This paper was converted on www.awesomepapers.org from LaTeX by an anonymous user.
Want to know more? Visit the Converter page.

Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars

Abstract

The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks. However, just like classical detection, evasive behaviors also exist in quantum detection. In this paper, we formulate an adversarial quantum detection scenario where the detector is passive and does not know the quantum states have been distorted by an attacker. We compare the performance of a passive detector with the one of a non-adversarial detector to demonstrate how evasive behaviors can undermine the performance of quantum detection. We use a case study of target detection with quantum radars to corroborate our analytical results.

Index Terms—  Quantum detection, quantum man-in-the-middle attack, quantum radars,

1 Introduction

The past two decades have witnessed a booming development of theories and applications of quantum systems. The discipline of quantum signal processing (QSP) [1] was founded under the construction of quantum mechanics frameworks [2] and studies the manipulation, detection and restoration of quantum signals. One important branch of study of quantum systems is the detection and discrimination of states. In quantum signal processing, detecting the quantum state with good accuracy lays a foundation for many signal processing problems including the reconstruction of the original quantum state.

One challenge in designing quantum detection systems results from undesirable interactions [3] between the target mixed states and the exogenous environments. In particular, the quantum states can be manipulated adversarially by malicious attackers, who undermine the detection performance significantly. For instance, a quantum man-in-the-middle attack [4] jeopardizes the encryption process in quantum key distribution. There is a need to formulate the adversarial manipulations of quantum states for quantum detection systems.

In this paper, we develop a game-theoretical framework to study how a strategic attacker can undermine the performance of a quantum detector by distorting the quantum states produced by the quantum system. Game-theoretic frameworks have been widely implemented in studying attacker-defender relationships in cyber-security [5, 6]. In a generic quantum detection characterized by hypothesis testing scenario [7], the detector (He) receives a sample of quantum states drawn from an unknown ensemble characterized by a density operator. The detector aims to make a decision regarding the genuine density operator that produces the quantum state. In our adversarial scenario, an attacker (She) observes the true density operator, intercepts the original quantum states, and sends strategically distorted quantum states to the ‘naive’ detector, who applies the decision rule as if the received quantum states were untainted. We model the relationship between the attacker and the naive detector as a Stackelberg game [8], where the attacker plays the role of a leader and the detector the follower.

Our analysis sheds light on fundamental limits on a quantum detector’s performance in adversarial situations. We observe that by varying the threshold, one can depict the detection rate and false alarm rate of a naive quantum detector in terms of a receiver-operational-characteristic (ROC) curve [7], which illustrates the detector performance. Compared to the curve for non-adversarial quantum detectors, the naive detector’s performances deteriorate significantly as the attacker attenuate the component of the mixed state that is projected on the detection region. Our results have clear implications for developing improved and attack-aware quantum detection systems that can combat strategically-designed quantum operations upon states. Our contributions are twofold:

The rest of the paper is organized as follows. In Section 2, we formulate man-in-the-middle-attack as a Stackelberg game and compute his optimal strategies. In Section 3, we illustrate our formulation with a case study in target detection using quantum radars of a particular type. We conclude in Section 4.

Notations

We denote \mathcal{H} as the Hilbert space (over the set of real numbers \mathbb{R}) and \mathcal{H}^{*} as its dual space. We inherit Dirac’s bra-ket notations [9] to denote generic quantum states: φ|,|φ\langle\varphi|\in\mathcal{H}^{*},|\varphi\rangle\in\mathcal{H}. Let B()B(\mathcal{H}) be the space of all positive, Hermitian and bounded operators from \mathcal{H} to itself. Let 𝒮\mathcal{S} be the subset of B()B(\mathcal{H}) such trace of its operators is 11. In addition, we denote 𝒱\mathcal{V} as the space of projection-valued measurements [2]. We denote 𝟏B()\mathbf{1}\in B(\mathcal{H}) as the identity operator. For any operator AB()A\in B(\mathcal{H}), we denote its conjugate transpose as AA^{\dagger}.

