def get_matrix(self):
size = 2**(len(self))
result = Matrix([[0] * size for x in range(size)])
for i in range(size):
for j in range(size):
result[i][j] = self.matrix[i][j]
return result
def get_subcircuit(self, name):
self.sort()
states = Matrix.Id(2**(len(self)))
for g_i in range(len(self.gates)):
gate = self.gates[g_i]
startqbits = [self.startqbit(g_i, i) for i in range(len(gate))]
permutation = [i for i in range(len(self))]
permuted = []
for i in range(len(startqbits)):
x = permutation[i]
y = startqbits[i]
if x != y and x not in permuted:
permutation[i] = y
permutation[y] = x
permuted.append(y)
P1 = Matrix.Permutation(permutation)
P2 = P1.getConjugate()
if len(self) == len(gate):
M = gate.get_matrix()
else:
M = tensor(
[gate.get_matrix(),
Matrix.Id(2**(len(self) - len(gate)))])
states = P2 * states
states = M * states
states = P1 * states
startqbits = self.startqbits()
P1 = Matrix.Permutation(startqbits)
P1.transpose()
states = P1 * states
return Gate(states, name)
from main.circuit import*
from main.matrix import Matrix, tensor
from math import pi, sqrt
#Grovers algorithm finds an x for which the function f: {0,1,...,2^n -1}-->{0,1} is 1.
#It depends on the function f. We define f with the variable x, so f(13)=1 and 0 otherwise
x=13
#2^n is the size of the domain of f. n+1 is then the size of the circuit (we need 1 extra qbit)
n=6
c=Circuit(n+1)
#define the matrix that computes f
F=Matrix.Zero(2**(n+1))
for i in range(2**(n+1)):
for j in range(2**(n+1)):
if i==j and i!=2*x and i!=(2*x)+1:
F[i][j]=1
elif i==2*x and j==(2*x)+1:
F[i][j]=1
elif i==(2*x)+1 and j==2*x:
F[i][j]=1
U=Matrix.Zero(2**(n))
for i in range(2**(n)):
if i==0:
U[i][i]=1
else:
U[i][i]=-1
U=tensor([U,Matrix.Id(2)])
def __init__(self, name="T"):
super().__init__(Matrix.T(), name, True)
def __init__(self, name="CNot"):
super().__init__(Matrix.Cnot(), name, True)
def __init__(self, size=1, name="QFT"):
super().__init__(Matrix.QFT(size), name, True)
def __init__(self, size=1, name="SqrtNot"):
super().__init__(Matrix.SqrtNot(size), name, True)
def __init__(self, size=1, name="H"):
super().__init__(Matrix.H(size), name, True)
def run_method2(self, in_v):
self.sort()
if len(in_v) != len(self):
raise RuntimeError(
"Input length does not match the size of the circuit.")
for i in in_v:
if i == 0 or i == 1:
pass
else:
raise RuntimeError("Invalid input - values must be 0 or 1.")
states = []
weights = []
for i in in_v:
if i == 0:
states.append(Matrix.vector([1, 0]))
elif i == 1:
states.append(Matrix.vector([0, 1]))
if len(states) > 1:
states = tensor(states)
else:
states = states[0]
for g_i in range(len(self.gates)):
gate = self.gates[g_i]
startqbits = [self.startqbit(g_i, i) for i in range(len(gate))]
permutation = [i for i in range(len(self))]
for i in range(len(startqbits)):
x = permutation[i]
y = startqbits[i]
if x != y:
j = permutation.index(y)
permutation[i] = y
permutation[j] = x
P1 = Matrix.Permutation(permutation)
if not P1.isUnitary():
print("permutation matrix seems to be wrong")
P2 = P1.getConjugate()
if len(self) == len(gate):
M = gate.get_matrix()
else:
M = tensor(
[gate.get_matrix(),
Matrix.Id(2**(len(self) - len(gate)))])
states = P2 * states
states = M * states
states = P1 * states
startqbits = self.startqbits()
P1 = Matrix.Permutation(startqbits)
P1.transpose()
states = P1 * states
for x in range(len(states)):
weights.append((abs(states[x][0]))**2)
return weights