{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.core.display import display, HTML\n", "display(HTML(\"\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.special as sps\n", "import matplotlib.ticker as mlt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "c = 299792.458; #speed of light in vacuum\n", "tprep = 6*10**(-6); #preparation time to create an entangled memory-photon\n", "\n", "#fibre parameter\n", "n = 1.44; #refractive index of the fibre\n", "L0 = 0.542; #attenuation length of the fibre\n", "pce = 0.49; #collection efficiency\n", "pzpl = 0.46; #post-selection of the photons - ZPL\n", "\n", "\n", "#Quantum memory parameters\n", "Fm = 0.95; # electron spin measurement depolarising parameter (single-qubit)\n", "Fg = 0.98; #electron-carbon two-qubit gate depolarising parameter (two-qubits)\n", "\n", "\n", "#Detection parameters\n", "DCperSec = 10; #Dark Count per second\n", "IntTime = 20; #Integration time in ns\n", "t_offset = 1.28; #offset due to time-filtering of photons from the excitation pulse\n", "Tau = 6.48; #characteristic time of the exponential emission decay of the NV in ns\n", "pdet = 0.8; #detector efficiency\n", "\n", "#dephasing parameters\n", "a0 = 1/2000; \n", "a1 = 1/3;\n", "Fprep = 0.99;\n", "\n", "#depolarising parameters\n", "b0 = 1/5000;\n", "b1 = 1/3;\n", "\n", "#Parties parameters\n", "thetalice = 0.5;\n", "thetabob = 0.5;\n", "LA = 4*L0;\n", "LB = 4*L0;\n", "\n", "#Uncertainty of the internal phase for single-photon\n", "DeltaPhi = 14.3*2*np.pi/360;\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#collective parameters\n", "Fbell = (Fg**2)*(Fm**2);#depolarising parameter for Bell measurement (to be used only for SiSQuaRe where the states are Bell diagonal as Fg acts on 2 qubits and Fm on 1)\n", "Fswap = Fg**2; #depolarising parameter for the swap gate\n", "lamb = sps.iv(1,1/DeltaPhi**2)/(2 * sps.iv(0,1/DeltaPhi**2)) + 1/2 #dephasing parameter due to the unknown phase of the photon (von Mises distribution)\n", "papp = pce*pzpl*pdet #total apparatus efficiency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Common functions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def pdet_prime(IntTime): #efficiency of the detector and losses due to finite detection window\n", " return pdet * (np.exp(-(t_offset/Tau)) - np.exp(-((IntTime+t_offset)/Tau)));\n", " #return 1\n", " \n", "def papp_prime(IntTime): #total apparatus efficiency including time window\n", " pde = pdet_prime(IntTime)\n", " return pce*pzpl*pde;\n", " \n", "def pd(IntTime): #probability of having at least a single dark count click within the time window\n", " return 1 - np.exp(-DCperSec*IntTime*10**(-9));\n", "\n", "def eta(L): #fibre losses over a distance L\n", " return np.exp(-L/L0);\n", "\n", "#depolarising and dephasing decoherence function including the effect of hyperfine coupling and time (decoherence due to time of light travelling distance L)\n", "def a(L):\n", " return a0 + a1*(L*n/c + tprep);\n", "\n", "def b(L):\n", " return b0 + b1*(L*n/c + tprep);\n", "\n", "def AveExp(x,theta, IntTime, L, nstar): #Average of the exponential of x during nstar trials\n", " Pbob = prob1click(theta,L,IntTime)\n", " A = np.exp(-x)*Pbob/(1-(1-Pbob)**nstar) \n", " B = (1-(1-Pbob)**(nstar)*np.exp(-x*nstar))/(1-(1-Pbob)*np.exp(-x))\n", " return A*B\n", "\n", "def h(p): #binary entropy function\n", " if p==0 or p==1:\n", " return 0\n", " else:\n", " A = -p*np.log2(p)- (1-p)*np.log2(1-p)\n", " return A\n", "\n", "def H(T): #Shannon entropy function\n", " H = 0\n", " for x in T:\n", " if x==0:\n", " H -= 0\n", " else:\n", " H -= x*np.log2(x)\n", " return H\n", "\n", "\n", "def thermal_channel_benchmark(eta, nbar): #thermal bound for channel with losses eta and mean number of thermal photons nbar\n", " if nbar0:\n", " return (nbar+1)*np.log2(nbar+1)-nbar*np.log2(nbar)\n", " else:\n", " return 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Single-photon scheme functions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def P0(L, IntTime): #probability that given one photon is emitted, it reaches the detector and no dark count\n", " Eta = papp_prime(IntTime)*eta(L/2)\n", " Pd = pd(IntTime)\n", " return Eta*(1-Pd)\n", "\n", "\n", "def P1(L, IntTime): #probability that one photon is emitted then lost and we get a dark count\n", " Eta = papp_prime(IntTime)*eta(L/2)\n", " Pd = pd(IntTime)\n", " return 2*Pd*(1-Pd)*(1-Eta)\n", "\n", "def P2(L, IntTime): #probability that two photon were emitted and we got one click\n", " Eta = papp_prime(IntTime)*eta(L/2)\n", " Pd = pd(IntTime)\n", " return 2*(1-Eta)*Eta*(1-Pd) + (1-Pd)*Eta**2 + 2*((1-Eta)**2)*Pd*(1-Pd)\n", "\n", "def P3(IntTime): #probability that no photon is emitted but we got a click\n", " Pd = pd(IntTime)\n", " return 2*Pd*(1-Pd)\n", "\n", "def prob1click(theta,L,IntTime): #number of channel uses = probability that one of the detector clicks\n", " Eta = papp_prime(IntTime)*eta(L/2)\n", " Pd = pd(IntTime)\n", " return 2*(1-Pd)*(Eta*(np.cos(theta)**2 )*( 1-(Eta/2)*(np.cos(theta)**2)) + ((1-Eta*(np.cos(theta)**2))**2)*Pd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First setup - SiSQuaRe scheme" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#probabilities of detecting a photon by Alice or Bob (using squashing map setups)\n", "def QR1ProbBB84(L,IntTime): #L is the distance between Alice or Bob and the repeater\n", " return 1-(1-papp_prime(IntTime)*eta(L))*(1-pd(IntTime))**2\n", "\n", "def QR1Prob6states(L,IntTime):\n", " return 1-(1-papp_prime(IntTime)*eta(L))*(1-pd(IntTime))**6\n", "\n", "#number of channel uses\n", "def QR1EspSumBB84(LA,LB,IntTime,nstar):\n", " Palice = QR1ProbBB84(LA,IntTime)\n", " Pbob = QR1ProbBB84(LB,IntTime)\n", " if Pbob == 0 or (nstar == 0):\n", " return 0\n", " else:\n", " return 1/(Palice-Palice*(1-Pbob)**nstar) + 1/Pbob\n", "\n", "def QR1EspSum6states(LA,LB,IntTime,nstar):\n", " Palice = QR1Prob6states(LA,IntTime)\n", " Pbob = QR1Prob6states(LB,IntTime)\n", " if (Pbob == 0) or (nstar == 0):\n", " return 0\n", " else:\n", " return 1/(Palice- Palice*(1-Pbob)**nstar) + 1/Pbob\n", "\n", "#Yield\n", "def QR1YieldSumBB84(LA,LB,IntTime,nstar): \n", " if (QR1EspSumBB84(LA,LB,IntTime,nstar) == 0):\n", " return 0\n", " else:\n", " return 1/QR1EspSumBB84(LA,LB,IntTime,nstar)\n", "\n", "def QR1YieldSum6states(LA,LB,IntTime,nstar):\n", " if QR1EspSum6states(LA,LB,IntTime,nstar) == 0:\n", " return 0\n", " else: \n", " return 1/QR1EspSum6states(LA,LB,IntTime,nstar)\n", "\n", "#average of the exponentials over nstar attempts\n", "def QR1AveExpBB84(x,L,IntTime,nstar):\n", " Pbob = QR1ProbBB84(L,IntTime)\n", " if (Pbob ==0) or (nstar == 0):\n", " return 0\n", " else:\n", " A = Pbob*np.exp(-x)/(1-(1-Pbob)**nstar)\n", " B = (1-(1-Pbob)**(nstar)*np.exp(-x*nstar))/(1-(1-Pbob)*np.exp(-x))\n", " return A*B\n", "\n", "def QR1AveExp6states(x,L,IntTime,nstar):\n", " Pbob = QR1Prob6states(L,IntTime)\n", " if (Pbob ==0) or (nstar == 0):\n", " return 0\n", " else:\n", " A = np.exp(-x)*Pbob/(1-(1-Pbob)**nstar) \n", " B = (1-(1-Pbob)**(nstar)*np.exp(-x*nstar))/(1-(1-Pbob)*np.exp(-x))\n", " return A*B\n", "\n", "def QR1AlphaBB84(L,IntTime): #noise due to the dark counts (using squashing map setup)\n", " return papp_prime(IntTime)*eta(L)*(1-pd(IntTime))/(1-(1-papp_prime(IntTime)*eta(L))*(1-pd(IntTime))**2)\n", "\n", "#QBERs\n", "def QR1eXAvBB84(LA,LB,IntTime,nstar): #Average quantum bit error rate in the X and Y basis\n", " alphaA = QR1AlphaBB84(LA,IntTime)\n", " alphaB = QR1AlphaBB84(LB,IntTime)\n", " \n", " A = a(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " B = b(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " avexp = QR1AveExpBB84(A+B,LB,IntTime,nstar)\n", " \n", " return 1/2-1/2*Fswap*Fbell*alphaA*alphaB*avexp*(2*Fprep-1)**(2)\n", "\n", "def QR1eZAvBB84(LA,LB,IntTime,nstar): #Average quantum bit error rate in the Z basis\n", " alphaA = QR1AlphaBB84(LA,IntTime)\n", " alphaB = QR1AlphaBB84(LB,IntTime)\n", " \n", " B = b(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " avexp = QR1AveExpBB84(B,LB,IntTime,nstar)\n", " \n", " return 1/2-1/2*Fswap*Fbell*alphaA*alphaB*avexp \n", "\n", "def QR1Alpha6states(L,IntTime): #noise due to the dark counts\n", " return (papp_prime(IntTime)*eta(L)*(1-pd(IntTime))**5)/(1-(1-papp_prime(IntTime)*eta(L))*(1-pd(IntTime))**6)\n", "\n", "def QR1eXAv6states(LA,LB,IntTime,nstar): #Average quantum bit error rate in the X and Y basis\n", " alphaA = QR1Alpha6states(LA,IntTime)\n", " alphaB = QR1Alpha6states(LB,IntTime)\n", " \n", " A = a(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " B = b(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " avexp = QR1AveExp6states(A+B,LB,IntTime,nstar)\n", " \n", " return 1/2-1/2*Fswap*Fbell*alphaA*alphaB*avexp*(2*Fprep-1)**(2)\n", "\n", "def QR1eZAv6states(LA,LB,IntTime,nstar): #Average quantum bit error rate in the Z basis\n", " alphaA = QR1Alpha6states(LA,IntTime)\n", " alphaB = QR1Alpha6states(LB,IntTime)\n", " \n", " B = b(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " avexp = QR1AveExp6states(B,LB,IntTime,nstar)\n", " \n", " return 1/2-1/2*Fswap*Fbell*alphaA*alphaB*avexp \n", "\n", "#Secret-key fractions\n", "def QR1secretKeyFracBB84(LA,LB,IntTime,nstar): #Secret-key