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Discrete-phase-randomized measurement-device-independent quantum key distribution

Zhu Cao caozhu@ecust.edu.cn Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
Abstract

Measurement-device-independent quantum key distribution removes all detector-side attacks in quantum cryptography, and in the meantime doubles the secure distance. The source side, however, is still vulnerable to various attacks. In particular, the continuous phase randomization assumption on the source side is normally not fulfilled in experimental implementation and may potentially open a loophole. In this work, we first show that indeed there are loopholes for imperfect phase randomization in measurement-device-independent quantum key distribution by providing a concrete attack. Then we propose a discrete-phase-randomized measurement-device-independent quantum key distribution protocol as a solution to close this source-side loophole.

I Introduction

Quantum key distribution (QKD) provides an information-theoretically secure method to distribute identical keys between two parties, and is hence one of the most important ingredients in information-theoretically secure communication. The first QKD protocol was developed by Bennett and Brassard in 1984 Bennett and Brassard (1984), which consisted of two sides, a source side and a detector side. We refer to this protocol as BB84 hereafter. The security of BB84, however, relies on a few idealized assumptions. These assumptions are often violated in practice, which allows attacks mostly on the detector side. Measurement-device-independent (MDI) QKD is hence developed to close all loopholes on the detector side Lo et al. (2012). To achieve higher security, it is ideal to also close loopholes on the source side in MDI-QKD.

In an idealized MDI-QKD, each of the two parties, called Alice and Bob, provide single photons in the eigenstates of the rectilinear basis or the diagonal bases. The measurement device performs a Bell measurement on Alice’s and Bob’s signals. It can be shown that if both Alice and Bob choose the rectilinear basis, they can recover identical keys based on the Bell measurement outcomes. The events that Alice and Bob choose different bases are discarded. It was shown that the security of this protocol can be proved by treating the protocol as the time-reversed version of an entanglement-based QKD Ekert (1991).

In a practical scenario, single photon sources are not available. Instead, a phase-randomized weak coherent laser is often utilized to approximate a single photon source. However, continuous phase randomization is impossible to be realized experimentally. Heuristically, the laser is turned off and then on again to approximate phase randomization, but there is no theoretical guarantee that this can provide perfect phase randomization. Indeed there is evidence that this method is far from perfect phase randomization Xu et al. (2012).

Failure in phase randomization can yield the QKD system insecure with respect to the original security analysis. In a related work Cao et al. (2015), it was shown that the phase randomization loophole in BB84 can be closed by using discrete phase randomization. Inspired by that work, we propose a discrete-phase-randomized (DPR) MDI-QKD protocol for solving the phase randomization loophole in MDI-QKD. In addition, we provide a formal security proof of the DPR MDI-QKD protocol.

The roadmap for the rest of the paper is as follows. In Sec. II, we first provide a brief review of the MDI-QKD protocol. In Sec. III, we show an attack against a MDI-QKD system with imperfect phase randomization. In Sec. IV, we describe the DPR MDI-QKD protocol and provide its security analysis. In Sec. V, we summarize the results and discuss future work.

II Review of MDI-QKD

A diagram of the MDI-QKD protocol is shown in Fig. 1. In a typical MDI-QKD setup, Alice and Bob prepare source states in the rectilinear basis or in the diagonal basis. A measurement device which may be controlled by Eve performs a joint Bell measurement on Alice’s and Bob’s states, and outputs either |01+|10\mbox{$\left|01\right\rangle$}+\mbox{$\left|10\right\rangle$} or |01|10\mbox{$\left|01\right\rangle$}-\mbox{$\left|10\right\rangle$} (other Bell measurement outcomes are discarded). Afterwards, Alice and Bob announce the bases they used and discard the events that they use different bases. It can be shown that if both Alice and Bob were using the rectilinear basis with different (the same) eigenstates, the measurement result is always |01|10\mbox{$\left|01\right\rangle$}-\mbox{$\left|10\right\rangle$} (|01+|10\mbox{$\left|01\right\rangle$}+\mbox{$\left|10\right\rangle$}). If both parties were using the diagonal bases, then outputting |01+|10\mbox{$\left|01\right\rangle$}+\mbox{$\left|10\right\rangle$} and |01|10\mbox{$\left|01\right\rangle$}-\mbox{$\left|10\right\rangle$} will have the same probability 0.5. By these properties, Alice and Bob can use the rectilinear basis to generate keys, and use the diagonal basis to estimate the errors in the measurement device. In addition, the two parties use the decoy state method Hwang (2003); Lo et al. (2005); Wang (2005) to estimate the channel gain and error rate with higher precision. The MDI-QKD protocol can be viewed as a time-reversed version of an entanglement-based QKD protocol Ekert (1991) and indeed its security can be proved using this time-reversal symmetry Lo et al. (2012).

Refer to caption
Figure 1: Diagram of the MDI-QKD protocol. There are three modules, including Alice, Bob and Eve. Alice and Bob generate source states, whereas the measurement device controlled by Eve performs a joint measurement on the states of Alice and Bob. Here, WCP stands for a weak coherent source; PM stands for a phase modulator; Decoy-IM stands for an intensity modulator that switches between the signal states and the decoy states. Conv is a converter that converts the phase encoding to the polarization encoding Tamaki et al. (2012); BS is a beam splitter; PBS is a polarization beam splitter; D1HD_{1H}, D1VD_{1V}, D2HD_{2H}, D2VD_{2V} are single-photon detectors.

III Vulnerability of imperfect phase randomization

In this section, we propose an attack to show that there is a serious loophole in MDI-QKD if the phase of the coherent source is not properly randomized. For simplicity, we consider the extreme case that there is no phase randomization, and the phases of the signal state and the decoy state are known to the eavesdropper Eve. We now describe how to use unambiguous state discrimination (USD) to attack a MDI-QKD system without phase randomization.

In the first step, Eve uses USD to distinguish the signal state and the decoy state on both Alice’s and Bob’s sides, each with some probability qq (note that in the case of perfect phase randomization, Eve cannot distinguish the signal state and the decoy state, i.e., q=0q=0). Eve discards the events that he fails to distinguish the signal state and the decoy state. Then Eve measures the photon number and chooses to forward some of the photons conditioned on the results of signal or decoy states and the photon number to preserve the channel statistics.

