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import qiskit
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
import itertools
def makeCircuit(N):
q = QuantumRegister(2)
c = ClassicalRegister(2)
qc = QuantumCircuit(q, c)
return q, c, qc
def superDenseCoding(b1, b2, backend, shots=1024, basis_gates=None, noise_model=None, draw_diagram=False):
q, c, qc = makeCircuit(2)
# prepare share entangled state
qc.h(q[0])[0], q[1])
# superdense coding operation depend on sended binary bits
# this part of the circuit is classically controlled
if b1:
if b2:
# suppose q0 register is sent to receiver
# decode the transfered information by the receiver[0], q[1])
# measurement
qc.measure(q, c)
# build diagram for visualisation
diagram = None
if draw_diagram:
diagram = qc.draw(output="mpl")
# perform simulation and extract counts
job = qiskit.execute(
qc, backend,
result = job.result()
counts = result.get_counts()
comb = ["".join(seq) for seq in itertools.product('01', repeat=2)]
for key in comb:
if key not in counts.keys():
counts[key] = 0
# return everything
return qc, diagram, counts
def simulateCommunicationChannel(b1, b2, backend, basis_gates=None, noise_model=None):
_, _, counts = superDenseCoding(b1, b2, backend, shots=1, basis_gates=basis_gates, noise_model=noise_model)
received_b1 = None
received_b2 = None
combinations = list(itertools.product([0, 1], repeat=2))
for cb1, cb2 in combinations:
index = str(cb1) + str(cb2)
if counts[index] == 1:
received_b1 = cb1
received_b2 = cb2
return received_b1, received_b2
import numpy as np
from PIL import Image
from qiskit import Aer
import qiskit.providers.aer.noise as noise
from qm import simulateCommunicationChannel
# Error probabilities
# play with those values and look how the file received_mario_sprite.bmp changes compared to mario_sprite.bmp
prob_1 = 0.15 # 1-qubit gate
prob_2 = prob_1 # 2-qubit gate
# Depolarizing quantum errors
error_1 = noise.depolarizing_error(prob_1, 1)
error_2 = noise.depolarizing_error(prob_2, 2)
# Add errors to noise model
noise_model = noise.NoiseModel()
noise_model.add_all_qubit_quantum_error(error_1, ['u1', 'u2', 'u3'])
noise_model.add_all_qubit_quantum_error(error_2, ['cx'])
# Get basis gates from noise model
basis_gates = noise_model.basis_gates
backend = Aer.get_backend('qasm_simulator')
# if your sprite is png and not bmp, run the following
# convert mario_sprite.png -depth 2 mario_sprite.bmp
# source:
im ='mario_sprite.bmp')
pixels = np.array(im)
columns, rows, colors = pixels.shape
dtype = pixels.dtype
hashes = []
palette = {}
indices = {}
# get the palette and hashed colors
for row in range(rows):
for column in range(columns):
color = pixels[column, row, :]
hashed = hash(tuple(color))
palette[hashed] = color
hashes = list(set(hashes))
# assign a unique index to each color
for i, hashed in enumerate(hashes):
indices[hashed] = i
# get binary tuple from the integer
def binaryTupleFromInteger(i):
return tuple([int(j) for j in list(bin(i)[2:].zfill(2))])
print('Test converting integer to a binary tuple')
# get integer from binary tuple
def integerFromBinaryTuple(a, b):
return a*2**1 + b*2**0
print('Test converting binary tuple to integer')
print(integerFromBinaryTuple(0, 0))
print(integerFromBinaryTuple(0, 1))
print(integerFromBinaryTuple(1, 0))
print(integerFromBinaryTuple(1, 1))
# time to send the Mario
received_mario = np.zeros((columns, rows, colors), dtype=dtype)
for row in range(rows):
for column in range(columns):
color = pixels[column, row, :]
hashed = hash(tuple(color))
index = indices[hashed]
b1, b2 = binaryTupleFromInteger(index)
# here quantum magic happens TODO
cb1, cb2 = simulateCommunicationChannel(b1, b2, backend, basis_gates, noise_model=noise_model)
# quantum magick is done
received_index = integerFromBinaryTuple(cb1, cb2)
received_hashed = hashes[received_index]
received_color = palette[received_hashed]
received_mario[column, row, :] = received_color
print('Is received Mario identical to Mario that has been sent?')
print((pixels == received_mario).all())
# make image of the received Mario
received_im = Image.fromarray(received_mario)'received_mario_sprite.bmp')
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