fraction for fully asymmetric one-way BB84 protocol, see Appendix G\n", " Ex = QR1eXAvBB84(LA,LB,IntTime,nstar)\n", " Ez = QR1eZAvBB84(LA,LB,IntTime,nstar)\n", " \n", " r = 1 - h(Ez) - h(Ex)\n", " \n", " return max([r,0])\n", "\n", "def QR1secretKeyFrac6states(LA,LB,IntTime,nstar): # secret-key fraction with advantage distillation and symmetric 6-states protocol, see Appendix G\n", " Exy = QR1eXAv6states(LA,LB,IntTime,nstar)\n", " Ez = QR1eZAv6states(LA,LB,IntTime,nstar)\n", " \n", " #key in Z-basis\n", " p = [1-(Ez/2)-Exy, Exy-Ez/2, Ez/2, Ez/2] \n", " PX = [1-2*Ez + 2*(Ez)**2, 2*(1-Ez)*Ez] \n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r = max([A,B,0])\n", " \n", " #key in X and Y bases\n", " p = [1-(Ez/2)-Exy, Ez/2, Exy-Ez/2, Ez/2] \n", " PX = [1-2*Exy + 2*(Exy)**2, 2*(1-Exy)*Exy]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r2 = max([A,B,0])\n", " \n", " return (1/3)*((1/3)*r + (2/3)*r2)\n", "\n", "#secret-key rates\n", "def QR1SecKeyRateSumBB84(LA,LB,IntTime,nstar): #secret-key rate using asymetric BB84 protocol\n", " Y = QR1YieldSumBB84(LA,LB,IntTime,nstar)\n", " r = QR1secretKeyFracBB84(LA,LB,IntTime, nstar)\n", " \n", " return Y*r/2\n", "\n", "def QR1SecKeyRateSum6states(LA,LB,IntTime,nstar): #secret-key rate using 6-states with advantage distillation protocol\n", " Y = QR1YieldSum6states(LA,LB,IntTime,nstar)\n", " r = QR1secretKeyFrac6states(LA,LB,IntTime,nstar)\n", " \n", " return Y*r/2\n", "\n", "#Rate optimized over BB84 and 6-states\n", "def QR1SecretKeyRateSum(LA,LB,IntTime,nstar): #the secret-key rate optimised over BB84 and the 6-state with advantage distillation\n", " A = QR1SecKeyRateSum6states(LA,LB,IntTime,nstar)\n", " B = QR1SecKeyRateSumBB84(LA,LB,IntTime,nstar)\n", " \n", " R = max([A,B])\n", " return R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two-node setup: single-photon scheme" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def QR2Yield(theta,L,IntTime): #number of channel uses = probability that one of the detector clicks\n", " return prob1click(theta,L,IntTime)\n", "\n", "def Coeffs2node(theta,L,IntTime):\n", "\n", " Y = prob1click(theta,L,IntTime)\n", " p0 = P0(L, IntTime)\n", " p1 = P1(L,IntTime)\n", " p2 = P2(L, IntTime)\n", " p3 = P3(IntTime)\n", "\n", "\n", " A = ((1)/Y)*2*(np.cos(theta)**2)*(np.sin(theta)**2)*p0*((Fprep**2 + (1-Fprep)**2)*(lamb) + 2*Fprep*(1-Fprep)*(1-lamb))\n", " B = ((1)/Y)*2*(np.cos(theta)**2)*(np.sin(theta)**2)*p0*((Fprep**2 + (1-Fprep)**2)*(1-lamb) + 2*Fprep*(1-Fprep)*(lamb))\n", " C = ((1)/Y)*(np.cos(theta)**2)*(np.sin(theta)**2)*p1\n", " D = ((1)/Y)*(np.cos(theta)**4)*p2\n", " E = ((1)/Y)*(np.sin(theta)**4)*p3\n", "\n", " return Y, p0, p1, p2, p3, lamb, A, B, C, D, E\n", "\n", "def QR2Errz(theta,L,IntTime):\n", " \n", " Y, p0, p1, p2, p3, lamb, A, B, C, D, E = Coeffs2node(theta,L,IntTime)\n", " \n", " term = 0.5 + (-0.5 + D + E)*Fm**2\n", " \n", " return term\n", "\n", "def QR2Errxy(theta,L,IntTime):\n", " \n", " Y, p0, p1, p2, p3, lamb, A, B, C, D, E = Coeffs2node(theta,L,IntTime)\n", " \n", " term = (1 + (-A + B)*Fm**2)/2.\n", " \n", " return term\n", "\n", "def QR2secretKeyFrac6states(theta,L,IntTime): #Secret-key fraction with advantage distillation and 6-states protocol, see Appendix G\n", " Exy = QR2Errxy(theta,L,IntTime)\n", " Ez = QR2Errz(theta,L,IntTime)\n", " \n", " #key in Z-basis\n", " p = [1-(Ez/2)-Exy, Exy-Ez/2, Ez/2, Ez/2] \n", " PX = [1-2*Ez + 2*(Ez)**2, 2*(1-Ez)*Ez]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ] \n", " A = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r = max([A,B,0])\n", " \n", " return r\n", "\n", "def QR2secretKeyFracBB84(theta,L,IntTime): #Secret-key fraction for fully asymmetric one-way BB84 protocol, see Appendix G\n", " Exy = QR2Errxy(theta,L,IntTime)\n", " Ez = QR2Errz(theta,L,IntTime)\n", " \n", " r = 1 - h(Ez) - h(Exy)\n", " \n", " return np.max([r,0])\n", "\n", "def QR2Rate(theta,L,IntTime): #Secret-key rate for 6-states protocol\n", " r = QR2secretKeyFrac6states(theta,L,IntTime)\n", " Y = QR2Yield(theta,L,IntTime)\n", " return Y*r\n", "\n", "def QR2RateBB84(theta,L,IntTime): #Secret-key rate for BB84 protocol\n", " r = QR2secretKeyFracBB84(theta,L,IntTime)\n", " Y = QR2Yield(theta,L,IntTime)\n", " return Y*r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Three nodes setup: SPOTL scheme" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def Coeffs3node(thetalice,thetabob,LA,LB,IntTime,nstar):\n", "\n", " Ya = prob1click(thetalice,LA,IntTime)\n", " p0a = P0(LA,IntTime)\n", " p1a = P1(LA,IntTime)\n", " p2a = P2(LA,IntTime)\n", " p3a = P3(IntTime)\n", "\n", " Yb = prob1click(thetabob,LB,IntTime)\n", " p0b = P0(LB, IntTime)\n", " p1b = P1(LB,IntTime)\n", " p2b = P2(LB,IntTime)\n", " p3b = P3(IntTime)\n", "\n", " A = a(LB)\n", " B = b(LB)\n", "\n", "\n", " F1 = AveExp(B,thetabob,IntTime,LB,nstar)\n", " F12 = (AveExp(B,thetabob,IntTime,LB,nstar) + AveExp(A+B,thetabob,IntTime,LB,nstar))/2\n", "\n", " A1 = ((1)/Ya)*2*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p0a*((Fprep**2 + (1-Fprep)**2)*(lamb) + 2*Fprep*(1-Fprep)*(1-lamb))\n", " B1 = ((1)/Ya)*2*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p0a*((Fprep**2 + (1-Fprep)**2)*(1-lamb) + 2*Fprep*(1-Fprep)*(lamb))\n", " C1 = ((1)/Ya)*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p1a\n", " D1 = ((1)/Ya)*(np.cos(thetalice)**4)*p2a\n", " E1 = ((1)/Ya)*(np.sin(thetalice)**4)*p3a\n", "\n", "\n", " A2 = (1/Yb)*2*(np.cos(thetabob)**2)*(np.sin(thetabob)**2)*p0b*((Fprep**2 + (1-Fprep)**2)*(lamb) + 2*Fprep*(1-Fprep)*(1-lamb))\n", " B2 = (1/Yb)*2*(np.cos(thetabob)**2)*(np.sin(thetabob)**2)*p0b*((Fprep**2 + (1-Fprep)**2)*(1-lamb) + 2*Fprep*(1-Fprep)*(lamb))\n", " C2 = (1/Yb)*(np.cos(thetabob)**2)*(np.sin(thetabob)**2)*p1b\n", " D2 = (1/Yb)*(np.cos(thetabob)**4)*p2b\n", " E2 = (1/Yb)*(np.sin(thetabob)**4)*p3b\n", " \n", " return F1, F12, A1, B1, C1, D1, E1, A2, B2, C2, D2, E2\n", "\n", "def QR3EspSum(thetalice,thetabob,IntTime,LA,LB,nstar): #Number of channel uses\n", " Palice = prob1click(thetalice,LA,IntTime)\n", " Pbob = prob1click(thetabob,LB,IntTime)\n", " if (Pbob == 0) or (nstar == 0):\n", " return 0\n", " else:\n", " return 1/(Palice*(1-(1-Pbob)**nstar)) + 1/Pbob\n", " \n", "def QR3Yield(thetalice,thetabob,IntTime,LA,LB,nstar): #Yield\n", " if (QR3EspSum(thetalice,thetabob,IntTime,LA,LB,nstar) == 0):\n", " return 0\n", " else:\n", " return 1/QR3EspSum(thetalice,thetabob,IntTime,LA,LB,nstar)\n", "\n", "#QBERs in the computational basis\n", "def QR3Errz(thetalice,thetabob,LA,LB,IntTime,nstar):\n", " \n", " F1, F12, A1, B1, C1, D1, E1, A2, B2, C2, D2, E2 = Coeffs3node(thetalice,thetabob,LA,LB,IntTime,nstar) \n", " \n", " term = 0.5 - ((-1 + 2*D1 + 2*E1)*(-1 + 2*D2 + 2*E2)*F1*Fg**2*Fm**4*Fswap)/2.\n", " \n", " return term\n", "\n", "#QBERs in the XY basis\n", "def QR3Errxy(thetalice,thetabob,LA,LB,IntTime,nstar):\n", "\n", " F1, F12, A1, B1, C1, D1, E1, A2, B2, C2, D2, E2 = Coeffs3node(thetalice,thetabob,LA,LB,IntTime,nstar) \n", " \n", " term = (1 - (A1 - B1)*(A2 - B2)*(-F1 + 2*F12)*Fg**2*Fm**4*Fswap)/2.\n", " \n", " return term\n", "\n", "#Secret-key fraction\n", "def QR3secretKeyFrac6states(thetalice,thetabob,LA,LB,IntTime,nstar): #Secret-key fraction with advantage distillation and fully asymmetric 6-states protocol, see Appendix G\n", " \n", " Exy = QR3Errxy(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " Ez = QR3Errz(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " \n", " #key in X- and Y- bases\n", " p = [1-(Ez/2)-Exy, Ez/2, Exy-Ez/2, Ez/2] \n", " PX = [1-2*Exy + 2*(Exy)**2, 2*(1-Exy)*Exy]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r = max([A,B,0])\n", " \n", " return r\n", "\n", "def QR3secretKeyFrac6statesNoAdvDist(thetalice,thetabob,LA,LB,IntTime,nstar):\n", " \n", " Exy = QR3Errxy(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " Ez = QR3Errz(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " \n", " r = 1 - Ez * h(1/2) - (1-Ez) * h((1-(2*Exy+Ez)/2)/(1-Ez)) - h(Ez)\n", " \n", " return max([r,0])\n", "\n", "\n", "\n", "def QR3secretKeyFracBB84(thetalice,thetabob,LA,LB,IntTime,nstar): #Secret-key fraction for fully asymmetric one-way BB84 protocol, see Appendix G\n", " \n", " Exy = QR3Errxy(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " Ez = QR3Errz(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " \n", " r = 1 - h(Ez) - h(Exy)\n", " \n", " return max([r,0])\n", "\n", "#Rate\n", "def QR3Rate(thetalice,thetabob,LA,LB,IntTime,nstar):\n", " r = QR3secretKeyFrac6states(thetalice,thetabob,LA,LB,IntTime,nstar)\n", " Y = QR3Yield(thetalice,thetabob,IntTime,LA,LB,nstar)\n", " return Y*r\n", "\n", "def QR3RateBB84(thetalice,thetabob,LA,LB,DCperSec,IntTime,Fm,Fg,Fprep,Fswap,Fbell,DeltaPhi,a0,a1,b0,b1,n,nstar):\n", " r = QR3secretKeyFracBB84(thetalice,thetabob,LA,LB,DCperSec,IntTime,Fm,Fg,Fprep,Fswap,Fbell,DeltaPhi,a0,a1,b0,b1,n,nstar)\n", " Y = QR3Yield(thetalice,thetabob,DCperSec,IntTime,LA,LB,nstar)\n", " return Y*r\n", "\n", "def QR3Rate6StateNoAdvDist(thetalice,thetabob,LA,LB,DCperSec,IntTime,Fm,Fg,Fprep,Fswap,Fbell,DeltaPhi,a0,a1,b0,b1,n,nstar):\n", " r = QR3secretKeyFrac6statesNoAdvDist(thetalice,thetabob,LA,LB,DCperSec,IntTime,Fm,Fg,Fprep,Fswap,Fbell,DeltaPhi,a0,a1,b0,b1,n,nstar)\n", " Y = QR3Yield(thetalice,thetabob,DCperSec,IntTime,LA,LB,nstar)\n", " return Y*r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.5 node setup: SPADS scheme:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def Coeffs25node(thetalice,LA,LB,IntTime,nstar):\n", "\n", " Ya = prob1click(thetalice,LA,IntTime)\n", " p0a = P0(LA, IntTime)\n", " p1a = P1(LA, IntTime)\n", " p2a = P2(LA, IntTime)\n", " p3a = P3(IntTime)\n", "\n", " A = a(2*LB) #factor of two because for the waiting time the quantum signal needs to be sent over this distance and then classical signal back\n", " B = b(2*LB)\n", "\n", "\n", " F1_BB84 = QR1AveExpBB84(B,LB,IntTime,nstar)\n", " F12_BB84 = (QR1AveExpBB84(B,LB,IntTime,nstar) + QR1AveExpBB84(A+B,LB,IntTime,nstar))/2\n", " \n", " F1_6states = QR1AveExp6states(B,LB,IntTime,nstar)\n", " F12_6states = (QR1AveExp6states(B,LB,IntTime,nstar) + QR1AveExp6states(A+B,LB,IntTime,nstar))/2\n", "\n", " A1 = ((1)/Ya)*2*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p0a*((Fprep**2 + (1-Fprep)**2)*(lamb) + 2*Fprep*(1-Fprep)*(1-lamb))\n", " B1 = ((1)/Ya)*2*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p0a*((Fprep**2 + (1-Fprep)**2)*(1-lamb) + 2*Fprep*(1-Fprep)*(lamb))\n", " C1 = ((1)/Ya)*(np.cos(thetalice)**2)*(np.sin(thetalice)**2)*p1a\n", " D1 = ((1)/Ya)*(np.cos(thetalice)**4)*p2a\n", " E1 = ((1)/Ya)*(np.sin(thetalice)**4)*p3a\n", "\n", " A2 = Fprep\n", " B2 = 1-Fprep\n", " \n", " return F1_BB84, F12_BB84, F1_6states, F12_6states, A1, B1, C1, D1, E1, A2, B2\n", "\n", "def QR25EspSumBB84(thetalice,IntTime,LA,LB,nstar): #Number of channel uses\n", " Palice = prob1click(thetalice,LA, IntTime)\n", " Pbob = QR1ProbBB84(LB,IntTime)\n", " if (Pbob == 0) or (nstar == 0):\n", " return 0\n", " else:\n", " return 1/(Palice*(1-(1-Pbob)**nstar)) + 1/Pbob\n", " \n", "def QR25EspSum6states(thetalice,IntTime,LA,LB,nstar): #Number of channel uses\n", " Palice = prob1click(thetalice,LA, IntTime)\n", " Pbob = QR1Prob6states(LB,IntTime)\n", " if (Pbob == 0) or (nstar == 0):\n", " return 0\n", " else:\n", " return 1/(Palice*(1-(1-Pbob)**nstar)) + 1/Pbob\n", " \n", "def QR25YieldBB84(thetalice,IntTime,LA,LB,nstar): #Yield\n", " if (QR25EspSumBB84(thetalice,IntTime,LA,LB,nstar) == 0):\n", " return 0\n", " else:\n", " return 1/QR25EspSumBB84(thetalice,IntTime,LA,LB,nstar)\n", " \n", "def QR25Yield6states(thetalice,IntTime,LA,LB,nstar): #Yield\n", " if (QR25EspSum6states(thetalice,IntTime,LA,LB,nstar) == 0):\n", " return 0\n", " else:\n", " return 1/QR25EspSum6states(thetalice,IntTime,LA,LB,nstar) \n", "\n", "#QBERs in the computational basis\n", "def QR25ErrzBB84(thetalice,LA,LB,IntTime,nstar):\n", " \n", " F1_BB84, F12_BB84, F1_6states, F12_6states, A1, B1, C1, D1, E1, A2, B2 = Coeffs25node(thetalice,LA,LB,IntTime,nstar) \n", "\n", " alphaB = QR1AlphaBB84(LB,IntTime)\n", " \n", " term = (1 + (-1 + 2*D1 + 2*E1)*F1_BB84*Fg**2*Fm**3*Fswap*alphaB)/2.\n", " \n", " return term\n", "\n", "def QR25Errz6states(thetalice,LA,LB,IntTime,nstar):\n", " \n", " F1_BB84, F12_BB84, F1_6states, F12_6states, A1, B1, C1, D1, E1, A2, B2 = Coeffs25node(thetalice,LA,LB,IntTime,nstar) \n", "\n", " alphaB = QR1Alpha6states(LB,IntTime)\n", " \n", " term = (1 + (-1 + 2*D1 + 2*E1)*F1_6states*Fg**2*Fm**3*Fswap*alphaB)/2.\n", " \n", " return term\n", "\n", "#QBERs in the XY basis\n", "def QR25ErrxyBB84(thetalice,LA,LB,IntTime,nstar):\n", "\n", " F1_BB84, F12_BB84, F1_6states, F12_6states, A1, B1, C1, D1, E1, A2, B2 = Coeffs25node(thetalice,LA,LB,IntTime,nstar) \n", "\n", " alphaB = QR1AlphaBB84(LB,IntTime)\n", " \n", " term = (1 - (A1 - B1)*(A2 - B2)*(-F1_BB84 + 2*F12_BB84)*Fg**2*Fm**3*Fswap*alphaB)/2.\n", " \n", " return term\n", "\n", "def QR25Errxy6states(thetalice,LA,LB,IntTime,nstar):\n", "\n", " F1_BB84, F12_BB84, F1_6states, F12_6states, A1, B1, C1, D1, E1, A2, B2 = Coeffs25node(thetalice,LA,LB,IntTime,nstar) \n", "\n", " alphaB = QR1Alpha6states(LB,IntTime)\n", " \n", " term = (1 - (A1 - B1)*(A2 - B2)*(-F1_6states + 2*F12_6states)*Fg**2*Fm**3*Fswap*alphaB)/2.\n", " \n", " return term\n", "\n", "#Secret-key fraction\n", "def QR25secretKeyFrac6states(thetalice,LA,LB,IntTime,nstar): #Secret-key fraction with advantage distillation and symmetric 6-state protocol, see Appendix G\n", " \n", " Exy = QR25Errxy6states(thetalice,LA,LB,IntTime,nstar)\n", " Ez = QR25Errz6states(thetalice,LA,LB,IntTime,nstar)\n", " \n", " #key in Z-basis\n", " p = [1-(Ez/2)-Exy, Exy-Ez/2, Ez/2, Ez/2]\n", " PX = [1-2*Ez + 2*(Ez)**2, 2*(1-Ez)*Ez]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A1 = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B1 = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r =max([A1,B1,0])\n", " \n", " #key in X- and Y-bases\n", " p = [1-(Ez/2)-Exy, Ez/2, Exy-Ez/2, Ez/2]\n", " PX = [1-2*Exy + 2*(Exy)**2, 2*(1-Exy)*Exy]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A2 = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B2 = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r2 = max([A2,B2,0])\n", " \n", " return (1/3)*((1/3)*r + (2/3)*r2) \n", "\n", "def QR25secretKeyFrac6statesNoAdvDist(thetalice,LA,LB,IntTime,nstar):#Secret-key fraction for one-way symmetric 6-state protocol, see Appendix G\n", " \n", " Exy = QR25Errxy6states(thetalice,LA,LB,IntTime,nstar)\n", " Ez = QR25Errz6states(thetalice,LA,LB,IntTime,nstar)\n", " \n", " r = 1 - Ez * h(1/2) - (1-Ez) * h((1-(2*Exy+Ez)/2)/(1-Ez)) - h(Ez)\n", " \n", " return max([r/3,0])\n", "\n", "def QR25secretKeyFracBB84(thetalice,LA,LB,IntTime,nstar): #Secret-key fraction for fully asymmetric one-way BB84 protocol, see Appendix G\n", " \n", " Exy = QR25ErrxyBB84(thetalice,LA,LB,IntTime,nstar)\n", " Ez = QR25ErrzBB84(thetalice,LA,LB,IntTime,nstar)\n", " \n", " r = 1 - h(Ez) - h(Exy)\n", " \n", " return max([r,0])\n", "\n", "#secret-key rates\n", "def QR25SecKeyRateSumBB84(thetalice,LA,LB,IntTime,nstar): #secret-key rate using asymetric BB84 protocol\n", " Y = QR25YieldBB84(thetalice,IntTime,LA,LB,nstar)\n", " r = QR25secretKeyFracBB84(thetalice,LA,LB,IntTime,nstar)\n", " \n", " return Y*r/2\n", "\n", "def QR25SecKeyRateSum6states(thetalice,LA,LB,IntTime,nstar): #secret-key rate using fully asymmetric 6states with advantage distillation protocol\n", " Y = QR25Yield6states(thetalice,IntTime,LA,LB,nstar)\n", " r = QR25secretKeyFrac6states(thetalice,LA,LB,IntTime,nstar)\n", " \n", " return Y*r/2\n", "\n", "#Rate optimized over BB84 and 6-states\n", "def QR25Rate(thetalice,LA,LB,IntTime,nstar): #the secret-key rate optimised over BB84 and the two 6-states protocols\n", " A = QR25SecKeyRateSum6states(thetalice,LA,LB,IntTime,nstar)\n", " B = QR25SecKeyRateSumBB84(thetalice,LA,LB,IntTime,nstar)\n", " \n", " R = max([A,B])\n", " \n", " return R\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Direct transmission using diamond as a single photon source" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#QBERs\n", "def DirecteXAvBB84(L,IntTime): #Average quantum bit error rate in the X and Y basis\n", " alphaB = QR1AlphaBB84(L,IntTime)\n", " \n", " return 1/2-1/2*Fm*alphaB*(2*Fprep-1)\n", "\n", "def DirecteZAvBB84(L,IntTime): #Average quantum bit error rate in the Z basis\n", " alphaB = QR1AlphaBB84(L,IntTime)\n", " \n", " return 1/2-1/2*Fm*alphaB \n", "\n", "def DirecteXAv6states(L,IntTime): #Average quantum bit error rate in the X and Y basis\n", " alphaB = QR1Alpha6states(L,IntTime)\n", " \n", " return 1/2-1/2*Fm*alphaB*(2*Fprep-1)\n", "\n", "def DirecteZAv6states(L,IntTime): #Average quantum bit error rate in the Z basis\n", " alphaB = QR1Alpha6states(L,IntTime)\n", " \n", " return 1/2-1/2*Fm*alphaB \n", "\n", "#Secret-key fractions\n", "def DirectsecretKeyFracBB84(L,IntTime): #Secret-key fraction for fully asymmetric one-way BB84 protocol, see Appendix G\n", " Ex = DirecteXAvBB84(L,IntTime)\n", " Ez = DirecteZAvBB84(L,IntTime)\n", " \n", " r = 1 - h(Ez) - h(Ex)\n", " \n", " return max([r,0])\n", "\n", "def DirectsecretKeyFrac6states(L, IntTime): #Secret-key fraction with advantage distillation and symmetric 6-state protocol, see Appendix G\n", " Exy = DirecteXAv6states(L,IntTime)\n", " Ez = DirecteZAv6states(L,IntTime)\n", " \n", " #key in Z-basis\n", " p = [1-(Ez/2)-Exy, Exy-Ez/2, Ez/2, Ez/2]\n", " PX = [1-2*Ez + 2*(Ez)**2, 2*(1-Ez)*Ez]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A1 = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B1 = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r = max([A1,B1,0])\n", " \n", " #key in X- and Y- bases\n", " p = [1-(Ez/2)-Exy, Ez/2, Exy-Ez/2, Ez/2]\n", " PX = [1-2*Exy + 2*(Exy)**2, 2*(1-Exy)*Exy]\n", " PprimeXZ = [(p[0]**2 + p[1]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[0]*p[1]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), (p[2]**2 + p[3]**2)/((p[0]+ p[1])**2 + (p[2]+ p[3])**2), 2*p[2]*p[3]/((p[0]+ p[1])**2 + (p[2]+ p[3])**2) ]\n", " A2 = 1- H(p) + PX[1]*(1/2)*h((p[0]*p[2]+ p[1]*p[3])/((p[0]+p[1])*(p[2]+p[3])))\n", " B2 = PX[0]*(1/2)*(1-H(PprimeXZ))\n", " \n", " r2 = max([A2,B2,0])\n", " \n", " return (1/3)*((1/3)*r + (2/3)*r2)\n", "\n", "#secret-key rates\n", "def DirectSecKeyRateBB84(L,IntTime): #secret-key rate using asymetric BB84 protocol\n", " Y = QR1ProbBB84(L,IntTime)\n", " r = DirectsecretKeyFracBB84(L,IntTime)\n", " \n", " return Y*r/2\n", "\n", "def DirectSecKeyRate6states(L,IntTime): #secret-key rate using 6states with advantage distillation protocol\n", " Y = QR1Prob6states(L,IntTime)\n", " r = DirectsecretKeyFrac6states(L,IntTime)\n", " \n", " return Y*r/2\n", "\n", "#Rate optimized over BB84 and 6-states\n", "def DirectSecretKeyRate(L,IntTime): #the secret-key rate optimised over BB84 and the two 6-states protocols\n", " A = DirectSecKeyRate6states(L,IntTime)\n", " B = DirectSecKeyRateBB84(L,IntTime)\n", " \n", " R = max([A,B])\n", " \n", " return R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimisation functions" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def QR1RateoptCutoff(LA,LB,IntTime,nstarMin,nstarMax): #Optimize the secret-key rate over a cutoff in [nstarMin ; nstarMax] for the SiSQuaRe scheme\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR1SecretKeyRateSum(LA,LB,IntTime, i)\n", " if ( R> value):\n", " value = R\n", " return value\n", "\n", "def QR1cutoffOpt(LA,LB,IntTime, nstarMin,nstarMax): #give the optimal cutoff in [nstarMin ; nstarMax] for a set of parameters for the SiSQuaRe scheme\n", " optcut = 0\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR1SecretKeyRateSum(LA,LB,IntTime, i)\n", " if ( R> value):\n", " value = R\n", " optcut = i\n", " return optcut\n", "\n", "def QR2ThetaOpt(L,IntTime): #Give the optimal angle for the single-photon setup\n", " theta = np.arange(1.1,np.pi/2,0.005)\n", " value = 0\n", " r = 0\n", " for x in theta:\n", " if QR2Rate(x,L,IntTime) > r:\n", " r = QR2Rate(x,L,IntTime)\n", " value = x\n", " return value\n", "\n", "def QR2RateoptTheta(L,IntTime): #optimize the rate over theta for the single-photon setup\n", " theta = QR2ThetaOpt(L,IntTime)\n", " return QR2Rate(theta,L,IntTime)\n", "\n", "def QR3ThetaOpt(LA,LB,IntTime,nstar):#Give the optimal angle for the SPOTL setup for a cutoff nstar, assume thetalice=thetabob\n", " theta = np.arange(1.1,np.pi/2,0.005)\n", " value = 0\n", " r = 0\n", " for x in theta:\n", " if QR3Rate(x,x,LA,LB,IntTime,nstar) > r:\n", " r = QR3Rate(x,x,LA,LB,IntTime,nstar)\n", " value = x\n", " \n", " return value\n", "\n", "def QR3RateoptTheta(LA,LB,IntTime,nstar):#optimize the rate over theta for the SPOTL setup for cutoff nstar, assume thetalice=thetabob\n", " theta = QR3ThetaOpt(LA,LB,IntTime,nstar)\n", " return QR3Rate(theta,theta,LA,LB,IntTime,nstar)\n", "\n", "def QR3RateoptCutoff(thetalice,thetabob,LA,LB,IntTime,nstarMin,nstarMax): #Optimize the secret-key rate for the SPOTL setup over a cutoff in [nstarMin,nstarMax] for the angles thetalice, thetabob\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR3Rate(thetalice,thetabob,LA,LB,IntTime,i)\n", " if ( R> value):\n", " value = R\n", " return value\n", "\n", "def QR3cutoffOpt(thetalice,thetabob,LA,LB,IntTime,nstarMin,nstarMax): #give the optimal cutoff for a set of parameters for the SPOTL setup for the angles thetalice, thetabob\n", " optcut = 0\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR3Rate(thetalice,thetabob,LA,LB,IntTime,i)\n", " if ( R> value):\n", " value = R\n", " optcut = i\n", " return optcut\n", "\n", "def QR25optTheta(LA,LB,IntTime,nstar):#Give the optimal angle for the SPADS setup for cutoff nstar\n", " theta = np.arange(1.1,np.pi/2,0.005)\n", " value = 0\n", " r = 0\n", " for x in theta:\n", " if QR25Rate(x,LA,LB,IntTime,nstar) > r:\n", " r = QR25Rate(x,LA,LB,IntTime,nstar)\n", " value = x\n", " \n", " return value\n", "\n", "def QR25RateoptTheta(LA,LB,IntTime,nstar):#optimize the rate over theta for the SPADS setup for cutoff nstar\n", " theta = QR25optTheta(LA,LB,IntTime,nstar)\n", " return QR25Rate(theta,LA,LB,IntTime,nstar)\n", "\n", "def QR25RateoptCutoff(thetalice,LA,LB,IntTime,nstarMin,nstarMax): #Optimize the secret-key rate for the SPADS setup over a cutoff in [nstarMin,nstarMax]\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR25Rate(thetalice,LA,LB,IntTime,i)\n", " if ( R> value):\n", " value = R\n", " return value\n", "\n", "def QR25cutoffOpt(thetalice,LA,LB,IntTime,nstarMin,nstarMax): #give the optimal cutoff in [nstarMin,nstarMax] for a set of parameters for the SPADS setup\n", " optcut = 0\n", " value = 0\n", " cutoff = np.arange(nstarMin,nstarMax,1)\n", " for i in cutoff:\n", " R = QR25Rate(thetalice,LA,LB,IntTime,i)\n", " if ( R> value):\n", " value = R\n", " optcut = i\n", " return optcut" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#Plot secret key rate as a function of theta for a fixed distance and optimised over cut-off for the SPOTL setup\n", "fixed_dist = 12.5\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "IntTimeRange = np.arange(5,35,5)\n", "L = []\n", "table=[]\n", "\n", "for x in theta:\n", " for z in IntTimeRange: \n", " table.append(QR3RateoptCutoff(x,x,fixed_dist*L0/2,fixed_dist*L0/2,z,5,100))\n", " rate = max(table)\n", " L.append(rate)\n", " table=[]\n", "\n", "#filename = \"thetaSPOTL_correct.txt\"\n", "#file = open(filename, 'w')\n", "#for element in L:\n", "# file.write(str(element)+\"\\n\")\n", "#file.close()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "filename = \"thetaSPOTL_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in L:\n", " file.write(str(element)+\"\\n\")\n", "file.close()\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "filename = \"thetaSPOTL_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist = []\n", "for line in fileread:\n", " readlist.append(float(line[:-1]))\n", "fileread.close()\n", "readlist = [i*10**8 for i in readlist]\n", "\n", "fontsize = 24\n", "plt.figure(figsize=(12,9))\n", "plt.plot(theta,readlist,label=\"SPOTL\")\n", "plt.xlabel(r'Angle $\\theta$ (rad)', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper left',prop={'size': fontsize})\n", "plt.text(-0.075,1.65, \"$x 10^{‒8}$\", size = fontsize)\n", "plt.xticks(size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "plt.savefig(\"thetaSPOTL.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Plot secret key rate as a function of theta for a fixed distance and optimised over cut-off for the SPADS setup\n", "fixed_dist = 12.5\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "IntTimeRange = np.arange(5,35,5)\n", "M = []\n", "table=[]\n", "\n", "for x in theta:\n", " for z in IntTimeRange:\n", " table.append(QR25RateoptCutoff(x,2*fixed_dist*L0/3,fixed_dist*L0/3,z,5,300))\n", " rate = max(table)\n", " M.append(rate)\n", " table=[]\n", "\n", "filename = \"thetaSPADS.txt\"\n", "file = open(filename, 'w')\n", "for element in M:\n", " file.write(str(element)+\"\\n\")\n", "file.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fontsize = 24\n", "filename = \"thetaSPADS.txt\"\n", "fileread = open(filename, 'r')\n", "readlist = []\n", "for line in fileread:\n", " readlist.append(float(line[:-1]))\n", "fileread.close()\n", "readlist = [i*10**7 for i in readlist]\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(theta,readlist,label=\"SPADS\",color = 'red')\n", "plt.xlabel(r'Angle $\\theta$ (rad)', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper left',prop={'size': fontsize})\n", "plt.text(-0.075,3.2, \"$x 10^{‒7}$\", size = fontsize)\n", "plt.xticks(size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "\n", "plt.savefig(\"thetaSPADS.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "#Plot secret key rate as a function of theta for a fixed distance and optimised over cut-off for the single-photon scheme\n", "fixed_dist = 12.5\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "N = []\n", "table=[]\n", "IntTimeRange = np.arange(5,35,5)\n", "for x in theta:\n", " for z in IntTimeRange:\n", " table.append(QR2Rate(x,fixed_dist*L0,z))\n", " rate = max(table)\n", " N.append(rate)\n", " table=[]\n", "\n", "filename = \"single-photon.txt\"\n", "file = open(filename, 'w')\n", "for element in N:\n", " file.write(str(element)+\"\\n\")\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fixed_dist = 12.5\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "filename = \"single-photon.txt\"\n", "fileread = open(filename, 'r')\n", "readlist = []\n", "for line in fileread:\n", " readlist.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(theta,[i*1e6 for i in readlist],label=\"Single-photon\", color = 'green')\n", "plt.xlabel(r'Angle $\\theta$ (rad)', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper left',prop={'size': fontsize})\n", "plt.xticks(size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "ax.yaxis.set_major_formatter(mlt.FormatStrFormatter(\"%.1f\"))\n", "#uu = u.decode(u\"\\u2013\")\n", "plt.text(-0.07,6.75, \"$x 10^{‒6}$\", size = fontsize)\n", "plt.savefig(\"thetasingle-photon.