In a normal MDI-QKD, the key rate is lower bounded by

Rl=QRectf(ERect)H(ERect)+Qn1,1(1H(EDiag1,1,b)),R^{l}=-Q_{\textrm{Rect}}f(E_{\textrm{Rect}})H(E_{\textrm{Rect}})+Q^{1,1}_{n}(1-H(E^{1,1,b}_{\textrm{Diag}})), (1)

where the first term is the error correction term and the second term is the privacy amplification term. Here QRectQ_{\textrm{Rect}} is the gain of the rectilinear basis, ERectE_{\textrm{Rect}} is the bit error rate of the rectilinear basis, Qn1,1Q^{1,1}_{n} is the estimated gain when both parities emit single-photon states, f()1f(\cdot)\geq 1 is the error correction efficiency, EDiag1,1,bE^{1,1,b}_{\textrm{Diag}} is the bit error rate of the diagonal basis, and H()H(\cdot) is the binary Shannon entropy. Under the attack, the key rate is upper bounded by Ru=Qa1,1R^{u}=Q^{1,1}_{a} where Qa1,1Q^{1,1}_{a} is the actual gain of the single photon states from both parities under the attack. Apparently, if Rl>RuR_{l}>R^{u}, then Alice and Bob would mistakenly generate keys with a key rate higher than the maximal secure key rate possible under the attack, thus leaking part of the key information to Eve. The goal of Eve is hence to minimize RuR^{u} to the extent that it is smaller than RlR^{l}. We next show that this indeed can happen.

Suppose the intensities of the signal state and the decoy state are μ\mu and ν\nu respectively, it can be shown Tang et al. (2013) that on each side, the optimal success probability of unambiguous state discrimination is

qopt=1exp(|μν|24).q_{\textrm{opt}}=1-\textrm{exp}(-\frac{|\sqrt{\mu}-\sqrt{\nu}|^{2}}{4}). (2)

In the attack, the gains of the signal state and the decoy state at each side are

Qμ\displaystyle Q_{\mu} =\displaystyle= i=1qoptZiμeμμii!,\displaystyle\sum\limits_{i=1}^{\infty}q_{opt}Z_{i}^{\mu}e^{-\mu}\frac{\mu^{i}}{i!},
Qν\displaystyle Q_{\nu} =\displaystyle= i=1qoptZiνeννii!\displaystyle\sum\limits_{i=1}^{\infty}q_{opt}Z_{i}^{\nu}e^{-\nu}\frac{\nu^{i}}{i!} (3)

respectively, where ZiμZ_{i}^{\mu} (ZiνZ_{i}^{\nu}) is the probability of Eve forwarding the photons conditioned on the signal state (the decoy state) and the photon number ii. Here we make the simplified assumption that the dark count is zero, so the summation index starts from 1.

Eve should choose ZiμZ_{i}^{\mu} and ZiνZ_{i}^{\nu} properly so that his faked gains match the normal channel gains of both the signal state and the decoy state, namely,

Qμ\displaystyle Q_{\mu} =\displaystyle= 1eημ,\displaystyle 1-e^{-\eta\mu},
Qν\displaystyle Q_{\nu} =\displaystyle= 1eην.\displaystyle 1-e^{-\eta\nu}. (4)

Here η\eta is the channel loss and we assume there is no dark count for simplicity. In addition, since

Ru=Qa1,1=(qμZ1μeμμ)2,R^{u}=Q^{1,1}_{a}=(q_{\mu}Z_{1}^{\mu}e^{-\mu}\mu)^{2}, (5)

minimizing RuR^{u} is equivalent to minimizing Z1μZ_{1}^{\mu}.

Assume μ1\mu\ll 1 and μ>ν>μ2/2\mu>\nu>\mu^{2}/2 and let η=qoptμ/2\eta=q_{\textrm{opt}}\mu/2, we can take

Z2μ\displaystyle Z_{2}^{\mu} =\displaystyle= 1,\displaystyle 1,
Z1ν\displaystyle Z_{1}^{\nu} =\displaystyle= μ2/2ν,\displaystyle\mu^{2}/2\nu,
Ziμ\displaystyle Z_{i}^{\mu} =\displaystyle= 0i2,\displaystyle 0\;\forall i\neq 2, (6)
Ziν\displaystyle Z_{i}^{\nu} =\displaystyle= 0i1.\displaystyle 0\;\forall i\neq 1.

For these parameters, it can be checked that the constraints Eqs. (III) to (III) are satisfied. Hence we have Z1μ=0Z_{1}^{\mu}=0, thus Ru=0R^{u}=0, meaning that all the key information is leaked to Eve. It only remains to show that Rl>0R^{l}>0 for these parameters.

For simplicity, we assume there are no errors, namely Eμ=0E_{\mu}=0. The estimated key rate lower bound RlR^{l} is then reduced to Qn1,1Q^{1,1}_{n}. In a normal estimation, since Eve cannot distinguish the signal state and the decoy state, we have

Qμ,μ\displaystyle Q_{\mu,\mu} =\displaystyle= i.j=1Yi,jeμμii!eμμjj!,\displaystyle\sum\limits_{i.j=1}^{\infty}Y_{i,j}e^{-\mu}\frac{\mu^{i}}{i!}e^{-\mu}\frac{\mu^{j}}{j!},
Qν,μ\displaystyle Q_{\nu,\mu} =\displaystyle= i,j=1Yi,jeννii!eμμjj!,\displaystyle\sum\limits_{i,j=1}^{\infty}Y_{i,j}e^{-\nu}\frac{\nu^{i}}{i!}e^{-\mu}\frac{\mu^{j}}{j!},
Qμ,ν\displaystyle Q_{\mu,\nu} =\displaystyle= i,j=1Yi,jeμμii!eννjj!,\displaystyle\sum\limits_{i,j=1}^{\infty}Y_{i,j}e^{-\mu}\frac{\mu^{i}}{i!}e^{-\nu}\frac{\nu^{j}}{j!}, (7)
Qμ,μ\displaystyle Q_{\mu,\mu} =\displaystyle= i.j=1Yi,jeμμii!eμμjj!.\displaystyle\sum\limits_{i.j=1}^{\infty}Y_{i,j}e^{-\mu}\frac{\mu^{i}}{i!}e^{-\mu}\frac{\mu^{j}}{j!}.

Here Qα,βQ_{\alpha,\beta} stands for the gain when the mean photon number of Alice’s state is α\alpha and the mean photon number of Bob’s state is β\beta, Yi,jY_{i,j} stands for the gain when Alice’s state contains ii photon and Bob’s state contains jj photons. Since Alice and Bob send their states independently, we have Qα,β=QαQβQ_{\alpha,\beta}=Q_{\alpha}Q_{\beta}, where QαQ_{\alpha} and QβQ_{\beta} are given by Eq. (III).

