bra(*bitstring(j, len(self.cod))).eval()
for j in range(2**len(self.cod))
]
array = np.zeros(len(self.dom + self.cod) * (2, ))
for state in states if self.dom else [Tensor.id(1)]:
for effect in effects if self.cod else [Tensor.id(1)]:
scalar = np.absolute((state >> process >> effect).array)**2
array += scalar * (state.dagger() >> effect.dagger()).array
return array
def __init__(self, *bitstring, _dagger=False):
utensor = Tensor.id(
Dim(1)).tensor(*(Tensor(Dim(1), Dim(2), [0, 1] if bit else [1, 0])
for bit in bitstring))
name = "Bits({})".format(', '.join(map(str, bitstring)))
dom, cod = (len(bitstring), 0) if _dagger else (0, len(bitstring))
super().__init__(name, dom, cod, array=utensor.array, _dagger=_dagger)
self.bitstring = bitstring
def discard(dom):
""" Discard a quantum dimension or take the marginal distribution. """
array = Tensor.np.tensordot(Tensor.np.ones(dom.classical),
Tensor.id(dom.quantum).array, 0)
return CQMap(dom, CQ(), array)
def id(dom=CQ()):
utensor = Tensor.id(dom.classical @ dom.quantum @ dom.quantum)
return CQMap(dom, dom, utensor=utensor)
def id(dom):
data = Tensor.id(dom.classical @ dom.quantum @ dom.quantum)
return CQMap(dom, dom, data.array)