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#Secret Key as a function of the cutoff for fixed distance for the SisQuaRe setup and SPADS setup\n", "fixed_dist = 12.5\n", "cutoff = np.arange(5,1000,2)\n", "IntTimeRange = np.arange(5,35,5)\n", "L = []\n", "M = []\n", "N = []\n", "Q = []\n", "R = []\n", "T = []\n", "S = []\n", "U = []\n", "V = []\n", "tableQR1=[]\n", "tableQR25=[]\n", "for x in cutoff:\n", " for z in IntTimeRange:\n", " tableQR1.append(QR1SecretKeyRateSum(fixed_dist*L0/2,fixed_dist*L0/2,z,x))\n", " tableQR25.append(QR25RateoptTheta(2*fixed_dist*L0/3,fixed_dist*L0/3,z,x))\n", " rateQR1=max(tableQR1)\n", " rateQR25=max(tableQR25)\n", " R.append(rateQR1)\n", " T.append(rateQR25)\n", " tableQR1=[]\n", " tableQR25=[]\n", "\n", "filename = \"cutoffSiSQuaRe.txt\"\n", "file = open(filename, 'w')\n", "for element in R:\n", " file.write(str(element)+\"\\n\")\n", "file.close()\n", "\n", "filename = \"cutoffSPADS.txt\"\n", "file = open(filename, 'w')\n", "for element in T:\n", " file.write(str(element)+\"\\n\")\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fontsize = 24\n", "cutoff = np.arange(5,1000,2)\n", "filename = \"cutoffSiSQuaRe.txt\"\n", "fileread = open(filename, 'r')\n", "readlistSiSQuaRe = []\n", "for line in fileread:\n", " readlistSiSQuaRe.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"cutoffSPADS.txt\"\n", "fileread = open(filename, 'r')\n", "readlistSPADS = []\n", "for line in fileread:\n", " readlistSPADS.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(cutoff, [i*1e7 for i in readlistSiSQuaRe], label=\"SiSQuaRe\", color = 'orange')\n", "plt.plot(cutoff, [i*1e7 for i in readlistSPADS], label=\"SPADS\", color = 'red')\n", "plt.xlabel('Cutoff $n^*$', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper right',prop={'size': fontsize})\n", "plt.xticks(size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "ax.yaxis.set_major_formatter(mlt.FormatStrFormatter(\"%.1f\"))\n", "plt.text(-45,4.05, \"$x10^{-7}$\", size = fontsize)\n", "\n", "plt.savefig(\"cutoffSiSQuaReSPADS.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#Secret Key as a function of the cutoff for fixed distance for the SPOTL setup\n", "fixed_dist = 12.5\n", "cutoff = np.arange(5,200,2)\n", "IntTimeRange = np.arange(5,35,5)\n", "L = []\n", "M = []\n", "N = []\n", "Q = []\n", "R = []\n", "T = []\n", "S = []\n", "U = []\n", "V = []\n", "tableQR3=[]\n", "for x in cutoff:\n", " for z in IntTimeRange:\n", " tableQR3.append(QR3RateoptTheta(fixed_dist*L0/2,fixed_dist*L0/2,z,x))\n", " rateQR3=max(tableQR3)\n", " Q.append(rateQR3)\n", " tableQR3=[]\n", " \n", "filename = \"cutoffSPOTL_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in Q:\n", " file.write(str(element)+\"\\n\")\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fontsize = 24\n", "cutoff = np.arange(5,200,2)\n", "filename = \"cutoffSPOTL_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist = []\n", "for line in fileread:\n", " readlist.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "readlist = [i*10**8 for i in readlist]\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(cutoff, readlist, label=\"SPOTL\")\n", "plt.xlabel('Cutoff $n^*$', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper right',prop={'size': fontsize})\n", "plt.text(-0.075,1.65, \"$x 10^{‒8}$\", size = fontsize)\n", "plt.xticks(size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "plt.savefig(\"cutoffSPOTL.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", 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"1.0527868812377029e-10\n", "5.950000000000001\n", "1.467689299970215e-10\n", "6.050000000000001\n", "1.9311351489794986e-10\n", "6.15\n", "2.4317174482383397e-10\n", "6.250000000000001\n", "2.9644882863535595e-10\n", "6.3500000000000005\n", "3.51841585274816e-10\n", "6.450000000000001\n", "4.162746868839666e-10\n", "6.550000000000001\n", "4.855216380344755e-10\n", "6.65\n", "5.543479612185204e-10\n", "6.750000000000001\n", "6.200327560364597e-10\n", "6.8500000000000005\n", "6.814943934057257e-10\n", "6.950000000000001\n", "7.418570483069741e-10\n", "7.050000000000001\n", "7.959997508307032e-10\n", "7.15\n", "8.418059491587567e-10\n", "7.250000000000001\n", "8.767121681903396e-10\n", "7.3500000000000005\n", "9.00240585492236e-10\n", "7.450000000000001\n", "9.286325032087364e-10\n", "7.550000000000001\n", "9.53321729855824e-10\n", "7.65\n", "9.690245802337455e-10\n", "7.750000000000001\n", "9.731940155287566e-10\n", "7.8500000000000005\n", "9.626554909684118e-10\n", "7.950000000000001\n", "9.334426628265358e-10\n", "8.05\n", "8.874998389586033e-10\n", "8.15\n", "8.204019444059145e-10\n", "8.25\n", "7.39222183042086e-10\n", "8.350000000000001\n", "6.362583608062338e-10\n", "8.450000000000001\n", "5.519057401128841e-10\n", "8.55\n", "5.016082924152966e-10\n", "8.65\n", "4.410178789047028e-10\n", "8.75\n", "3.6762113218306957e-10\n", "8.850000000000001\n", "2.8415720563784046e-10\n", "8.950000000000001\n", "1.9568599832333567e-10\n", "9.05\n", "1.1283518605289463e-10\n", "9.15\n", "4.088285472909967e-11\n", "9.25\n", "1.1774034198783777e-12\n", "9.350000000000001\n", "0.0\n", "9.450000000000001\n", "0.0\n", "9.55\n", "0.0\n", "9.65\n", "0.0\n", "9.750000000000002\n", "0.0\n", "9.850000000000001\n", "0.0\n", "9.950000000000001\n", "0.0\n", "10.05\n", "0.0\n", "10.15\n", "0.0\n", "10.250000000000002\n", "0.0\n", "10.350000000000001\n", "0.0\n", "10.450000000000001\n", "0.0\n", "10.55\n", "0.0\n", "10.65\n", "0.0\n", "10.750000000000002\n", "0.0\n" ] } ], "source": [ "#Secret key rate as a function of the relative positioning of the repeater for the SPADS scheme (we optimise over theta and cutoff for each distance)\n", "fixed_dist1 = 5 # fixed distance for the plot, expressed in L0\n", "fixed_dist2 = 10 # fixed distance for the plot, expressed in L0\n", "fixed_dist3 = 15 # fixed distance for the plot, expressed in L0\n", "fixed_dist4 = 20 # fixed distance for the plot, expressed in L0\n", "fixed_dist5 = 25 # fixed distance for the plot, expressed in L0\n", "dist1 = np.arange(0.15,fixed_dist1*L0,0.1)\n", "dist2 = np.arange(0.15,fixed_dist2*L0,0.1)\n", "dist3 = np.arange(0.15,fixed_dist3*L0,0.1)\n", "dist4 = np.arange(0.15,fixed_dist4*L0,0.1)\n", "dist5 = np.arange(0.15,fixed_dist5*L0,0.1)\n", "\n", "thetalice = np.arange(1.25,1.5,0.01)\n", "nstar = np.arange(10,400,10)\n", "IntTimeRange = np.arange(5,35,5)\n", "\n", "L = []\n", "M = []\n", "N = []\n", "Q = []\n", "R = []\n", "table = []\n", "\n", "\n", "for z in dist1:\n", " print(z)\n", " for y in thetalice:\n", " for w in nstar:\n", " for x in IntTimeRange:\n", " table.append(QR25Rate(y,z,fixed_dist1*L0-z,x,w))\n", " rate = max(table)\n", " print(rate)\n", " L.append(rate) \n", " table=[]\n", " \n", "for z in dist2:\n", " print(z)\n", " for y in thetalice:\n", " for w in nstar:\n", " for x in IntTimeRange:\n", " table.append(QR25Rate(y,z,fixed_dist2*L0-z,x,w))\n", " rate = max(table)\n", " print(rate)\n", " M.append(rate) \n", " table=[]\n", " \n", "for z in dist3:\n", " print(z)\n", " for y in thetalice:\n", " for w in nstar:\n", " for x in IntTimeRange:\n", " table.append(QR25Rate(y,z,fixed_dist3*L0-z,x,w))\n", " rate = max(table)\n", " print(rate)\n", " N.append(rate) \n", " table=[]\n", " \n", "for z in dist4:\n", " print(z)\n", " for y in thetalice:\n", " for w in nstar:\n", " for x in IntTimeRange:\n", " table.append(QR25Rate(y,z,fixed_dist4*L0-z,x,w))\n", " rate = max(table)\n", " print(rate)\n", " Q.append(rate) \n", " table=[]\n", " \n", "# for z in dist5:\n", "# print(z)\n", "# for y in thetalice:\n", "# for w in nstar:\n", "# for x in IntTimeRange:\n", "# table.append(QR25Rate(y,z,fixed_dist5*L0-z,x,w))\n", "# rate = max(table)\n", "# print(rate)\n", "# R.append(rate) \n", "# table=[]\n", "\n", "filename = \"positioningSPADS1.txt\"\n", "file = open(filename, 'w')\n", "for element in L:\n", " file.write(str(element)+\"\\n\")\n", "file.close()\n", "\n", "filename = \"positioningSPADS2.txt\"\n", "file = open(filename, 'w')\n", "for element in M:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"positioningSPADS3.txt\"\n", "file = open(filename, 'w')\n", "for element in N:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"positioningSPADS4.txt\"\n", "file = open(filename, 'w')\n", "for element in Q:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "# filename = \"positioningSPADS5.txt\"\n", "# file = open(filename, 'w')\n", "# for element in R:\n", "# file.write(str(element)+\"\\n\")\n", "# file.close() " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fixed_dist1 = 5 # fixed distance for the plot, expressed in L0\n", "fixed_dist2 = 10 # fixed distance for the plot, expressed in L0\n", "fixed_dist3 = 15 # fixed distance for the plot, expressed in L0\n", "fixed_dist4 = 20 # fixed distance for the plot, expressed in L0\n", "fixed_dist5 = 25 # fixed distance for the plot, expressed in L0\n", "dist1 = np.arange(0.15,fixed_dist1*L0,0.1)\n", "dist2 = np.arange(0.15,fixed_dist2*L0,0.1)\n", "dist3 = np.arange(0.15,fixed_dist3*L0,0.1)\n", "dist4 = np.arange(0.15,fixed_dist4*L0,0.1)\n", "dist5 = np.arange(0.15,fixed_dist5*L0,0.1)\n", "\n", "filename = \"positioningSPADS1.txt\"\n", "fileread = open(filename, 'r')\n", "readlist1 = []\n", "for line in fileread:\n", " readlist1.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPADS2.txt\"\n", "fileread = open(filename, 'r')\n", "readlist2 = []\n", "for line in fileread:\n", " readlist2.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPADS3.txt\"\n", "fileread = open(filename, 'r')\n", "readlist3 = []\n", "for line in fileread:\n", " readlist3.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPADS4.txt\"\n", "fileread = open(filename, 'r')\n", "readlist4 = []\n", "for line in fileread:\n", " readlist4.