q_sim.py

#Quantum Simulator
#We want to generate two files:
#Alice
#	Alice intensity choice, Alice Basis Choice, Alice Element Choice
# Bob
#	Bob Basis Choice, Bob Measurement Result
# Where Alice's choices are random, and Bob's Measurement Result is as follows, based on alice's choice:
# 	With Pr(Vacuum)
#		vacum                         hjjhbbbbbbbbbbbbbbbbbbbn                                                                                                                                                                                                                                                             
#	With Pr(1-EQ(11 paper he sent me)*(1/2*Pr(Double) + 1-(pr(double))*Overall Gain) (eq10)

#check to see that we get ~150 clicks from vacuums, 10^5 clicks from decoy and 10^6 clicks from signal ,m                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  
import numpy as np
import math
from config import *
VAC = 3
DUB = 2
def Alice_Choices(int_pdf, basis_pdf):
	intensity = np.random.choice(int_pdf[0],1, p=int_pdf[1])[0]
	basis = np.random.choice(basis_pdf[0],1, p=basis_pdf[1])[0]
	element = np.random.choice([0,1],1, [.5,.5])[0]
	return [intensity, basis, element]

def Bob_Results(alice_choices, basis_pdf, Bob_params):
	alice_int = alice_choices[0]
	alice_basis = alice_choices[1]
	alice_element = alice_choices[2]
	Y0 = Bob_params[0]
	Nu = Bob_params[1]
	e0 = Bob_params[2]
	ed = Bob_params[3]
	Double = Bob_params[4]
	noise = Bob_params[5]
	bob_basis = np.random.choice(basis_pdf[0], 1, p=basis_pdf[1])[0]

	gain = Y0+1-math.exp(-1*alice_int*Nu) #prob of a detection = Y0+1 - e^-(nu*mu)
	detection = np.random.choice([True, False], 1, p=[gain, 1-gain])[0]
	#if it is a detection and there is not a double click, add a noise paramater
	if detection:
		double_occ =  np.random.choice([True, False], 1, p=[Double, 1-Double])[0]
		if double_occ:
			result = DUB
		else:
			if bob_basis == alice_basis:
				QBER = e0*Y0+ed*(1-math.exp(-1*alice_int*Nu)) if not noise else noise
				result = np.random.choice([alice_element, (alice_element + 1)%2], 1,p=[1-QBER, QBER])[0]
			else:
				result = np.random.choice([alice_element, (alice_element + 1)%2],1, p=[.5, .5])[0]
	else:
		result = VAC
	return [bob_basis, result]

def main_loop():
	#Alice Params 
	#	mu1 vacc, mu2 decoy, mu3 signal intensities
	#	pi is probability of mui being chosen


	# pza = .5
	# bases = ([0,1], [pza, 1-pza])

	#Alice choice 
	alice_choices = Alice_Choices(intensities, bases)
	#Bob Params
	# #	
	# alpha = .15
	# length = 1
	# tAB = 10 ** -((alpha*length)/10)
	# eta_det = .1
	# t_bob = .1
	# eta_bob = t_bob*eta_det
	# noise = .05

	# Y0 = 1e-5
	# eta = tAB*eta_bob #could precompute nui (and this is eq 5 if we want to further paramaterize)
	# e0 = .5
	# edetect = 1e-3
	# pdouble = 1e-3
	# bob_params = [Y0, eta, e0, edetect, pdouble, noise]

	# pzb = .5
	# bob_bases = [[0,1],[pzb, 1-pzb]]

	bob_results  = Bob_Results(alice_choices, bob_bases, bob_params)
	#print("Alice: " + str(alice_choices))
	#print("Bob:   " + str([ ' ' ]+bob_results))
	return alice_choices, bob_results
# mu1, mu2, mu3 = .0007, .1, .4
# p3 = .6
# p2 = .2
# p1 = 1-p2-p3
# intensities = ([mu1,mu2,mu3], [p1,p2,p3])
# N = int(1e9)

with open("bob_data.csv", "w+") as bob:
	with open("alice_data.csv", "w+") as alice:
		with open("combined_data.csv", "w+") as comb:
			alice.write("B, Y, Mu\n")
			bob.write("B, Y\n")
			comb.write("Ba, Bb, Ya, Yb, Mu\n")
			i = 0
			vac = [0,0,0]
			error = [0,0,0] 
			int_count = [0,0,0]
			corr = [0,0,0]
			dub = [0,0,0]
			click = [0,0,0]

			while(i<=N):
				a,b = main_loop()
				alice.write("{}, {}, {}\n".format(a[1], a[2], a[0]))
				bob.write("{}, {}\n".format(b[0],b[1]))
				comb.write("{}, {}, {}, {}, {}\n".format(a[1], b[0], a[2], b[1], a[0]))

				#Stats:
				#Vac on each
				#Error on each
				i+=1
				int_count[intensities[0].index(a[0])]+=1
				if b[1] == VAC:
					vac[intensities[0].index(a[0])]+=1
				else:
					click[intensities[0].index(a[0])]+=1
				if b[1] == DUB:
					dub[intensities[0].index(a[0])]+=1
				if b[0] == a[1] and b[1]!=VAC and b[1]!=a[2]:
					error[intensities[0].index(a[0])]+=1
				if b[0] == a[1] and b[1]==a[2]:
					corr[intensities[0].index(a[0])]+=1

				if i % 5000 == 0:
					print("{} rounds completed".format(i))
					print("Intensities:  " + str(intensities[0]))
					print("Int Counts:   " + str(int_count))
					print("Vac Counts:   " + str([float(v) for v in vac]))
					print("Click Counts: " + str([float(v) for v in click]))
					print("Dubl Counts:   " + str([float(d) for d in dub]))
					print("Corr Counts:  " + str([float(c) for c in corr]))
					print("Error Counts: " + str([float(e) for e in error]))
			bob.write("\nend\n")
			alice.write("\nend\n")
			comb.write("\nend\n")
			comb.write("\n{} rounds completed".format(i))
			comb.write("\nIntensities:   " + str(intensities[0]))
			comb.write("\nIntensity p's: " + str(intensities[1]))
			comb.write("\nInt Counts:    " + str(int_count))
			comb.write("\nVac Counts:    " + str([float(v) for v in vac]))
			comb.write("\nClick Counts:  " + str([float(v) for v in click]))
			comb.write("\nDub Counts:    " + str([float(d) for d in dub]))
			comb.write("\nCorr Counts:   " + str([float(c) for c in corr]))
			comb.write("\nError Counts:  " + str([float(e) for e in error]))