2 The formulation of a quantum man-in-the-middle (MITM) attacker

We begin by considering a non-adversarial detection formulation based on quantum hypothesis testing introduced in [7]. Suppose that there is an unknown target quantum system characterized by a density operator ρ𝒮()\rho\in\mathcal{S}(\mathcal{H}). We consider the binary hypothesis testing scenario, assuming that the nature of the system specifies two possible choices for ρ=ρ0\rho=\rho_{0} or ρ=ρ1\rho=\rho_{1}, each of which forms a hypothesis H0,H1H_{0},H_{1} as follows:

H0:ρ0=j=1drj0|ψjψj|,H1:ρ1=i=1dri1|φiφi|,H_{0}:\rho_{0}=\sum_{j=1}^{d}{r^{0}_{j}|\psi_{j}\rangle\langle\psi_{j}|},\;\;H_{1}:\rho_{1}=\sum_{i=1}^{d}{r^{1}_{i}|\varphi_{i}\rangle\langle\varphi_{i}|}, (1)

with iri1=jrj1=1,rj1,ri00\sum_{i}{r^{1}_{i}}=\sum_{j}{r^{1}_{j}}=1,\;r^{1}_{j},r^{0}_{i}\geqslant 0 and |ψj,|φi,i,j=1,2,,d|\psi_{j}\rangle,|\varphi_{i}\rangle\in\mathcal{H},\;i,j=1,2,\dots,d. We assume that {|ψj},{|φi}\{|\psi_{j}\rangle\},\{|\varphi_{i}\rangle\} span the Hilbert space \mathcal{H} of dimension dd. Notice that we do not assume that ρ0,ρ1\rho_{0},\rho_{1} commute; i.e. {|ψj}j=1d,{|φi}i=1d\{|\psi_{j}\rangle\}^{d}_{j=1},\{|\varphi_{i}\rangle\}^{d}_{i=1} may be two different bases of \mathcal{H}. The target system produces quantum states |φ|\varphi\rangle\in\mathcal{H} which are collected by a detector (He), who wants to arrive at a decision rule δ[0,1]\delta\in\mathcal{H}\rightarrow[0,1] on which is the genuine characterization of the target system in (1): δ(φ)=k,k{0,1},\delta(\varphi)=k,\;k\in\{0,1\}, when he thinks the hypothesis HkH_{k} holds true. According to the measurement postulate of quantum mechanics [2], the detector decides by applying a measurement Π1𝒱\Pi_{1}\in\mathcal{V} upon the received quantum state |φ|\varphi\rangle: δ(φ)=φ|Π1|φ\delta(\varphi)=\langle\varphi|\Pi_{1}|\varphi\rangle. The decision rule δ\delta, or equivalently the measurement Π1\Pi_{1}, leads to a detection rate PD:𝒱[0,1]P_{D}:\mathcal{V}\rightarrow[0,1] and a false alarm rate PF:𝒱[0,1]P_{F}:\mathcal{V}\rightarrow[0,1] as follows:

PD(Π1)=Tr(Π1ρ1),PF(Π1)=Tr(Π1ρ0).\displaystyle P_{D}(\Pi_{1})=\text{Tr}(\Pi_{1}\rho_{1}),\;\;P_{F}(\Pi_{1})=\text{Tr}(\Pi_{1}\rho_{0}). (2)

In Bayesian hypothesis testing formulation, the detector knows the probabilities that H1,H0H_{1},H_{0} hold true are c1,c0c_{1},\;c_{0} (c1+c0=1c_{1}+c_{0}=1) respectively. Referring to [7] and denoting τ=c1c0\tau=\frac{c_{1}}{c_{0}}, detector’s optimal solution measurement Π1\Pi^{*}_{1} can be designed as follows:

Π1(τ)=ηj>0|ηjηj|,\Pi^{*}_{1}(\tau)=\sum_{\eta_{j}>0}{|\eta_{j}\rangle\langle\eta_{j}|}, (3)

where {|ηj}j=1d\{|\eta_{j}\rangle\}^{d}_{j=1} are orthogonal eigenstates of ρ1τρ0\rho_{1}-\tau\rho_{0} with eigenvalues ηj\eta_{j}.