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "# filename = \"positioningSPADS5.txt\"\n", "# fileread = open(filename, 'r')\n", "# readlist5 = []\n", "# for line in fileread:\n", "# readlist5.append(float(line[:-1]))\n", "# fileread.close()\n", "\n", "\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(dist1/(fixed_dist1*L0),readlist1,label=\"5$L_0$\")\n", "plt.plot(dist2/(fixed_dist2*L0),readlist2,label=\"10$L_0$\",marker = 'o',markevery=7,markersize = 7.5)\n", "plt.plot(dist3/(fixed_dist3*L0),readlist3,label=\"15$L_0$\",marker = '*',markevery=7,markersize = 7.5)\n", "plt.plot(dist4/(fixed_dist4*L0),readlist4,label=\"20$L_0$\",marker = 'v',markevery=7,markersize = 7.5)\n", "#plt.plot(dist5/(fixed_dist5*L0),readlist5,label=\"25$L_0$\")\n", "plt.axvline(x=2/3, ls = '--', color = 'black')\n", "plt.xlabel('Relative positioning of the repeater', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='lower left',prop={'size': fontsize})\n", "plt.yscale('log')\n", "plt.xticks(np.arange(0,1.1,0.1), size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "\n", "plt.savefig(\"positioningSPADS.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.15\n", "8.160180277331635e-07\n", "0.25\n", "8.321864115361247e-07\n", "0.35\n", "8.471735431066859e-07\n", "0.45000000000000007\n", "8.60990022333793e-07\n", "0.55\n", "8.736501458609592e-07\n", "0.65\n", 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distance for the plot, expressed in L0\n", "fixed_dist4 = 20 # fixed distance for the plot, expressed in L0\n", "dist1 = np.arange(0.15,fixed_dist1*L0,0.1)\n", "dist2 = np.arange(0.15,fixed_dist2*L0,0.1)\n", "dist3 = np.arange(0.15,fixed_dist3*L0,0.1)\n", "dist4 = np.arange(0.15,fixed_dist4*L0,0.1)\n", "\n", "thetalice = np.arange(1.25,1.5,0.01)\n", "thetabob = np.arange(1.25,1.5,0.01)\n", "nstar = np.arange(10,400,10)\n", "IntTimeRange = np.arange(5,35,5)\n", "\n", "L = []\n", "M = []\n", "N = []\n", "Q = []\n", "table = []\n", "\n", "for z in dist1:\n", " print(z)\n", " for y in thetalice:\n", " for x in thetabob:\n", " for w in nstar:\n", " for t in IntTimeRange:\n", " table.append(QR3Rate(y,x,z,fixed_dist1*L0-z,t,w))\n", " rate = max(table)\n", " print(rate)\n", " L.append(rate) \n", " table=[]\n", " \n", "for z in dist2:\n", " print(z)\n", " for y in thetalice:\n", " for x in thetabob:\n", " for w in nstar:\n", " for t in IntTimeRange:\n", " table.append(QR3Rate(y,x,z,fixed_dist2*L0-z,t,w))\n", " rate = max(table)\n", " print(rate)\n", " M.append(rate) \n", " table=[]\n", " \n", "for z in dist3:\n", " print(z)\n", " for y in thetalice:\n", " for x in thetabob:\n", " for w in nstar:\n", " for t in IntTimeRange:\n", " table.append(QR3Rate(y,x,z,fixed_dist3*L0-z,t,w))\n", " rate = max(table)\n", " print(rate)\n", " N.append(rate) \n", " table=[]\n", " \n", "for z in dist4:\n", " print(z)\n", " for y in thetalice:\n", " for x in thetabob:\n", " for w in nstar:\n", " for t in IntTimeRange:\n", " table.append(QR3Rate(y,x,z,fixed_dist4*L0-z,t,w))\n", " rate = max(table)\n", " print(rate)\n", " Q.append(rate) \n", " table=[]\n", "\n", "filename = \"positioningSPOTL1_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in L:\n", " file.write(str(element)+\"\\n\")\n", "file.close()\n", "\n", "filename = \"positioningSPOTL2_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in M:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"positioningSPOTL3_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in N:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"positioningSPOTL4_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in Q:\n", " file.write(str(element)+\"\\n\")\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fontsize = 24\n", "fixed_dist1 = 5 # fixed distance for the plot, expressed in L0\n", "fixed_dist2 = 10 # fixed distance for the plot, expressed in L0\n", "fixed_dist3 = 15 # fixed distance for the plot, expressed in L0\n", "fixed_dist4 = 20 # fixed distance for the plot, expressed in L0\n", "dist1 = np.arange(0.15,fixed_dist1*L0,0.1)\n", "dist2 = np.arange(0.15,fixed_dist2*L0,0.1)\n", "dist3 = np.arange(0.15,fixed_dist3*L0,0.1)\n", "dist4 = np.arange(0.15,fixed_dist4*L0,0.1)\n", "\n", "filename = \"positioningSPOTL1_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist1 = []\n", "for line in fileread:\n", " readlist1.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPOTL2_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist2 = []\n", "for line in fileread:\n", " readlist2.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPOTL3_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist3 = []\n", "for line in fileread:\n", " readlist3.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"positioningSPOTL4_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist4 = []\n", "for line in fileread:\n", " readlist4.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "plt.figure(figsize=(12,9)) \n", "plt.plot(dist1/(fixed_dist1*L0),readlist1,label=\"5$L_0$\")\n", "plt.plot(dist2/(fixed_dist2*L0),readlist2,label=\"10$L_0$\",marker = 'o',markevery=7,markersize = 7.5)\n", "plt.plot(dist3/(fixed_dist3*L0),readlist3,label=\"15$L_0$\",marker = '*',markevery=7,markersize = 7.5)\n", "plt.plot(dist4/(fixed_dist4*L0),readlist4,label=\"20$L_0$\",marker = 'v',markevery=7,markersize = 7.5)\n", "plt.axvline(x=1/2, ls = '--', color = 'black')\n", "plt.xlabel('Relative positioning of the repeater', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='lower left',prop={'size': fontsize})\n", "plt.yscale('log')\n", "plt.xticks(np.arange(0,1,0.1), size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "\n", "plt.savefig(\"positioningSPOTL.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1\n", "0.2\n", "0.30000000000000004\n", "0.4\n", "0.5\n", "0.6\n", "0.7000000000000001\n", "0.8\n", "0.9\n", "1.0\n", "1.1\n", "1.2000000000000002\n", "1.3000000000000003\n", "1.4000000000000001\n", "1.5000000000000002\n", "1.6\n", "1.7000000000000002\n", "1.8000000000000003\n", "1.9000000000000001\n", "2.0\n", "2.1\n", "2.2\n", "2.3000000000000003\n", "2.4000000000000004\n", "2.5000000000000004\n", "2.6\n", "2.7\n", "2.8000000000000003\n", "2.9000000000000004\n", "3.0000000000000004\n", "3.1\n", "3.2\n", "3.3000000000000003\n", "3.4000000000000004\n", "3.5000000000000004\n", "3.6\n", "3.7\n", "3.8000000000000003\n", "3.9000000000000004\n", "4.0\n", "4.1\n", "4.2\n", "4.3\n", "4.3999999999999995\n", "4.5\n", "4.6\n", "4.7\n", "4.8\n", "4.9\n", "5.0\n", "5.1\n", "5.2\n", "5.3\n", "5.4\n", "5.5\n", "5.6\n", "5.7\n", "5.8\n", "5.9\n", "6.0\n", "6.1\n", "6.2\n", "6.3\n", "6.4\n", "6.5\n", "6.6\n", "6.7\n", "6.8\n", "6.9\n", "7.0\n", "7.1\n", "7.2\n", "7.3\n", "7.4\n", "7.5\n", "7.6\n", "7.7\n", "7.8\n", "7.9\n", "8.0\n", "8.1\n", "8.2\n", "8.3\n", "8.4\n", "8.5\n", "8.6\n", "8.7\n", "8.8\n", "8.9\n", "9.0\n", "9.1\n", "9.2\n", "9.3\n", "9.4\n", "9.5\n", "9.6\n", "9.700000000000001\n", "9.8\n", "9.9\n", "10.0\n", "10.1\n", "10.200000000000001\n", "10.3\n", "10.4\n", "10.5\n", "10.6\n", "10.700000000000001\n", "10.8\n", "10.9\n", "11.0\n", "11.1\n", "11.200000000000001\n", "11.3\n", "11.4\n", "11.5\n", "11.6\n", "11.700000000000001\n", "11.8\n", "11.9\n", "12.0\n", "12.1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in double_scalars\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "12.200000000000001\n", "12.3\n", "12.4\n", "12.5\n", "12.6\n", "12.700000000000001\n", "12.8\n", "12.9\n", "13.0\n", "13.1\n", "13.200000000000001\n", "13.3\n", "13.4\n", "13.5\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Relative difference between secret-keys obtained when optimising over two different angles or only one angle for the SPOTL setup (we use middle positioning of the repeater and optimise over cutoff for each distance)\n", "# We use this to justify thetalice=thetabob\n", "fixed_dist = 25 # fixed distance for the plot, expressed in L0\n", "dist = np.arange(0.1,fixed_dist*L0,0.1)\n", "\n", "thetalice = np.arange(1.44,1.47,0.005)\n", "thetabob = np.arange(1.44,1.47,0.005)\n", "nstar = np.arange(10,400,10)\n", "IntTimeRange = np.arange(5,35,5)\n", "\n", "L = []\n", "M = []\n", "N = []\n", "Q = []\n", "R = []\n", "T = []\n", "S = []\n", "U = []\n", "V = []\n", "table_separate = []\n", "table_same=[]\n", "\n", "for z in dist:\n", " print(z)\n", " for w in nstar:\n", " for q in IntTimeRange:\n", " for y in thetalice:\n", " for x in thetabob:\n", " table_separate.append(QR3Rate(y,x,z/2,z/2,q,w))\n", " table_same.append(QR3Rate(y,y,z/2,z/2,q,w))\n", " rate_separate = max(table_separate)\n", " rate_same = max(table_same)\n", " rel_error = (rate_separate - rate_same)/rate_separate\n", " L.append(rel_error) \n", " table_separate=[]\n", " table_same = []\n", " \n", " \n", "plt.figure(figsize=(15,9)) \n", "plt.plot(dist,L,label=\"3 nodes, total distance 5*L0\")\n", "plt.xlabel('Distance (L0 unit)')\n", "plt.ylabel('relative error')\n", "plt.legend(loc='upper