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simulador
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from sympy import I, Matrix, symbols
from sympy.physics.quantum import TensorProduct
from random import randint,random
from math import *
class simulacao:
def __init__ (self,n):
self.n = n
#global matriz
self.matriz = self.inicialização()
#self.arvore = self.cria_arvore()
self.cbits = []
def inicialização(self):
matriz = []
matriz.append([1])
for i in range(2**self.n-1):
matriz.append([0])
return Matrix(matriz)
def perm(self,permut):
permutacao = [None]*2**self.n
temp = []
for i in range(len(permut)):
permutacao[int(self.tratar_perm(i),base = 2)] = int(self.tratar_perm(permut[i]), base = 2)
for i in range(len(permut)):
k = [self.matriz[permutacao[i]]]
temp = temp + k
matriz = Matrix(temp)
#print(matriz)
self.matriz = matriz
#print(self.matriz)
def x(self,k,t=1):
for j in range(0,t):
cont = 0
trava = 0
permut = []
for i in range(2**self.n):
permut.append(i)
for i in range(int(2**(self.n))):
if cont == 2**(k+j):
if trava < 2**(k+j)-1:
trava += 1
else:
trava = 0
cont = 0
else:
permut[i],permut[i+2**(k+j)] = permut[i+2**(k+j)],permut[i]
cont += 1
self.perm(permut)
def h(self,k,t=1):
op = Matrix([1])
for i in range(self.n):
if i >= k and i <k+t:
had = Matrix([[1,1],[1,-1]])
had = had *2**(-0.5)
op = TensorProduct(op,had)
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def z(self,k,t=1):
op = Matrix([1])
for i in range(self.n):
if i >= k and i <k+t:
pz = Matrix([[1,0],[0,-1]])
op = TensorProduct(op,pz)
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def s(self,k,t=1):
op = Matrix([1])
for i in range(self.n):
if i >= k and i <k+t:
ps = Matrix([[1,0],[0,I]])
op = TensorProduct(op,ps)
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def y(self,k,t=1):
op = Matrix([1])
for i in range(self.n):
if i >= k and i <k+t:
py = Matrix([[0,-I],[I,0]])
op = TensorProduct(op,py)
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def t(self,k,t=1):
op = Matrix([1])
for i in range(self.n):
if i >= k and i <k+t:
pt = Matrix([[1,0],[0,e**(I*pi/4)]])
op = TensorProduct(op,pt)
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cx(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
cnot = Matrix([[1,0,0,0],[0,1,0,0],[0,0,0,1],[0,0,1,0]])
op = TensorProduct(op,cnot)
elif i == (k + 1):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cz(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
control_z = Matrix([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,-1]])
op = TensorProduct(op,control_z)
elif i == (k + 1):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cs(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
cphase = Matrix([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,I]])
op = TensorProduct(op,cphase)
elif i == (k + 1):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def swap(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
s = Matrix([[1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])
op = TensorProduct(op,s)
elif i == (k + 1):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cfs(self,k,t):
# Condicional Phase Shift
op = Matrix([1])
for i in range(self.n):
if i == k:
num = e**((pi*2*I/2**t))
s = Matrix([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,num]])
op = TensorProduct(op,s)
elif i == (k + 1):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def ccx(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
ccnot = Matrix([[1,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0],[0,0,1,0,0,0,0,0],[0,0,0,1,0,0,0,0],[0,0,0,0,1,0,0,0],[0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1],[0,0,0,0,0,0,1,0]])
op = TensorProduct(op,ccnot)
elif i == (k + 1) or i == (k+2):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cswap(self,k):
op = Matrix([1])
for i in range(self.n):
if i == k:
control_swap = Matrix([[1,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0],[0,0,1,0,0,0,0,0],[0,0,0,1,0,0,0,0],[0,0,0,0,1,0,0,0],[0,0,0,0,0,0,1,0],[0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1]])
op = TensorProduct(op,control_swap)
elif i == (k + 1) or i == (k+2):
i = i
else:
op = TensorProduct(op,Matrix([[1,0],[0,1]]))
self.matriz = op * self.matriz
def cria_soma(self):
temp = []
m = self.matriz
#print(m)
for x in m:
num = x.as_real_imag()
num_f = (num[0]**2 + num[1]**2)
temp.append(num_f)
N=2**(self.n)
#print(temp)
soma=[0]*N
soma[0]=temp[0]
for i in range(1,N):
soma[i]= soma[i-1]+temp[i]
#print(soma)
return soma
def medir(self,b):
self.cbits = b
def tratar(self,k):
temp = bin(2**self.n + k)[3:]
#temp = temp[::-1]
num = ''
for i in self.cbits:
num = num + temp[i]
return num
def tratar_perm(self,k):
temp = bin(2**self.n + k)[3:]
return temp[::-1]
def simular(self,t):
resultado = {}
arvore = self.cria_soma()
N = 2**self.n
size = 2**len(self.cbits)
r = [0]*size
for _ in range(t):
p = random()
#p = .1
# busca binária
inicio=0
fim = N-1
sai = False
while fim>=inicio and not sai:
k=(inicio+fim)//2
#print(k)
if p < arvore[k]:
if p> arvore[k-1]:
sai=True
fim = k-1
else :
inicio = k + 1
if self.tratar(k) in resultado:
resultado[self.tratar(k)] += 1
else:
resultado[self.tratar(k)] = 1
#print(self.tratar(k))
r[int(str(self.tratar(k)),base = 2)] +=1
print(resultado)
print(r)