By a two-step estimation, we first estimate the intermediate quantities Yμ1Y^{1}_{\mu} and Yν1Y^{1}_{\nu} defined by

Yμ1\displaystyle Y^{1}_{\mu} =\displaystyle= i=1Y1,ieμμii!,\displaystyle\sum\limits_{i=1}^{\infty}Y_{1,i}e^{-\mu}\frac{\mu^{i}}{i!},
Yν1\displaystyle Y^{1}_{\nu} =\displaystyle= i=1Y1,ieννii!.\displaystyle\sum\limits_{i=1}^{\infty}Y_{1,i}e^{-\nu}\frac{\nu^{i}}{i!}. (8)

Using Eq. (III), Yμ1Y^{1}_{\mu} can be estimated from Qμ,μQ_{\mu,\mu} and Qν,μQ_{\nu,\mu} as

Yμ1\displaystyle Y^{1}_{\mu} \displaystyle\geq μμνν2(Qν,μeνQμ,μeμν2μ2)\displaystyle\frac{\mu}{\mu\nu-\nu^{2}}(Q_{\nu,\mu}e^{\nu}-Q_{\mu,\mu}e^{\mu}\frac{\nu^{2}}{\mu^{2}}) (9)
\displaystyle\approx η2μ,\displaystyle\eta^{2}\mu,

and similarly for Yν1Y^{1}_{\nu}, we have

Yν1η2ν.Y^{1}_{\nu}\approx\eta^{2}\nu. (10)

Finally, by Eqs. (III) to (10), Y1,1Y_{1,1} can be estimated as

Y1,1\displaystyle Y_{1,1} \displaystyle\geq μμνν2(Yν1eνYμ1eμν2μ2)\displaystyle\frac{\mu}{\mu\nu-\nu^{2}}(Y^{1}_{\nu}e^{\nu}-Y^{1}_{\mu}e^{\mu}\frac{\nu^{2}}{\mu^{2}}) (11)
\displaystyle\approx η2.\displaystyle\eta^{2}.

Thus Rl=Q1,1=Y1,1(eμμ)2>0=RuR^{l}=Q_{1,1}=Y_{1,1}(e^{-\mu}\mu)^{2}>0=R^{u}, which shows that Eve’s attack is successful.

It should be noted that this example is not the only case that Eve can successfully attack a MDI-QKD system without phase randomization. The exact parameter region which is vulnerable to Eve’s attack is beyond the scope of this paper, and is left as an interesting future research direction.

IV Discrete-phase-randomized MDI-QKD protocol

In this section, we first describe our discrete-phase-randomized MDI-QKD protocol and then provide its security analysis.

A weak coherent laser can be described by the following state Glauber (1963)

|α=e|α|22αnn!|n,\mbox{$\left|\alpha\right\rangle$}=e^{-\frac{|\alpha|^{2}}{2}}\sum\frac{\alpha^{n}}{\sqrt{n!}}\mbox{$\left|n\right\rangle$}, (12)

where α\alpha is a complex number and |n\left|n\right\rangle is the Fock state of nn photons. In continuous phase randomization, a random phase θ[0,2π)\theta\in[0,2\pi) is applied on |α\left|\alpha\right\rangle, and the input state becomes

12π02π|αeiθαeiθ|𝑑θ=n=0e|α|2|α|2n!|nn|.\frac{1}{2\pi}\int_{0}^{2\pi}\mbox{$\left|\alpha e^{i\theta}\right\rangle$}\mbox{$\left\langle\alpha e^{i\theta}\right|$}d\theta=\sum\limits_{n=0}^{\infty}e^{-|\alpha|^{2}}\frac{|\alpha|^{2}}{n!}\mbox{$\left|n\right\rangle$}\mbox{$\left\langle n\right|$}. (13)

Conditioning on sufficiently small |α||\alpha| and photon detection, this input state approximates the single photon state |1\left|1\right\rangle1|\left\langle 1\right| quite well and hence is a good substitute for a single photon source.

In contrast to continuous phase randomization, in our discrete-phase-randomized MDI-QKD protocol, we apply one of the discrete phases

{θk=2πkN|k=0,1,,N1}\{\theta_{k}=\frac{2\pi k}{N}|k=0,1,\dots,N-1\} (14)

randomly on the weak coherent laser |2α\left|\sqrt{2}\alpha\right\rangle. Here NN is the number of discrete phases. Using the virtual qudit formalism of randomization, the input state can be written as

|ΨN\left|\Psi_{N}\right\rangle =\displaystyle= k=0N1|akA|2αe2kπi/NB\displaystyle\sum\limits_{k=0}^{N-1}\mbox{$\left|a_{k}\right\rangle$}_{A}\mbox{$\left|\sqrt{2}\alpha e^{2k\pi i/N}\right\rangle$}_{B}
=\displaystyle= j=0N1|bjA|λjB,\displaystyle\sum\limits_{j=0}^{N-1}\mbox{$\left|b_{j}\right\rangle$}_{A}\mbox{$\left|\lambda_{j}\right\rangle$}_{B},

where

|λj=k=0N1e2kjπi/N|e2kπi/N2α.\mbox{$\left|\lambda_{j}\right\rangle$}=\sum_{k=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|e^{2k\pi i/N}\sqrt{2}\alpha\right\rangle$}.\\ (16)

Here {|ak}k=0,1,,N1\{\mbox{$\left|a_{k}\right\rangle$}\}_{k=0,1,\dots,N-1} and {|bj}j=0,1,,N1\{\mbox{$\left|b_{j}\right\rangle$}\}_{j=0,1,\dots,N-1} are sets of orthogonal bases, and |ak\left|a_{k}\right\rangle can be transformed from |bj\left|b_{j}\right\rangle by

|ak=j=0N1e2kjπi/N|bj.\mbox{$\left|a_{k}\right\rangle$}=\sum_{j=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|b_{j}\right\rangle$}. (17)

By Taylor expansion on |λj\left|\lambda_{j}\right\rangle, one has

|λj=l=0(2α)lN+j(lN+j)!|lN+j.\mbox{$\left|\lambda_{j}\right\rangle$}=\sum_{l=0}^{\infty}\frac{(\sqrt{2}\alpha)^{lN+j}}{\sqrt{(lN+j)!}}\mbox{$\left|lN+j\right\rangle$}.\\ (18)

The probability of obtaining |λj\left|\lambda_{j}\right\rangle is

Pj=λj|λjj=0N1λj|λj=l=0μlN+jeμ(lN+j)!,\displaystyle P_{j}=\frac{\mbox{$\left\langle\lambda_{j}|\lambda_{j}\right\rangle$}}{\sum_{j=0}^{N-1}\mbox{$\left\langle\lambda_{j}|\lambda_{j}\right\rangle$}}=\sum_{l=0}^{\infty}\frac{\mu^{lN+j}e^{-\mu}}{(lN+j)!}, (19)

where μ=2|α|2\mu=2|\alpha|^{2}. It can be seen that as NN goes to infinity, |λj\left|\lambda_{j}\right\rangle approaches the Fock state |j\left|j\right\rangle. Therefore we will call |λj\left|\lambda_{j}\right\rangle the approximated jj-photon state.