right')\n", "#plt.yscale('log')\n", "plt.xticks(np.arange(0,fixed_dist*L0,L0),np.arange(fixed_dist+1))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.05\n", "0.1\n", "0.15000000000000002\n", "0.2\n", "0.25\n", "0.3\n", "0.35000000000000003\n", "0.4\n", "0.45\n", "0.5\n", "0.55\n", "0.6000000000000001\n", "0.6500000000000001\n", "0.7000000000000001\n", "0.7500000000000001\n", "0.8\n", "0.8500000000000001\n", "0.9000000000000001\n", "0.9500000000000001\n", "1.0\n", "1.05\n", "1.1\n", "1.1500000000000001\n", "1.2000000000000002\n", "1.2500000000000002\n", "1.3\n", "1.35\n", "1.4000000000000001\n", "1.4500000000000002\n", "1.5000000000000002\n", "1.55\n", "1.6\n", "1.6500000000000001\n", "1.7000000000000002\n", "1.7500000000000002\n", "1.8\n", "1.85\n", "1.9000000000000001\n", "1.9500000000000002\n", "2.0\n", "2.05\n", "2.1\n", "2.15\n", "2.1999999999999997\n", "2.25\n", "2.3\n", "2.35\n", "2.4\n", "2.45\n", "2.5\n", "2.55\n", "2.6\n", "2.65\n", "2.7\n", "2.75\n", "2.8\n", "2.85\n", "2.9\n", "2.95\n", "3.0\n", "3.05\n", "3.1\n", "3.15\n", "3.2\n", "3.25\n", "3.3\n", "3.35\n", "3.4\n", "3.45\n", "3.5\n", "3.55\n", "3.6\n", "3.65\n", "3.7\n", "3.75\n", "3.8\n", "3.85\n", "3.9\n", "3.95\n", "4.0\n", "4.05\n", "4.1\n", "4.15\n", "4.2\n", "4.25\n", "4.3\n", "4.35\n", "4.4\n", "4.45\n", "4.5\n", "4.55\n", "4.6\n", "4.65\n", "4.7\n", "4.75\n", "4.8\n", "4.8500000000000005\n", "4.9\n", "4.95\n", "5.0\n", "5.05\n", "5.1000000000000005\n", "5.15\n", "5.2\n", "5.25\n", "5.3\n", "5.3500000000000005\n", "5.4\n", "5.45\n", "5.5\n", "5.55\n", "5.6000000000000005\n", "5.65\n", "5.7\n", "5.75\n", "5.8\n", "5.8500000000000005\n", "5.9\n", "5.95\n", "6.0\n", "6.05\n", "6.1000000000000005\n", "6.15\n", "6.2\n", "6.25\n", "6.3\n", "6.3500000000000005\n", "6.4\n", "6.45\n", "6.5\n", "6.55\n", "6.6000000000000005\n", "6.65\n", "6.7\n", "6.75\n", "6.8\n", "6.8500000000000005\n", "6.9\n", "6.95\n", "7.0\n", "7.05\n", "7.1000000000000005\n", "7.15\n", "7.2\n", "7.25\n", "7.3\n", "7.3500000000000005\n", "7.4\n", "7.45\n", "7.5\n", "7.55\n", "7.6000000000000005\n", "7.65\n", "7.7\n", "7.75\n", "7.8\n", "7.8500000000000005\n", "7.9\n", "7.95\n", "8.0\n", "8.05\n", "8.100000000000001\n", "8.15\n", "8.200000000000001\n", "8.250000000000002\n", "8.3\n", "8.350000000000001\n", "8.4\n", "8.450000000000001\n", "8.500000000000002\n", "8.55\n", "8.600000000000001\n", "8.65\n", "8.700000000000001\n", "8.750000000000002\n", "8.8\n", "8.850000000000001\n", "8.9\n", "8.950000000000001\n", "9.000000000000002\n", "9.05\n", "9.100000000000001\n", "9.15\n", "9.200000000000001\n", "9.250000000000002\n", "9.3\n", "9.350000000000001\n", "9.4\n", "9.450000000000001\n", "9.500000000000002\n", "9.55\n", "9.600000000000001\n", "9.650000000000002\n", "9.700000000000001\n", "9.750000000000002\n", "9.8\n", "9.850000000000001\n", "9.900000000000002\n", "9.950000000000001\n", "10.000000000000002\n", "10.05\n", "10.100000000000001\n", "10.150000000000002\n", "10.200000000000001\n", "10.250000000000002\n", "10.3\n", "10.350000000000001\n", "10.400000000000002\n", "10.450000000000001\n", "10.500000000000002\n", "10.55\n", "10.600000000000001\n", "10.650000000000002\n", "10.700000000000001\n", "10.750000000000002\n", "10.8\n", "10.850000000000001\n", "10.900000000000002\n", "10.950000000000001\n", "11.000000000000002\n", "11.05\n", "11.100000000000001\n", "11.150000000000002\n", "11.200000000000001\n", "11.250000000000002\n", "11.3\n", "11.350000000000001\n", "11.400000000000002\n", "11.450000000000001\n", "11.500000000000002\n", "11.55\n", "11.600000000000001\n", "11.650000000000002\n", "11.700000000000001\n", "11.750000000000002\n", "11.8\n", "11.850000000000001\n", "11.900000000000002\n", "11.950000000000001\n", "12.000000000000002\n", "12.05\n", "12.100000000000001\n", "12.150000000000002\n", "12.200000000000001\n", "12.250000000000002\n", "12.3\n", "12.350000000000001\n", "12.400000000000002\n", "12.450000000000001\n", "12.500000000000002\n", "12.55\n", "12.600000000000001\n", "12.650000000000002\n", "12.700000000000001\n", "12.750000000000002\n", "12.8\n", "12.850000000000001\n", "12.900000000000002\n", "12.950000000000001\n", "13.000000000000002\n", "13.05\n", "13.100000000000001\n", "13.150000000000002\n", "13.200000000000001\n", "13.250000000000002\n", "13.3\n", "13.350000000000001\n", "13.400000000000002\n", "13.450000000000001\n", "13.500000000000002\n", "13.55\n", "13.600000000000001\n", "13.650000000000002\n", "13.700000000000001\n", "13.750000000000002\n", "13.8\n", "13.850000000000001\n", "13.900000000000002\n", "13.950000000000001\n", "14.000000000000002\n", "14.05\n", "14.100000000000001\n", "14.150000000000002\n", "14.200000000000001\n", "14.250000000000002\n", "14.3\n", "14.350000000000001\n", "14.400000000000002\n", "14.450000000000001\n", "14.500000000000002\n", "14.55\n", "14.600000000000001\n", "14.650000000000002\n", "14.700000000000001\n", "14.750000000000002\n", "14.8\n", "14.850000000000001\n", "14.900000000000002\n", "14.950000000000001\n", "15.000000000000002\n", "15.05\n", "15.100000000000001\n", "15.150000000000002\n", "15.200000000000001\n", "15.250000000000002\n", "15.3\n", "15.350000000000001\n", "15.400000000000002\n", "15.450000000000001\n", "15.500000000000002\n", "15.55\n", "15.600000000000001\n", "15.650000000000002\n", "15.700000000000001\n", "15.750000000000002\n", "15.8\n", "15.850000000000001\n", "15.900000000000002\n", "15.950000000000001\n", "16.0\n", "16.05\n", "16.1\n", "16.150000000000002\n", "16.200000000000003\n", "16.25\n" ] } ], "source": [ "#Main plot: Secret key rate vs distance. On this plot we optimise over everything.\n", "max_dist = 30 # max distance for the plot, expressed in L0.\n", "dist = np.arange(0.05,max_dist*L0,0.05)\n", "L = []\n", "M = []\n", "N = []\n", "V = []\n", "tableQR1 = []\n", "tableQR2 =[]\n", "tableQR3 = []\n", "#variablesQR3=[]\n", "#optimalsQR3 =[]\n", "tableQR25 = []\n", "#variablesQR25=[]\n", "#optimalsQR25 =[]\n", "thermal_benchmark = []\n", "thetaQR3 = np.arange(1.25,1.5,0.01)\n", "nstarQR3 = np.arange(5,400,5)\n", "thetaQR25 = np.arange(1.25,1.5,0.01)\n", "nstarQR25 = np.arange(5,400,5)\n", "IntTimeRange = np.arange(5,35,5)\n", "for x in dist:\n", " print(x)\n", " for y in thetaQR3:\n", " for w in nstarQR3:\n", " for z in IntTimeRange:\n", " tableQR3.append(QR3Rate(y,y,x/2,x/2,z,w))\n", " #variablesQR3.append({y,w})\n", " #print({y,z,w})\n", " #print(y)\n", " #print(z)\n", " rateQR3 = max(tableQR3)\n", " #optimalsQR3.append(variablesQR3[tableQR3.index(max(tableQR3))])\n", " tableQR3=[]\n", " #variablesQR3=[]\n", " L.append(rateQR3)\n", " \n", " \n", " for y in thetaQR25:\n", " for w in nstarQR25:\n", " for z in IntTimeRange:\n", " tableQR25.append(QR25Rate(y,2*x/3,x/3,z,w))\n", " #variablesQR25.append({y,w})\n", " #print({y,z,w})\n", " #print(y)\n", " #print(z)\n", " rateQR25 = max(tableQR25)\n", " #optimalsQR25.append(variablesQR25[tableQR25.index(max(tableQR25))])\n", " tableQR25=[]\n", " #variablesQR25=[]\n", " V.append(rateQR25)\n", " \n", " \n", " for z in IntTimeRange:\n", " tableQR2.append(QR2RateoptTheta(x,z))\n", " rateQR2 = max(tableQR2)\n", " tableQR2=[]\n", " M.append(rateQR2)\n", " \n", " for z in IntTimeRange: \n", " tableQR1.append(QR1RateoptCutoff(x/2,x/2,z, 5, 800))\n", " rateQR1 = max(tableQR1)\n", " tableQR1=[]\n", " N.append(rateQR1)\n", " \n", "filename = \"RateVSdistSPOTL_correct.txt\"\n", "file = open(filename, 'w')\n", "for element in L:\n", " file.write(str(element)+\"\\n\")\n", "file.close()\n", "\n", "filename = \"RateVSdistSPADS.txt\"\n", "file = open(filename, 'w')\n", "for element in V:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"RateVSdistsingle-photon.txt\"\n", "file = open(filename, 'w')\n", "for element in M:\n", " file.write(str(element)+\"\\n\")\n", "file.close() \n", "\n", "filename = \"RateVSdistSiSQuaRe.txt\"\n", "file = open(filename, 'w')\n", "for element in N:\n", " file.write(str(element)+\"\\n\")\n", "file.close() " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fontsize = 20\n", "max_dist = 30\n", "dist = np.arange(0.05,max_dist*L0,0.05)\n", "filename = \"RateVSdistSPOTL_correct.txt\"\n", "fileread = open(filename, 'r')\n", "readlist1 = []\n", "for line in fileread:\n", " readlist1.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"RateVSdistSPADS.txt\"\n", "fileread = open(filename, 'r')\n", "readlist2 = []\n", "for line in fileread:\n", " readlist2.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"RateVSdistsingle-photon.txt\"\n", "fileread = open(filename, 'r')\n", "readlist3 = []\n", "for line in fileread:\n", " readlist3.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "filename = \"RateVSdistSiSQuaRe.txt\"\n", "fileread = open(filename, 'r')\n", "readlist4 = []\n", "for line in fileread:\n", " readlist4.append(float(line[:-1]))\n", "fileread.close()\n", "\n", "Q = []\n", "S = []\n", "#R = []\n", "#T = []\n", "#U = []\n", "thermal_benchmark = []\n", "\n", "for x in dist:\n", " value2 = eta(x)\n", " #value3 = np.sqrt(value2)\n", " #value4 = np.sqrt(value3)\n", "\n", " value5 = -np.log2(1-value2)\n", " Q.append([value5])\n", " #value6 = -np.log2(1-value3)\n", " #R.append([value6])\n", " #value7 = -np.log2(1-value4)\n", " #T.append([value7])\n", "\n", " S.append(-np.log2(1-papp*eta(x)))\n", " #U.append(-np.log2(1-papp*eta(x/2)))\n", " thermal_benchmark.append( thermal_channel_benchmark(papp*eta(x), nbar = DCperSec*5* 1e-9 )) # 10e-9 is to convert from ns to s\n", "\n", "plt.figure(figsize=(15,9))\n", "plt.plot(dist,readlist4,label=\"SiSQuaRe\", color=\"orange\",marker = 'P',markevery=10,markersize = 7.5)\n", "plt.plot(dist,readlist3,label=\"Single-photon\", color=\"green\",marker = 'o',markevery=10,markersize = 7.5)\n", "plt.plot(dist,readlist2,label=\"SPADS\", color=\"red\",marker = '*',markevery=10,markersize = 7.5)\n", "plt.plot(dist,readlist1,label=\"SPOTL\",marker = 'v',markevery=10,markersize = 7.5)\n", "plt.plot(dist, Q, label=\"Secret-key capacity\",color='k')\n", "plt.plot(dist, S, label=\"Secret-key capacity (Ext.)