Refer to caption
Fig. 1: The scheme of quantum state detection with adversaries. The target system produces quantum states |φ|\varphi\rangle from different ensembles depending on the true hypothesis H0,H1H_{0},H_{1}. The attacker hijacks and replaces the clean quantum states with the distorted ones. One case study is the target detection of quantum radars [10].

2.1 Quantum man-in-the-middle attack: a Stackelberg game formulation

We now introduce the scenario (as in Figure 1) of adversarial quantum detection in which an attacker (She) stands between the target system and detector, intercepts the quantum state |φ|\varphi\rangle\in\mathcal{H} from the target systems and sends out strategically distorted signal |φ|\varphi^{\prime}\rangle\in\mathcal{H} to the detector to undermine his performance. We assume that the detector is passive and ‘naive’ about the distortion and designs his optimal measurement operation as if the quantum state |φ|\varphi^{\prime}\rangle were directly generated from the target system. Upon receiving |φ|\varphi^{\prime}\rangle, the detector designs an optimal decision rule δ^:[0,1]\hat{\delta}^{*}:\mathcal{H}\rightarrow[0,1] by measuring the quantum state |φ|\varphi^{\prime}\rangle from attacker with measurement, denoted as Π1\Pi_{1} as follows:

δ^(φ)=φ|Π1|φ.\hat{\delta}^{*}(\varphi^{\prime})=\langle\varphi^{\prime}|\Pi^{*}_{1}|\varphi^{\prime}\rangle.

Thus we let the detector’s cost function uB:𝒱×𝒮×𝒮u_{B}:\mathcal{V}\times\mathcal{S}\times\mathcal{S}\rightarrow\mathbb{R} be the Bayesian risk, which is a weight sum of counterfactual miss rate and counterfactual false alarm rate in (2) as if the quantum state were untainted:

minρ1,ρ0𝒮\displaystyle\underset{\rho^{\prime}_{1},\rho^{\prime}_{0}\in\mathcal{S}}{\min} uB(Π1,ρ1,ρ0)\displaystyle u_{B}(\Pi_{1},\rho^{\prime}_{1},\rho^{\prime}_{0}) (4)
minρ1,ρ0𝒮\displaystyle\Longleftrightarrow\underset{\rho^{\prime}_{1},\rho^{\prime}_{0}\in\mathcal{S}}{\min} c1Tr((𝟏Π1)ρ1)+c0Tr(Π1ρ0),\displaystyle c_{1}\text{Tr}((\mathbf{1}-\Pi_{1})\rho_{1})+c_{0}\text{Tr}(\Pi_{1}\rho_{0}),\;\;

which leads to his optimal measurement Π1\Pi^{*}_{1} as in (3). The attacker, after observing the true hypothesis (H1H_{1} or H0H_{0}) on the target systems, obtains the quantum state |φ|\varphi\rangle\in\mathcal{H}. Based on his observation on the true density state (ρ1\rho_{1} or ρ0\rho_{0}), the attacker designs quantum noisy operations E1,E0B(){E}^{1},E^{0}\in B(\mathcal{H}) acting upon the received state |φ|\varphi\rangle to create a distorted quantum state |φ|\varphi^{\prime}\rangle. According to [11], we formulate the resulting noisy density operators E0,E1E^{0},E^{1} as the ‘operator-sum’ representation as follows:

\displaystyle\mathcal{E} ={(E0,E1)B()2:kEklEkl=𝟏,l{0,1}}.\displaystyle=\{(E^{0},E^{1})\in{B}(\mathcal{H})^{\otimes 2}:\sum_{k}{E^{l}_{k}E^{l\dagger}_{k}}=\mathbf{1},\;\;l\in\{0,1\}\}. (5)

We could treat the space \mathcal{E} in (5) as the attacker’s action space. Yet we argue through the following lemma that it is equivalent to characterize the attacker’s strategies as a pair of density operators ρ1,ρ0\rho^{\prime}_{1},\rho^{\prime}_{0}.

Lemma 1 (Equivalency of attacker’s strategies)

Let ρ1,ρ0\rho_{1},\rho_{0} be the two density operators in (1). Then, for any ρ1,ρ0𝒮\rho^{\prime}_{1},\rho^{\prime}_{0}\in\mathcal{S} there exist operations E1,E0{E}^{1},{E}^{0}\in\mathcal{E} of the operator-sum representation such that E1(ρ1)=ρ1,E0(ρ0)=ρ0{E}^{1}(\rho_{1})=\rho^{\prime}_{1},\;{E}^{0}(\rho_{0})=\rho^{\prime}_{0}.

Knowing detector’s strategy (3) the detector designs her optimal strategies ρ1,ρ0\rho^{\prime*}_{1},\rho^{\prime*}_{0} by minimizing her utility function uA:𝒮×𝒮×𝒱u_{A}:\mathcal{S}\times\mathcal{S}\times\mathcal{V}\rightarrow\mathbb{R} as follows:

minρ0,ρ1𝒮\displaystyle\underset{\rho^{\prime}_{0},\rho^{\prime}_{1}\in\mathcal{S}}{\min} uA(ρ1,ρ0,Π1),\displaystyle\;{u_{A}(\rho^{\prime}_{1},\rho^{\prime}_{0},\Pi^{*}_{1})}, (6)
minρ0,ρ1𝒮\displaystyle\Longleftrightarrow\underset{\rho^{\prime}_{0},\rho^{\prime}_{1}\in\mathcal{S}}{\min} Tr(Π1ρ1)+λ[S(ρ1ρ1)+S(ρ0ρ0)],\displaystyle\;\text{Tr}(\Pi^{*}_{1}\rho^{\prime}_{1})+\lambda[S(\rho^{\prime}_{1}\|\rho_{1})+S(\rho^{\prime}_{0}\|\rho_{0})],

where we adopt the Von-Neumann relative entropy [11] for any two density operators ν1,ν0𝒮\nu_{1},\nu_{0}\in\mathcal{S} as S(ν1ν0):=Tr(ν1(lnν1lnν0))S(\nu_{1}\|\nu_{0}):=\text{Tr}(\nu_{1}(\ln\nu_{1}-\ln\nu_{0})), and Π1\Pi^{*}_{1} is the solution to detector’s optimization problem (4).

In the objective function of (6), the attacker trades off between undermining the genuine detection rate (the first term) and minimizing the loss incurred from distorting quantum states through noisy gates (the second term). Such a loss term originates from the detector’s awareness of the distortion if the received quantum states deviate significantly from the ‘normal’ ones. The parameter λ+\lambda\in\mathbb{R}_{+} is a regularization parameter characterizing the attacker’s intentions.

Summarizing the formulations raised in (4) and (6), we arrive at the game between passive quantum detector and the attacker 𝒢\mathcal{G}, which is a Stackelberg game [8] defined as follows.

Definition 1

We define the relationship between the passive quantum detector and the attacker as a Stackelberg game 𝒢\mathcal{G} with the following tuples

𝒢=,Θ,,,𝒱,uA,uB,\mathcal{G}=\langle\mathcal{I},\Theta,\mathcal{H},\mathcal{E},\mathcal{V},u_{A},u_{B}\rangle, (7)

where ={Attacker,Detector}\mathcal{I}=\{\text{Attacker},\;\text{Detector}\} refers to the set of players; Θ={H0,H1}\Theta=\{H_{0},H_{1}\} refers to the set of true hypotheses specified in (1); \mathcal{H} is the Hilbert space; the set \mathcal{E} in (5) characterizes the attacker’s strategy space; the set of measurements 𝒱\mathcal{V} characterizes the detector’s space of decision rules. The functions uA,uBu_{A},u_{B} specified in (4) and (6) the objectives of the attacker and the detector, respectively.

We now state the attacker’s optimal design of distorted mixed densities ρ1,ρ0\rho^{\prime*}_{1},\rho^{\prime*}_{0} into the following proposition:

Proposition 1 (Attacker’s optimal strategies)

Let 𝒢\mathcal{G} be the Stackelberg game between the attacker and the detector (the passive quantum detector) as mentioned in definition 1. Then the attacker’s optimal strategies ρ1,ρ0\rho^{\prime*}_{1},\rho^{\prime*}_{0} are expressed as follows:

ρ0\displaystyle\rho^{\prime*}_{0} =ρ0,\displaystyle=\rho_{0}, (8)
ρ1\displaystyle\rho^{\prime*}_{1} =exp(lnρ11λΠ1)Tr(exp(lnρ11λΠ1)).\displaystyle=\frac{\exp(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1})}{\text{Tr}(\exp(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1}))}. (9)

We have several remarks on the attacker’s and the detector’s optimal strategies. First, as λ0\lambda\rightarrow 0, the penalty for distorting the mixed state vanishes, and the attacker manages to suppress all the components in Π1\Pi_{1}. On the other hand, when λ\lambda\rightarrow\infty, the attacker’s optimal strategies ρ1=ρ1\rho^{\prime*}_{1}=\rho_{1}, meaning the attacker does not distort the original density operator at all because the penalty for distorting the mixed states becomes infinitely high.

Secondly, we can interpret the attacker’s optimal manipulation of mixed density states as in Proposition (1) as the attenuation of the components of the original quantum state ρ1\rho_{1} upon the subspace spanned by the base states {|ηi}i\{|\eta_{i}\rangle\}_{i} lying in the region of detection. Applying Baker-Campbell-Hausdorf formula to the nominator of the RHS, of (9) we obtain

ρ1=ρ1e1λΠ1e12λ[lnρ1,Π1]+Tr(exp(lnρ11λΠ1)),\rho^{\prime*}_{1}=\frac{\rho_{1}e^{-\frac{1}{\lambda}\Pi^{*}_{1}}e^{-\frac{1}{2\lambda}[\ln\rho_{1},\Pi^{*}_{1}]+\dots}}{\text{Tr}(\exp(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1}))}, (10)

where “\dots” refers to additional terms involving iterative Poisson brackets of lnρ1\ln\rho_{1} and 1λΠ1\frac{1}{\lambda}\Pi_{1}. The product ρ1e1λΠ1\rho_{1}e^{-\frac{1}{\lambda}\Pi^{*}_{1}} indicates the attenuation of the original mixed state ρ1\rho_{1} on the subspace controlled by the parameter 1λ\frac{1}{\lambda}. The the remaining factor e12λ[lnρ1,Π1]+e^{-\frac{1}{2\lambda}[\ln\rho_{1},\Pi_{1}]+\dots}, where the commutator [lnρ1,Π1][\ln\rho_{1},\Pi_{1}] is included in the exponent, implies that the quantum uncertainty principle also affects attacker’s optimal strategies.

Thirdly, when ρ1,Π1\rho_{1},\Pi^{*}_{1} commute (a sufficient condition would be ρ1,ρ0\rho_{1},\rho_{0} commute), the attacker’s optimal strategies (8)(9) reduce to the strategies in classical Stackelberg hypothesis testing games as formulated in Section II of [12].

We can now compute the genuine detection rate P¯D:𝒱[0,1]\bar{P}_{D}:\mathcal{V}\rightarrow[0,1] under the attacker’s manipulation:

P¯D(Π1)=Tr(Π1ρ1),P¯F(Π1)=Tr(Π1ρ0),\bar{P}_{D}(\Pi^{*}_{1})=\text{Tr}(\Pi^{*}_{1}\rho^{\prime*}_{1}),\;\bar{P}_{F}(\Pi^{*}_{1})=\text{Tr}(\Pi^{*}_{1}\rho^{\prime*}_{0}), (11)

where ρ1\rho^{\prime*}_{1} is obtained from (9). We have the following statement:

Proposition 2

Let (ρ0,ρ1,Π1)(\rho^{\prime*}_{0},\rho^{\prime*}_{1},\Pi^{*}_{1}) be the optimal strategy profile for the Stackelberg game 𝒢\mathcal{G} derived in (8)(9) and (4), respectively. Recall PD,P¯DP_{D},\bar{P}_{D} as the counterfactual and genuine detection rates defined in (2) and (11), respectively. Then, under some technical assumptions we have:

PD(Π1)e1λP¯D(Π1)PD(Π1),Π1𝒱.\;P_{D}(\Pi^{*}_{1})e^{-\frac{1}{\lambda}}\leqslant\bar{P}_{D}(\Pi^{*}_{1})\leqslant P_{D}(\Pi^{*}_{1}),\;\forall\Pi^{*}_{1}\in\mathcal{V}. (12)

Proposition 2 characterizes an upper and lower bound of the genuine detection rate under strategically designed quantum operations.

3 Case study: target detection using quantum radars

We now apply the formulation of the quantum man-in-the-middle attack discussed in Section 2 to study spoofing in quantum radar detection. A quantum radar is a standoff detection system using photons to explore some quantum phenomena to strengthen its capacity to detect targets of interest. In this section, we assume that our quantum radar generates non-entangled, monochromatic, coherent [13] photonic quantum states subject to noise in line with Llyod’s theory [14].

We illustrate the MITM attack scheme in Figure 1: a quantum radar (detector) determines on the absence or presence of the target object based on the reflective photon-based quantum states. An attacker blocks the transmission of reflective quantum signals, manipulates them through noisy quantum gates selected from the action space in (5), which can be implemented using photonic quantum circuits [15], and sends out manipulated quantum signals. We use the mean number representation to characterize photonic quantum states from the reflective signals [16]. We associate the hypotheses H0,H1H_{0},H_{1} with the mixed state of reflective signals under the absence H0H_{0} or presence H1H_{1} of the target object in Figure 1, respectively as follows:

H0:ρ0\displaystyle H_{0}:\rho_{0} =(1NB)|00|+NB|kk|,\displaystyle=(1-N_{B})|0\rangle\langle 0|+N_{B}{|k\rangle\langle k|}, (13)
H1:ρ1\displaystyle H_{1}:\rho_{1} =(1x)((1NB)|00|+NB|kk|)+x|ll|,\displaystyle=(1-x)\left((1-N_{B})|0\rangle\langle 0|+N_{B}{|k\rangle\langle k|}\right)+x|l\rangle\langle l|,

where x[0,1]x\in[0,1] refers to the reflective index, and NB[0,1]N_{B}\in[0,1] characterizes the noise of the environment.

Based on the true hypothesis H0,H1H_{0},H_{1} and the input quantum state, the spoofer/attacker produces distorted quantum mixed densities ρ0,ρ1\rho^{\prime*}_{0},\rho^{\prime*}_{1} as in (6). We choose NB=0.4,l=2,k=1,x=0.9N_{B}=0.4,l=2,k=1,x=0.9 in (13) and depict in Figure 2 the relationship between the detection rate in terms the mean number of photons under attacks parameterized by different choices of λ\lambda. We observe that no matter the distortion level, the quantum detector reaches its worst detection rates when the mean photon number is around 0.620.62, and its second worst rate is at 1.691.69. Nevertheless, different distortion levels cause a contrast in detection rates among all mean photon number states. In Figure 3, we plot the receiver-operational-characteristic (ROC) curves of classical quantum detectors and passive quantum detectors when the attacker’s manipulation strategy is controlled under different choices of λ\lambda. We observe that the passive quantum detectors perform worse than the classical quantum detectors because the spoofer manipulate the states to undermine the detection rates. Lower values of λ\lambda lead to stronger distortion of states and thus worse performance in terms of the ROC curves.

Refer to caption
Fig. 2: The genuine detection rate P¯D\bar{P}_{D} in (11) in terms of the mean photon number of the given mixed state ρ1\rho_{1} in (13) under different attacks. We fix the detector’s threshold τ=1\tau=1, NB,k,xN_{B},k,x and let ll vary. The genuine detection rates become low when the mean photon number is close to kk, suggesting the detector performs the worst when the reflective signal resembles the noise.
Refer to caption
Fig. 3: The ROC curves of the naive quantum detector under distorted state ρ1\rho^{\prime*}_{1} parameterized by different values of parameter λ\lambda in (9). We choose intrinsic coefficients of the detector to be NB=0.4,l=2,k=1,x=0.9N_{B}=0.4,l=2,k=1,x=0.9 in (13).

4 Conclusion

We have formulated the quantum man-in-the-middle attack under the Stackelberg framework to study the fundamental limit of the reduction of the detection performance. We characterize the attacker’s optimal strategies as a suppression of the target’s density operator on the region of detection, which is reshaped by the quantum effects. We show that a passive quantum detector suffers an exponential reduction of detection rate in the worst case. Our numerical results also imply how adversarial attacks affect the performance of quantum radars.

One direction to extend our adversarial quantum detection framework would be to consider various detection frameworks such as side-channel attack detection [17]. It would be also possible to consider hybrid detection models taking advantage of both classical and quantum information as inputs.

References

  • [1] Yonina C Eldar and Alan V Oppenheim, “Quantum signal processing,” IEEE Signal Processing Magazine, vol. 19, no. 6, pp. 12–32, 2002.
  • [2] John Von Neumann, Mathematical foundations of quantum mechanics: New edition, Princeton university press, 2018.
  • [3] Adel Sohbi, Isabelle Zaquine, Eleni Diamanti, and Damian Markham, “Decoherence effects on the nonlocality of symmetric states,” Physical Review A, vol. 91, no. 2, pp. 022101, 2015.
  • [4] Yang-Yang Fei, Xiang-Dong Meng, Ming Gao, Hong Wang, and Zhi Ma, “Quantum man-in-the-middle attack on the calibration process of quantum key distribution,” Scientific reports, vol. 8, no. 1, pp. 1–10, 2018.
  • [5] Quanyan Zhu and Tamer Basar, “Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems,” IEEE Control Systems Magazine, vol. 35, no. 1, pp. 46–65, 2015.
  • [6] Yunhan Huang, Juntao Chen, Linan Huang, and Quanyan Zhu, “Dynamic games for secure and resilient control system design,” National Science Review, vol. 7, no. 7, pp. 1125–1141, 2020.
  • [7] Carl.W.Helsotrom, Quantum Detection and Estimation Theory, ISSN. Elsevier Science, 1976.
  • [8] Heinrich Von Stackelberg, Market structure and equilibrium, Springer Science & Business Media, 2010.
  • [9] Paul Adrien Maurice Dirac, The principles of quantum mechanics, Number 27. Oxford university press, 1981.
  • [10] Gregory Slepyan, Svetlana Vlasenko, Dmitri Mogilevtsev, and Amir Boag, “Quantum radars and lidars: Concepts, realizations, and perspectives,” IEEE Antennas and Propagation Magazine, vol. 64, no. 1, pp. 16–26, 2021.
  • [11] Michael A Nielsen and Isaac Chuang, Quantum computation and quantum information, Cambridge University Press, 2010.
  • [12] Yinan Hu and Quanyan Zhu, “Game-theoretic neyman-pearson detection to combat strategic evasion,” arXiv preprint arXiv:2206.05276, 2022.
  • [13] Ricardo Gallego Torromé, Nadya Ben Bekhti-Winkel, and Peter Knott, “Introduction to quantum radar,” arXiv preprint arXiv:2006.14238, 2020.
  • [14] Seth Lloyd, “Enhanced sensitivity of photodetection via quantum illumination,” Science, vol. 321, no. 5895, pp. 1463–1465, 2008.
  • [15] Jeremy L O’brien, “Optical quantum computing,” Science, vol. 318, no. 5856, pp. 1567–1570, 2007.
  • [16] Anthony Mark Fox, Mark Fox, et al., Quantum optics: an introduction, vol. 15, Oxford university press, 2006.
  • [17] Florian Lemarchand, Cyril Marlin, Florent Montreuil, Erwan Nogues, and Maxime Pelcat, “Electro-magnetic side-channel attack through learned denoising and classification,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 2882–2886.
  • [18] P.D. Lax, Linear Algebra and Its Applications, Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts. Wiley, 2007.
  • [19] Tosio Kato, Perturbation theory for linear operators, vol. 132, Springer Science & Business Media, 2013.