The input state |λj\left|\lambda_{j}\right\rangle is then encoded into four BB84 states with the phase encoding and becomes one of

|0xL\left|0_{x}^{L}\right\rangle =\displaystyle= k=0N1e2kjπi/N|e2kπi/Nα|e2kπi/Nα,\displaystyle\sum_{k=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|e^{2k\pi i/N}\alpha\right\rangle$}\mbox{$\left|e^{2k\pi i/N}\alpha\right\rangle$},
|1xL\left|1_{x}^{L}\right\rangle =\displaystyle= k=0N1e2kjπi/N|e2kπi/Nα|e2kπi/Nα,\displaystyle\sum_{k=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|e^{2k\pi i/N}\alpha\right\rangle$}\mbox{$\left|-e^{2k\pi i/N}\alpha\right\rangle$},
|0yL\left|0_{y}^{L}\right\rangle =\displaystyle= k=0N1e2kjπi/N|e2kπi/Nα|ie2kπi/Nα,\displaystyle\sum_{k=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|e^{2k\pi i/N}\alpha\right\rangle$}\mbox{$\left|ie^{2k\pi i/N}\alpha\right\rangle$}, (20)
|1yL\left|1_{y}^{L}\right\rangle =\displaystyle= k=0N1e2kjπi/N|e2kπi/Nα|ie2kπi/Nα,\displaystyle\sum_{k=0}^{N-1}e^{-2kj\pi i/N}\mbox{$\left|e^{2k\pi i/N}\alpha\right\rangle$}\mbox{$\left|-ie^{2k\pi i/N}\alpha\right\rangle$},

where |0xL\left|0_{x}^{L}\right\rangle and |1xL\left|1_{x}^{L}\right\rangle are logical qubits in the XX basis, and |0yL\left|0_{y}^{L}\right\rangle and |1yL\left|1_{y}^{L}\right\rangle are logical qubits in the YY basis, the first coherent state is the reference state and the second coherent state is the signal state with BB84 phases. Since the probabilities of choosing the eigenstates are equal, the overall states encoded in the XX basis and the YY basis are

ρABX\displaystyle\rho_{AB}^{X} =\displaystyle= (|0xL0xL|+|1xL1xL|)A(|0xL0xL|+|1xL1xL|)B,\displaystyle(\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$})_{A}\otimes(\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$})_{B},
ρABY\displaystyle\rho_{AB}^{Y} =\displaystyle= (|0yL0yL|+|1yL1yL|)A(|0yL0yL|+|1yL1yL|)B,\displaystyle(\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})_{A}\otimes(\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})_{B},

respectively. In the ideal case of basis-independent sources, we have

ρABX=ρABY.\rho_{AB}^{X}=\rho_{AB}^{Y}. (22)

We can characterize the deviation from the ideal case by bounding the fidelity between ρABX\rho_{AB}^{X} and ρABY\rho_{AB}^{Y} as

Fj,j(ρABX,ρABY)=trρABYρABXρABY\displaystyle F_{j,j}(\rho_{AB}^{X},\rho_{AB}^{Y})=\mathrm{tr}\sqrt{\sqrt{\rho_{AB}^{Y}}\rho_{AB}^{X}\sqrt{\rho_{AB}^{Y}}} (23)
|l=0μlN+j(lN+j)!2lN+j2(coslN+j4π+sinlN+j4π)l=0μlN+j(lN+j)!|2.\displaystyle\geq\left|\frac{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}2^{-\frac{lN+j}{2}}\left(\cos\frac{lN+j}{4}\pi+\sin\frac{lN+j}{4}\pi\right)}{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}}\right|^{2}.

The concrete derivation can be found in Appendix A. The first order approximation of F1,1F_{1,1} with respect to μN\mu^{N} is

F1,1(1)12(12N2cosN4π)μN(N+1)!.F^{(1)}_{1,1}\geq 1-2\left(1-2^{-\frac{N}{2}}\cos\frac{N}{4}\pi\right)\frac{\mu^{N}}{(N+1)!}. (24)

This will be later used in the key rate formula. Its derivation can also be found in Appendix A.

IV.1 Key rate

In a normal MDI-QKD, the key rate formula is given by Eq. (1). In the discrete phase version, we need to modify the key rate formula to

R\displaystyle R \displaystyle\geq QRectf(ERect)H(ERect)\displaystyle-Q_{\textrm{Rect}}f(E_{\textrm{Rect}})H(E_{\textrm{Rect}}) (25)
+ijPiPjYi,j[1H(ERecti,j,p)].\displaystyle+\sum_{i}\sum_{j}P_{i}P_{j}Y_{i,j}[1-H(E^{i,j,p}_{\textrm{Rect}})].

The error correction part stays unchanged. For the privacy amplification part, PiP_{i} is the probability of obtaining the state |λi\left|\lambda_{i}\right\rangle when a party uses a signal state, Yi,jY_{i,j} and ERecti,j,pE^{i,j,p}_{\textrm{Rect}} are the gain and the phase error rate of the rectilinear basis when Alice’s state is |λi\left|\lambda_{i}\right\rangle, Bob’s state is |λj\left|\lambda_{j}\right\rangle and both parties use signal states.

Recall that a phase error of the rectilinear basis occurs when Alice and Bob’s states are both encoded in the XX basis, and their joint state after the Bell measurement is |0xLA|0xLB+|1xLA|1xLB\mbox{$\left|0_{x}^{L}\right\rangle$}_{A}\mbox{$\left|0_{x}^{L}\right\rangle$}_{B}+\mbox{$\left|1_{x}^{L}\right\rangle$}_{A}\mbox{$\left|1_{x}^{L}\right\rangle$}_{B} instead of the correct outcome |0xLA|0xLB|1xLA|1xLB\mbox{$\left|0_{x}^{L}\right\rangle$}_{A}\mbox{$\left|0_{x}^{L}\right\rangle$}_{B}-\mbox{$\left|1_{x}^{L}\right\rangle$}_{A}\mbox{$\left|1_{x}^{L}\right\rangle$}_{B}. If their joint state after the Bell measurement is |0xLA|1xLB|0xLA|1xLB\mbox{$\left|0_{x}^{L}\right\rangle$}_{A}\mbox{$\left|1_{x}^{L}\right\rangle$}_{B}-\mbox{$\left|0_{x}^{L}\right\rangle$}_{A}\mbox{$\left|1_{x}^{L}\right\rangle$}_{B}, a bit error of the rectilinear basis is said to occur. Similarly, for the diagonal basis where Alice and Bob’s states are both encoded in the YY basis, the correct outcome after the Bell measurement should be |0yLA|0yLB|1yLA|1yLB\mbox{$\left|0_{y}^{L}\right\rangle$}_{A}\mbox{$\left|0_{y}^{L}\right\rangle$}_{B}-\mbox{$\left|1_{y}^{L}\right\rangle$}_{A}\mbox{$\left|1_{y}^{L}\right\rangle$}_{B}. If the actual joint state is |0yLA|0yLB+|1yLA|1yLB\mbox{$\left|0_{y}^{L}\right\rangle$}_{A}\mbox{$\left|0_{y}^{L}\right\rangle$}_{B}+\mbox{$\left|1_{y}^{L}\right\rangle$}_{A}\mbox{$\left|1_{y}^{L}\right\rangle$}_{B}, a phase error of the diagonal basis is said to occur. If the actual joint state is |0yLA|1yLB|1yLA|0yLB\mbox{$\left|0_{y}^{L}\right\rangle$}_{A}\mbox{$\left|1_{y}^{L}\right\rangle$}_{B}-\mbox{$\left|1_{y}^{L}\right\rangle$}_{A}\mbox{$\left|0_{y}^{L}\right\rangle$}_{B}, a bit error of the diagonal basis is said to occur.