\",color='k', linestyle='dotted')\n", "plt.plot(dist, thermal_benchmark, label=\"Thermal benchmark\", color='k', linestyle='dashed')\n", "plt.xlabel('Distance ($L_0$ unit)', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper right',prop={'size': fontsize})\n", "plt.yscale('log')\n", "plt.xticks(np.arange(0,max_dist*L0,2*L0),2*np.arange(max_dist+1), size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "plt.savefig(\"rateVSdist.pdf\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2\n", "0.30000000000000004\n", "0.4000000000000001\n", "0.5000000000000001\n", "0.6000000000000001\n", "0.7000000000000002\n", "0.8000000000000003\n", "0.9000000000000001\n", "1.0000000000000002\n", "1.1000000000000003\n", "1.2000000000000004\n", "1.3000000000000003\n", "1.4000000000000004\n", "1.5000000000000004\n", "1.6000000000000003\n", "1.7000000000000004\n", "1.8000000000000005\n", "1.9000000000000006\n", "2.000000000000001\n", "2.1000000000000005\n", "2.200000000000001\n", "2.3000000000000007\n", "2.400000000000001\n", "2.500000000000001\n", "2.600000000000001\n", "2.700000000000001\n", "2.800000000000001\n", "2.9000000000000012\n", "3.000000000000001\n", "3.100000000000001\n", "3.200000000000001\n", "3.300000000000001\n", "3.4000000000000012\n", "3.5000000000000013\n", "3.6000000000000014\n", "3.7000000000000015\n", "3.8000000000000016\n", "3.9000000000000012\n", "4.000000000000001\n", "4.100000000000001\n", "4.200000000000002\n", "4.300000000000002\n", "4.400000000000001\n", "4.500000000000002\n", "4.600000000000001\n", "4.700000000000002\n", "4.800000000000002\n", "4.900000000000002\n", "5.000000000000002\n", "5.100000000000001\n", "5.200000000000002\n", "5.300000000000002\n", "5.400000000000002\n", "5.500000000000002\n", "5.600000000000002\n", "5.700000000000002\n", "5.800000000000002\n", "5.900000000000002\n", "6.000000000000002\n", "6.100000000000002\n", "6.200000000000002\n", "6.3000000000000025\n", "6.400000000000002\n", "6.500000000000003\n", "6.600000000000002\n", "6.700000000000002\n", "6.8000000000000025\n", "6.900000000000002\n", "7.000000000000003\n", "7.100000000000002\n", "7.200000000000003\n", "7.3000000000000025\n", "7.400000000000003\n", "7.500000000000003\n", "7.600000000000002\n", "7.700000000000003\n", "7.8000000000000025\n", "7.900000000000003\n", "8.000000000000002\n", "8.100000000000003\n", "8.200000000000003\n", "8.300000000000002\n", "8.400000000000002\n", "8.500000000000002\n", "8.600000000000001\n", "8.700000000000003\n", "8.800000000000002\n", "8.900000000000002\n", "9.000000000000002\n", "9.100000000000001\n", "9.200000000000003\n", "9.300000000000002\n", "9.400000000000002\n", "9.500000000000002\n", "9.600000000000003\n", "9.700000000000003\n", "9.800000000000002\n", "9.900000000000002\n", "10.000000000000002\n", "10.100000000000003\n", "10.200000000000003\n", "10.300000000000002\n", "10.400000000000002\n", "10.500000000000004\n", "10.600000000000003\n", "10.700000000000003\n", "10.800000000000002\n", "10.900000000000002\n", "11.000000000000004\n", "11.100000000000003\n", "11.200000000000003\n", "11.300000000000002\n", "11.400000000000002\n", "11.500000000000004\n", "11.600000000000003\n", "11.700000000000003\n", "11.800000000000002\n", "11.900000000000004\n", "12.000000000000004\n", "12.100000000000003\n", "12.200000000000003\n", "12.300000000000002\n", "12.400000000000004\n", "12.500000000000004\n", "12.600000000000003\n", "12.700000000000003\n", "12.800000000000004\n", "12.900000000000004\n", "13.000000000000004\n", "13.100000000000003\n", "13.200000000000003\n", "13.300000000000004\n", "13.400000000000004\n", "13.500000000000004\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max_dist = 25 # max distance for the plot, expressed in L0. On this plot we plot the secret fraction for the SPADS setup that can be achieved with BB84, 6 state without and with adv dist, assumming cutoff of 1, IntTime of 5ns and optimising over the positioning of the repeater\n", "dist = np.arange(0.2,max_dist*L0,0.1)\n", "theta = np.arange(0.01,np.pi/2,0.01)\n", "#IntTimeRange = np.arange(5,35,5)\n", "L = []\n", "M = []\n", "N = []\n", "tableBB84 = []\n", "table6statesNoAdvDist = []\n", "table6states = []\n", "for x in dist:\n", " print(x)\n", " dist_rel = np.arange(0.1,x,0.1)\n", " for z in dist_rel:\n", " for y in theta:\n", " tableBB84.append(QR25secretKeyFracBB84(y,z,x-z,5,1))\n", " table6statesNoAdvDist.append(QR25secretKeyFrac6statesNoAdvDist(y,z,x-z,5,1))\n", " table6states.append(QR25secretKeyFrac6states(y,z,x-z,5,1))\n", " rateBB84 = max(tableBB84)\n", " rate6statesNoAdvDist = max(table6statesNoAdvDist)\n", " rate6states = max(table6states)\n", " L.append(rateBB84)\n", " M.append(rate6statesNoAdvDist)\n", " N.append(rate6states)\n", " tableBB84 = []\n", " table6statesNoAdvDist = []\n", " table6states = []\n", " \n", "plt.figure(figsize=(15,9)) \n", "plt.plot(dist,L,label=\"Secret-frac 2.5-node, BB84\")\n", "plt.plot(dist,M,label=\"Secret-frac 2.5-node, 6 states no adv dist\")\n", "plt.plot(dist,N,label=\"Secret-frac 2.5-node, 6 states with adv dist\")\n", "plt.xlabel('Distance (L0 unit)')\n", "plt.ylabel('Secret-key fraction')\n", "plt.legend(loc='upper right')\n", "plt.xticks(np.arange(0,max_dist*L0,L0),np.arange(max_dist+1))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.05\n", "0.1\n", "0.15000000000000002\n", "0.2\n", "0.25\n", "0.3\n", "0.35000000000000003\n", "0.4\n", "0.45\n", "0.5\n", "0\n" ] } ], "source": [ "#For the SPOTL setup no key can be generated without advantage distillation, below we see that for cutoff of 1, IntTime of 5 ns and total distance of L0, even if we optimise over the angles and position of the repeater, the secret-fraction is zero.\n", "fixed_dist1 = 1 # fixed distance for the plot, expressed in L0\n", "dist1 = np.arange(0.05,fixed_dist1*L0,0.05)\n", "thetalice = np.arange(0.01,np.pi/2,0.01)\n", "thetabob = np.arange(0.01,np.pi/2,0.01)\n", "\n", "table = []\n", "for z in dist1:\n", " print(z)\n", " for y in thetalice:\n", " for x in thetabob:\n", " table.append(QR3secretKeyFrac6statesNoAdvDist(y,x,z,fixed_dist1*L0-z,5,1))\n", "rate = max(table)\n", "print(rate)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fontsize = 24\n", "#The second main plot comparing SPADS to direct transmission using same devices:\n", "max_dist = 30 # max distance for the plot, expressed in L0. Main plot: Secret key rate vs distance. On this plot we optimise over everything.\n", "dist = np.arange(0.05,max_dist*L0,0.05)\n", "L = []\n", "Q = []\n", "S = []\n", "#R = []\n", "#T = []\n", "#U = []\n", "thermal_benchmark = []\n", "IntTimeRange = np.arange(5,35,5)\n", "thetaQR25 = np.arange(1.25,1.5,0.01)\n", "nstarQR25 = np.arange(5,400,5)\n", "tableDirect = []\n", "for x in dist:\n", " for w in IntTimeRange: \n", " tableDirect.append(DirectSecretKeyRate(x,w))\n", " rate = max(tableDirect)\n", " tableDirect=[]\n", " L.append(rate)\n", " \n", " value2 = eta(x)\n", " #value3 = np.sqrt(value2)\n", " #value4 = np.sqrt(value3)\n", " \n", " value5 = -np.log2(1-value2)\n", " Q.append([value5])\n", " #value6 = -np.log2(1-value3)\n", " #R.append([value6])\n", " #value7 = -np.log2(1-value4)\n", " #T.append([value7])\n", " \n", " S.append(-np.log2(1-papp*eta(x)))\n", " #U.append(-np.log2(1-papp*eta(x/2)))\n", " thermal_benchmark.append( thermal_channel_benchmark(papp*eta(x), nbar = DCperSec*5 * 1e-9 )) # 1e-9 is to convert from ns to s\n", "\n", "filename = \"RateVSdistSPADS.txt\"\n", "fileread = open(filename, 'r')\n", "readlist = []\n", "for line in fileread:\n", " readlist.append(float(line[:-1]))\n", "fileread.close() \n", " \n", "plt.figure(figsize=(12,9))\n", "plt.plot(dist,readlist,label=\"SPADS\",color='red',marker = '*',markevery=10,markersize = 7.5)\n", "plt.plot(dist,L,label=\"NV direct transmission\",color='olive',marker = 'D',markevery=10,markersize = 7.5)\n", "plt.plot(dist, Q, label=\"Secret-key capacity\",color='k')\n", "plt.plot(dist, S, label=\"Secret-key capacity (Ext.)\",color='k',linestyle='dotted')\n", "plt.plot(dist, thermal_benchmark, label=\"Thermal benchmark\", color='k', linestyle='dashed')\n", "plt.xlabel('Distance ($L_0$ unit)', size = fontsize)\n", "plt.ylabel('Secret-key rate', size = fontsize)\n", "plt.legend(loc='upper right',prop={'size': fontsize})\n", "plt.yscale('log')\n", "plt.xticks(np.arange(0,max_dist*L0,4*L0),4*np.arange(max_dist+1), size = fontsize)\n", "plt.yticks(size = fontsize)\n", "ax = plt.gca()\n", "ax.yaxis.offsetText.set_fontsize(fontsize)\n", "\n", "plt.savefig(\"RateVSdistSPADSDirect.pdf\")\n", "#plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }