def get_bisection(state, prob, dim):
# to calculate bisection
if typex(state) == 'oper':
rs = np.zeros((5, dim, dim))
for i in range(5):
for j in range(dim):
for k in range(dim):
rs[i, j, k] = randunit() / prob[i, j, k]
else:
rs = np.zeros((5, dim))
for i in range(5):
for j in range(dim):
rs[i, j] = randunit() / prob[i, j]
return rs
def random(d):
""" to generate a random state
Parameters:
----------
d: dimension
Return: random state
"""
rpsi = np.zeros((d, 1))
ipsi = np.zeros((d, 1))
for i in range(d):
rpsi[i, 0] = randunit()
ipsi[i, 0] = randunit()
ppsi = rpsi + 1j * ipsi
ppsi = normx(ppsi)
M = haar(d)
prime = dotx((eyex(d) + M), ppsi)
prime = normx(prime)
return qx(prime)
def get_qmeas_simul(self):
res = []
for i in range(len(self.qmeas)):
if self.backend == 'mc':
res.append(mc(np.real(self.qmeas[i]), self.niter))
elif self.backend == 'cdf':
temp = []
for j in range(self.niter):
temp.append(randunit() / np.real(self.qmeas[i]))
res.append(cdf(temp))
else:
raise TypeError('no ', backend)
return res