In the key formula, since QRectQ_{\textrm{Rect}} and ERectE_{\textrm{Rect}} can be directly measured, only Yi,jY_{i,j} and ERecti,j,pE^{i,j,p}_{\textrm{Rect}} need to be estimated. In the basis-independent case, ERect1,1,p=EDiag1,1,bE^{1,1,p}_{\textrm{Rect}}=E^{1,1,b}_{\textrm{Diag}}, hence the phase error of the rectilinear basis can be estimated using the bit error rate of the diagonal basis. However, in the discrete phase case, the basis independence property no longer holds. Fortunately, we can estimate the difference between e1,1b=EDiag1,1,be_{1,1}^{b}=E^{1,1,b}_{\textrm{Diag}} and e1,1p=ERect1,1,pe_{1,1}^{p}=E^{1,1,p}_{\textrm{Rect}} as follows Lo and Preskill (2007),

ej,jp\displaystyle e_{j,j}^{p}\leq ej,jb+4Δj,j(1Δj,j)(12ej,jb)\displaystyle e_{j,j}^{b}+4\Delta_{j,j}(1-\Delta_{j,j})(1-2e_{j,j}^{b}) (26)
+4(12Δj,j)Δj,j(1Δj,j)ej,jb(1ej,jb),\displaystyle+4(1-2\Delta_{j,j})\sqrt{\Delta_{j,j}(1-\Delta_{j,j})e_{j,j}^{b}(1-e_{j,j}^{b})},

where

Δj,j=1Fj,j2Yj,j.\Delta_{j,j}=\frac{1-F_{j,j}}{2Y_{j,j}}. (27)

Here Fj,jF_{j,j} is given by Eq. (23). Next we show how to estimate the parameters Y1,1Y_{1,1} and e1,1be_{1,1}^{b}.

IV.2 Parameter estimation

In discrete-phase-randomized MDI-QKD, we need to estimate the gain Yi,jα,βY^{\alpha,\beta}_{i,j} and the error rate ei,jα,βe^{\alpha,\beta}_{i,j}. First we note that the following relations hold:

Qα,β\displaystyle Q^{\alpha,\beta} =\displaystyle= i,j=0N1PiαPjβYi,jα,β,\displaystyle\sum_{i,j=0}^{N-1}P^{\alpha}_{i}P^{\beta}_{j}Y^{\alpha,\beta}_{i,j},
Qα,βEα,β\displaystyle Q^{\alpha,\beta}E^{\alpha,\beta} =\displaystyle= i,j=0N1PiαPjβYi,jα,βei,jα,β,\displaystyle\sum_{i,j=0}^{N-1}P^{\alpha}_{i}P^{\beta}_{j}Y^{\alpha,\beta}_{i,j}e^{\alpha,\beta}_{i,j}, (28)

where α\alpha(β\beta) distinguishes the signal state and the decoy states, ii(jj) stands for the approximated ii-photon(jj-photon) state, Qα,βQ^{\alpha,\beta} and Eα,βE^{\alpha,\beta} are the observed gain and error rate in the case that Alice uses the intensity setting α\alpha and Bob uses the intensity setting β\beta, Yi,jα,βY^{\alpha,\beta}_{i,j} and ei,jα,βe^{\alpha,\beta}_{i,j} are the intrinsic gain and error rate in the case that Alice uses the intensity setting α\alpha and the approximated ii-photon state, Bob uses the intensity setting β\beta and the approximated jj-photon state, PiαP^{\alpha}_{i} is the probability of generating an approximated ii-photon state when the intensity setting is α\alpha.

There is an inherent assumption in normal MDI-QKD, namely

Yi,jα1,β1\displaystyle Y_{i,j}^{\alpha_{1},\beta_{1}} =Yi,jα2,β2,\displaystyle=Y_{i,j}^{\alpha_{2},\beta_{2}},
ei,jα1,β1\displaystyle e_{i,j}^{\alpha_{1},\beta_{1}} =ei,jα2,β2.\displaystyle=e_{i,j}^{\alpha_{2},\beta_{2}}. (29)

This no longer holds in the case of discrete phase randomization as

|λi,jα1,β1|λi,jα2,β2,\mbox{$\left|\lambda_{i,j}^{\alpha_{1},\beta_{1}}\right\rangle$}\neq\mbox{$\left|\lambda_{i,j}^{\alpha_{2},\beta_{2}}\right\rangle$}, (30)

where |λi,jα,β\left|\lambda_{i,j}^{\alpha,\beta}\right\rangle is the joint state of Alice and Bob when Alice uses the intensity setting α\alpha together with the approximated ii-photon state, and Bob uses the intensity setting β\beta together with the approximated jj-photon state. Nevertheless, we can bound the difference between gains and errors of different intensities as

|Yi,jα,μYi,jα,ν|\displaystyle|Y_{i,j}^{\alpha,\mu}-Y_{i,j}^{\alpha,\nu}| 1Fμν2,\displaystyle\leq\sqrt{1-F^{2}_{\mu\nu}},
|Yi,jα,μei,jα,μYi,jα,νei,jα,ν|\displaystyle|Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu}-Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu}| 1Fμν2,\displaystyle\leq\sqrt{1-F^{2}_{\mu\nu}}, (31)

where

Fμν=l=0(μν)lN/2(lN)!l=0μlN(lN)!l=0νlN(lN)!.F_{\mu\nu}=\frac{\sum_{l=0}^{\infty}\frac{(\mu\nu)^{lN/2}}{(lN)!}}{\sqrt{\sum_{l=0}^{\infty}\frac{\mu^{lN}}{(lN)!}\sum_{l=0}^{\infty}\frac{\nu^{lN}}{(lN)!}}}. (32)

The derivation of these bounds can be found in Appendix B.

The estimation of the gain Yi,jα,βY^{\alpha,\beta}_{i,j} and the error rate ei,jα,βe^{\alpha,\beta}_{i,j} is similar to normal MDI-QKD. We start with the estimation of the gain Yi,jα,βY^{\alpha,\beta}_{i,j}. Note that the first equation in Eq. (IV.2) can be rewritten as

Qα,β=i=0N1PiαYiα,β,Q^{\alpha,\beta}=\sum_{i=0}^{N-1}P^{\alpha}_{i}Y^{\alpha,\beta}_{i}, (33)

where

Yiα,β=j=0N1PjβYi,jα,β.Y^{\alpha,\beta}_{i}=\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\alpha,\beta}_{i,j}. (34)

For notation simplicity, let

ϵ=1Fμν2.\epsilon=\sqrt{1-F^{2}_{\mu\nu}}. (35)

From Eq. (34), we get

|Yiα,βYiμ,β|\displaystyle|Y^{\alpha,\beta}_{i}-Y^{\mu,\beta}_{i}| =\displaystyle= |j=0N1Pjβ(Yi,jα,βYi,jμ,β)|\displaystyle|\sum_{j=0}^{N-1}P^{\beta}_{j}(Y^{\alpha,\beta}_{i,j}-Y^{\mu,\beta}_{i,j})|
\displaystyle\leq j=0N1Pjβ|Yi,jα,βYi,jμ,β|\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}|Y^{\alpha,\beta}_{i,j}-Y^{\mu,\beta}_{i,j}|
\displaystyle\leq j=0N1Pjβϵ=ϵ,\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}\epsilon=\epsilon,

where the last inequality holds because j=0N1Pjβ=1\sum_{j=0}^{N-1}P_{j}^{\beta}=1. Hence, we can estimate the upper bound and the lower bound of Yiμ,βY^{\mu,\beta}_{i} under the following constraint,

Qμ,β\displaystyle Q^{\mu,\beta} =\displaystyle= i=0N1PiμYiμ,β,\displaystyle\sum_{i=0}^{N-1}P^{\mu}_{i}Y^{\mu,\beta}_{i},
Qα,β\displaystyle Q^{\alpha,\beta} =\displaystyle= i=0N1PiαYiα,β=i=0N1PiαYiμ,β±ϵ,\displaystyle\sum_{i=0}^{N-1}P^{\alpha}_{i}Y^{\alpha,\beta}_{i}=\sum_{i=0}^{N-1}P^{\alpha}_{i}Y^{\mu,\beta}_{i}\pm\epsilon, (37)
0\displaystyle 0 \displaystyle\leq Yiμ,β1.\displaystyle Y^{\mu,\beta}_{i}\leq 1.

After the range of Yiμ,βY^{\mu,\beta}_{i} is estimated for all β\beta, we can estimate the upper bound and the lower bound of Yi,jμ,μY^{\mu,\mu}_{i,j} under the following constraint:

Yiμ,μ\displaystyle Y^{\mu,\mu}_{i} =\displaystyle= j=0N1PjμYi,jμ,μ,\displaystyle\sum_{j=0}^{N-1}P^{\mu}_{j}Y^{\mu,\mu}_{i,j},
Yiμ,β\displaystyle Y^{\mu,\beta}_{i} =\displaystyle= j=0N1PjβYi,jμ,β=j=0N1PjβYi,jμ,μ±ϵ,\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\mu,\beta}_{i,j}=\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\mu,\mu}_{i,j}\pm\epsilon, (38)
0\displaystyle 0 \displaystyle\leq Yi,jμ,μ1.\displaystyle Y^{\mu,\mu}_{i,j}\leq 1.

The estimation of Yi,jα,βei,jα,βY^{\alpha,\beta}_{i,j}e^{\alpha,\beta}_{i,j} is almost identical to the estimation of Yi,jα,βY^{\alpha,\beta}_{i,j}. We can rewrite the second equation in Eq. (IV.2) as

Qα,βEα,β=iN1PiαWiα,β,Q^{\alpha,\beta}E^{\alpha,\beta}=\sum_{i}^{N-1}P^{\alpha}_{i}W^{\alpha,\beta}_{i}, (39)

where

Wiα,β=j=0N1PjβYi,jα,βei,jα,β.W^{\alpha,\beta}_{i}=\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\alpha,\beta}_{i,j}e^{\alpha,\beta}_{i,j}. (40)

From Eq. (40), we get

|Wiα,βWiμ,β|\displaystyle|W^{\alpha,\beta}_{i}-W^{\mu,\beta}_{i}| =\displaystyle= |j=0N1Pjβ(Yi,jα,βei,jα,βYi,jμ,βei,jμ,β)|\displaystyle|\sum_{j=0}^{N-1}P^{\beta}_{j}(Y^{\alpha,\beta}_{i,j}e^{\alpha,\beta}_{i,j}-Y^{\mu,\beta}_{i,j}e^{\mu,\beta}_{i,j})|
\displaystyle\leq j=0N1Pjβ|Yi,jα,βei,jα,βYi,jμ,βei,jμ,β|\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}|Y^{\alpha,\beta}_{i,j}e^{\alpha,\beta}_{i,j}-Y^{\mu,\beta}_{i,j}e^{\mu,\beta}_{i,j}|
\displaystyle\leq j=0N1Pjβϵ=ϵ.\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}\epsilon=\epsilon.

Hence, we can estimate the upper bound and the lower bound of Wiμ,βW^{\mu,\beta}_{i} under the following constraint:

Qμ,βEμ,β\displaystyle Q^{\mu,\beta}E^{\mu,\beta} =\displaystyle= i=0N1PiμWiμ,β,\displaystyle\sum_{i=0}^{N-1}P^{\mu}_{i}W^{\mu,\beta}_{i},
Qα,βEα,β\displaystyle Q^{\alpha,\beta}E^{\alpha,\beta} =\displaystyle= i=0N1PiαWiα,β=i=0N1PiαWiμ,β±ϵ,\displaystyle\sum_{i=0}^{N-1}P^{\alpha}_{i}W^{\alpha,\beta}_{i}=\sum_{i=0}^{N-1}P^{\alpha}_{i}W^{\mu,\beta}_{i}\pm\epsilon, (42)
0\displaystyle 0 \displaystyle\leq Wiμ,β1.\displaystyle W^{\mu,\beta}_{i}\leq 1.

After the range of Wiμ,βW^{\mu,\beta}_{i} is estimated for all β\beta, we can estimate the upper bound and the lower bound of Yi,jμ,μei,jμ,μY^{\mu,\mu}_{i,j}e^{\mu,\mu}_{i,j} under the following constraint:

Wiμ,μ\displaystyle W^{\mu,\mu}_{i} =\displaystyle= j=0N1PjμYi,jμ,μei,jμ,μ,\displaystyle\sum_{j=0}^{N-1}P^{\mu}_{j}Y^{\mu,\mu}_{i,j}e^{\mu,\mu}_{i,j},
Wiμ,β\displaystyle W^{\mu,\beta}_{i} =\displaystyle= j=0N1PjβYi,jμ,βei,jμ,β=j=0N1PjβYi,jμ,μei,jμ,μ±ϵ,\displaystyle\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\mu,\beta}_{i,j}e^{\mu,\beta}_{i,j}=\sum_{j=0}^{N-1}P^{\beta}_{j}Y^{\mu,\mu}_{i,j}e^{\mu,\mu}_{i,j}\pm\epsilon, (43)
0\displaystyle 0 \displaystyle\leq Yi,jμ,μei,jμ,μ1.\displaystyle Y^{\mu,\mu}_{i,j}e^{\mu,\mu}_{i,j}\leq 1.

This completes the parameter estimation of the discrete-phase-randomized MDI-QKD protocol.

Each linear system presented in this section can be efficiently solved through linear programming. When there are MM decoy states, each linear system contains NN variables and 2N+2M+12N+2M+1 constraints. Hence, the computation of its solution is manageable when NN is small (e.g., N<20N<20). When NN is large (e.g., N>10 000N>10\;000), the computation time can be infeasibly long. In that case, one method to accelerate the computation at a cost of a small decrease in performance is that we keep only variables with the lowest KK indices, such as Y0μ,β,,YK1μ,βY_{0}^{\mu,\beta},\dots,Y_{K-1}^{\mu,\beta}, and relax other variables to 0 or 1 in all constraining equations and inequalities. The reduced linear system then contains KK variables and 2K+2M+22K+2M+2 constraints. In later simulations, we take M=2M=2 and K=3K=3. Larger values of MM and KK can lead to more accurate estimation of the parameters.

IV.3 Simulation result

In Fig. 2, we plot the key rate of continuous randomization (annotated as “random phases”) and various number of discrete phases (9, 10, 11, 12, 14 phases, respectively) under different transmission distances. It can be seen that 14 phases already approximate continuous phase randomization quite well. The detailed simulation model and simulation parameters are shown in Appendix C.

Refer to caption
Figure 2: The relation between the key rate and the transmission distance for continuously random phases and discrete phases. The dashed line is the key rate for continuously random phases and the solid lines from left to right are for 9, 10, 11, 12, and 14 discrete phases, respectively.

With the same simulation model, we plot the key rate of continuous randomization (annotated as “random phases”) and various number of discrete phases (9, 10, 11, 12, 14 phases, respectively) under different noise levels e1,1be_{1,1}^{b} in Fig. 3. It can be seen that the security threshold (maximally tolerable noise) of 14 phases is already very close to that of continuous phase randomization, which is about 8.7%.

Refer to caption
Figure 3: The relation between the key rate and the noise level for continuously random phases and discrete phases. The dashed line is the key rate for continuously random phases and the solid lines from left to right are for 9, 10, 11, 12, and 14 discrete phases, respectively.

In a practical experiment, the deviation of experimental parameters from the simulation parameters used here should be accounted for by substituting the actual experimental parameters into the simulation model, and the selection of the number of discrete phases should be determined through this revized simulation.

V Conclusion

In summary, we showed that MDI-QKD with imperfect phase randomization is vulnerable to attacks and, as a solution, proposed a discrete-phase-randomized measurement-device-independent quantum key distribution protocol. We also provided a security proof of the protocol. Simulation results confirm that the protocol with only a few phases (14 phases) already approximates continuous phase randomization quite well.

As future work, we can consider further source imperfection in measurement-device-independent quantum key distribution. One direction is to consider imperfectly prepared discrete phases {|αei(2πk/N±δ)}k=1,,N\{\mbox{$\left|\alpha e^{i(2\pi k/N\pm\delta)}\right\rangle$}\}_{k=1,\dots,N}, where δ\delta is a small quantity characterizing the deviation from the exact discrete phases. One can modify the fidelity calculation to accommodate for this change. Another direction is to extend our analysis to other MDI protocols requiring weak coherent sources, such as MDI entanglement witness Branciard et al. (2013).

Acknowledgements

This work was supported by the internal Grant No. SLH00202007 from East China University of Science and Technology.

Appendix A Fidelity Calculation

In this section, we will provide the details on the calculation of the fidelity between the input states prepared in different bases. We will utilize a few results from Ref. Cao et al. (2015).

By Eqs. (IV) and (23) in the main text, we have

Fj,j(ρABX,ρABY)\displaystyle F_{j,j}(\rho_{AB}^{X},\rho_{AB}^{Y}) (44)
=\displaystyle= F((|0xL0xL|+|1xL1xL|)A(|0xL0xL|+|1xL1xL|)B,\displaystyle F((\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$})_{A}\otimes(\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$})_{B},
(|0yL0yL|+|1yL1yL|)A(|0yL0yL|+|1yL1yL|)B)\displaystyle\;\;\;\;(\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})_{A}\otimes(\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})_{B})
=\displaystyle= F(|0xL0xL|+|1xL1xL|,|0yL0yL|+|1yL1yL|)2\displaystyle F(\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$},\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})^{2}

In Ref. Cao et al. (2015), it was shown that

F(|0xL0xL|+|1xL1xL|,|0yL0yL|+|1yL1yL|)\displaystyle F(\mbox{$\left|0_{x}^{L}\right\rangle$}\mbox{$\left\langle 0_{x}^{L}\right|$}+\mbox{$\left|1_{x}^{L}\right\rangle$}\mbox{$\left\langle 1_{x}^{L}\right|$},\mbox{$\left|0_{y}^{L}\right\rangle$}\mbox{$\left\langle 0_{y}^{L}\right|$}+\mbox{$\left|1_{y}^{L}\right\rangle$}\mbox{$\left\langle 1_{y}^{L}\right|$})
\displaystyle\geq |l=0μlN+j(lN+j)!2lN+j2(coslN+j4π+sinlN+j4π)l=0μlN+j(lN+j)!|.\displaystyle\left|\frac{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}2^{-\frac{lN+j}{2}}\left(\cos\frac{lN+j}{4}\pi+\sin\frac{lN+j}{4}\pi\right)}{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}}\right|.

Thus Eq. (23) in the main text holds.

In addition, in Ref. Cao et al. (2015), it was shown that

|l=0μlN+j(lN+j)!2lN+j2(coslN+j4π+sinlN+j4π)l=0μlN+j(lN+j)!|\displaystyle\left|\frac{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}2^{-\frac{lN+j}{2}}\left(\cos\frac{lN+j}{4}\pi+\sin\frac{lN+j}{4}\pi\right)}{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}}\right| (46)
\displaystyle\geq 1(12N2cosN4π)μN(N+1)!.\displaystyle 1-\left(1-2^{-\frac{N}{2}}\cos\frac{N}{4}\pi\right)\frac{\mu^{N}}{(N+1)!}.

So we have

Fj,j(ρABX,ρABY)\displaystyle F_{j,j}(\rho_{AB}^{X},\rho_{AB}^{Y})
=\displaystyle= |l=0μlN+j(lN+j)!2lN+j2(coslN+j4π+sinlN+j4π)l=0μlN+j(lN+j)!|2\displaystyle\left|\frac{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}2^{-\frac{lN+j}{2}}\left(\cos\frac{lN+j}{4}\pi+\sin\frac{lN+j}{4}\pi\right)}{\sum_{l=0}^{\infty}\frac{\mu^{lN+j}}{(lN+j)!}}\right|^{2}
\displaystyle\geq (1(12N2cosN4π)μN(N+1)!)2\displaystyle\left(1-\left(1-2^{-\frac{N}{2}}\cos\frac{N}{4}\pi\right)\frac{\mu^{N}}{(N+1)!}\right)^{2}
\displaystyle\geq 12(12N2cosN4π)μN(N+1)!.\displaystyle 1-2\left(1-2^{-\frac{N}{2}}\cos\frac{N}{4}\pi\right)\frac{\mu^{N}}{(N+1)!}.

Hence, the first-order approximation of the fidelity in the main text is proved.

Appendix B Decoy-State Parameter Deviation

In this section, we show the details on the deviation of decoy state gain and error rate. Like the previous section, here we will also utilizes some results from Ref. Cao et al. (2015).

By the quantum coin idea Gottesman et al. (2004), we have

Yi,jα,μYi,jα,ν+(1Yi,jα,μ)(1Yi,jα,ν)\displaystyle\sqrt{Y_{i,j}^{\alpha,\mu}Y_{i,j}^{\alpha,\nu}}+\sqrt{(1-Y_{i,j}^{\alpha,\mu})(1-Y_{i,j}^{\alpha,\nu})}
F(|λiα|λjμ,|λiα|λjν),\displaystyle\geq F(\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\mu}\right\rangle$},\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\nu}\right\rangle$}),
Yi,jα,μei,jα,μYi,jα,νei,jα,ν+(1Yi,jα,μei,jα,μ)(1Yi,jα,νei,jα,ν)\displaystyle\sqrt{Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu}Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu}}+\sqrt{(1-Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu})(1-Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu})}
F(|λiα|λjμ,|λiα|λjν).\displaystyle\geq F(\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\mu}\right\rangle$},\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\nu}\right\rangle$}). (48)

The right-hand side can be simplified as

F(|λiα|λjμ,|λiα|λjν)=F(|λjμ,|λjν)Fμν.F(\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\mu}\right\rangle$},\mbox{$\left|\lambda_{i}^{\alpha}\right\rangle$}\mbox{$\left|\lambda_{j}^{\nu}\right\rangle$})=F(\mbox{$\left|\lambda_{j}^{\mu}\right\rangle$},\mbox{$\left|\lambda_{j}^{\nu}\right\rangle$})\geq F_{\mu\nu}. (49)

The first inequality is because the first systems of the two states are identical, and the second inequality was shown in Ref. Cao et al. (2015).

Hence

Yi,jα,μYi,jα,ν+(1Yi,jα,μ)(1Yi,jα,ν)Fμν,\displaystyle\sqrt{Y_{i,j}^{\alpha,\mu}Y_{i,j}^{\alpha,\nu}}+\sqrt{(1-Y_{i,j}^{\alpha,\mu})(1-Y_{i,j}^{\alpha,\nu})}\geq F_{\mu\nu},
Yi,jα,μei,jα,μYi,jα,νei,jα,ν+(1Yi,jα,μei,jα,μ)(1Yi,jα,νei,jα,ν)\displaystyle\sqrt{Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu}Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu}}+\sqrt{(1-Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu})(1-Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu})}
Fμν.\displaystyle\geq F_{\mu\nu}. (50)

In Ref. Cao et al. (2015), it was shown that if

xy+(1x)(1y)Fμν,\sqrt{xy}+\sqrt{(1-x)(1-y)}\geq F_{\mu\nu}, (51)

then

|xy|1Fμν2.|x-y|\leq\sqrt{1-F_{\mu\nu}^{2}}. (52)

Hence we have

|Yi,jα,μYi,jα,ν|\displaystyle|Y_{i,j}^{\alpha,\mu}-Y_{i,j}^{\alpha,\nu}| 1Fμν2,\displaystyle\leq\sqrt{1-F^{2}_{\mu\nu}}, (53)
|Yi,jα,μei,jα,μYi,jα,νei,jα,ν|\displaystyle|Y_{i,j}^{\alpha,\mu}e_{i,j}^{\alpha,\mu}-Y_{i,j}^{\alpha,\nu}e_{i,j}^{\alpha,\nu}| 1Fμν2.\displaystyle\leq\sqrt{1-F^{2}_{\mu\nu}}.

This finishes the proof.

Appendix C Simulation

In this section, we describe our simulation model and calculate the key rate.

In the simulation model, we have

η\displaystyle\eta =\displaystyle= 10α1L/10η1\displaystyle 10^{-\alpha_{1}L/10}\eta_{1}
Qμν\displaystyle Q_{\mu\nu} =\displaystyle= (Y0+1eημ)(Y0+1eην),\displaystyle(Y_{0}+1-e^{-\eta\mu})(Y_{0}+1-e^{-\eta\nu}), (54)
EμνQμν\displaystyle E_{\mu\nu}Q_{\mu\nu} =\displaystyle= Y0(Y0+2eημeην)/2\displaystyle Y_{0}(Y_{0}+2-e^{-\eta\mu}-e^{-\eta\nu})/2
+ed(1eημ)(1eην),\displaystyle+e_{d}(1-e^{-\eta\mu})(1-e^{-\eta\nu}),

where LL is the transmission distance, η\eta is the total transmission loss. For simplicity, we use three states on each side, namely the signal state, decoy state, and vacuum state, denoted as 1,2,3 on Alice’s side, and 4,5,6 on Bob’s side.

The simulation parameters are as follows: The fiber loss is α1=0.2db/km\alpha_{1}=0.2~{}\textrm{db/km}. Other losses excluding the fibre loss is η1=0.045\eta_{1}=0.045. The misalignment error rate is ed=0.033e_{d}=0.033. The error correction efficiency is f=1.16f=1.16. The dark count is Y0=1.7×106Y_{0}=1.7\times 10^{-6}.

To estimate the gain and the error rate, we exploit Eqs. (IV.2) to (43) in the main text. Then the intensities of the signal state and the decoy state are optimized to maximize the key rate.

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