5 Appendix

5.1 Proof of Lemma 1

Proof. It suffices to prove that ρ1\rho^{\prime}_{1} and E1E_{1} are equivalent in characterizing the attacker’s generic strategies when observing the hypothesis H1H_{1}. Without loss of generality we assume further that ρ1\rho_{1} is strictly positive, that is ri1>0r^{1}_{i}>0 for all ii. We prove two claims as follows.

Claim 1

For every ρ1𝒮\rho^{\prime}_{1}\in\mathcal{S}, there always exist a collection of operators {E11,E21,,Ed21}\{E^{1}_{1},E^{1}_{2},\dots,E^{1}_{d^{2}}\} such that

ρ1=E1(ρ1)=k=1d2Ek1ρ1Ek1.\rho^{\prime}_{1}=E^{1}(\rho_{1})=\sum_{k=1}^{d^{2}}{E^{1}_{k}\rho_{1}E^{1\dagger}_{k}}. (14)

Proof of Claim 1. Since we assume dim()=d\text{dim}(\mathcal{H})=d, we know the distorted density operator ρ1\rho^{\prime}_{1} has d2d^{2} degrees of freedom. Since ρ1𝒮\rho^{\prime}_{1}\in\mathcal{S}, we can parameterize them in the basis |φi|\varphi_{i}\rangle as well with the coefficients aija^{\prime}_{ij}. We can express them in terms of the basis {|φj}1d\{|\varphi_{j}\rangle\}^{d}_{1} as follows:

ρ1=i,j=1daij|φiφj|.\rho^{\prime}_{1}=\sum_{i,j=1}^{d}{a^{\prime}_{ij}|\varphi_{i}\rangle\langle\varphi_{j}|}. (15)

The impact of the quantum operations upon ρ1\rho_{1}can be characterized by the change of coordinates of ρ1\rho_{1} into the ones of ρ1\rho^{\prime}_{1} under the basis |φiφj||\varphi_{i}\rangle\langle\varphi_{j}|. Now the operations {Ek1}\{E^{1}_{k}\} turn the coordinates from aija_{ij} into aija^{\prime}_{ij}, respectively. Under the representation (or basis) of {|φj}1d\{|\varphi_{j}\rangle\}^{d}_{1}, we can select the matrix representation of Ek1,Ek0E^{1}_{k},E^{0}_{k}, where k=1,2,,d2k=1,2,\dots,d^{2} as follows:

Ek1\displaystyle E^{1}_{k} =[000aijaij000].\displaystyle=\begin{bmatrix}0&\ldots&\ldots&\ldots&0\\ 0&\ldots&\sqrt{\frac{a^{\prime}_{ij}}{a_{ij}}}&\ldots&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&\ldots&\ldots&\ldots&0\end{bmatrix}.\; (16)

(Ek1)i(k),j(k)=aijaij(E^{1}_{k})_{i(k),j(k)}=\sqrt{\frac{a^{\prime}_{ij}}{a_{ij}}}\; where i(k),j(k)i(k),j(k) refers to the row index and column index corresponding to the subscript kk. The rest of the entries for Ek1E^{1}_{k} are all zero. Then it is clear to verify that

E1(ρ1)=ρ1E^{1}({\rho_{1}})=\rho^{\prime}_{1} (17)

as claimed.

Also, we have the following conclusion.

Claim 2

Let ρ1S\rho_{1}\in S be a density operator. For every E1B()E^{1}\in B(\mathcal{H}), we can find a ρ1𝒮\rho^{\prime}_{1}\in\mathcal{S} such that

ρ1=k=1d2Ek1ρ1Ek1.\rho^{\prime}_{1}=\sum_{k=1}^{d^{2}}{E^{1}_{k}\rho_{1}E^{1\dagger}_{k}}. (18)

Proof of claim 2. Our goal is to prove that ρ1\rho^{\prime}_{1} obtained in (18) meets the requirements of a density operator, that is, it has a trace of 11; it is positive definite; it is symmetric (or Hermitian). We first of all notice

Tr(ρ1)\displaystyle\text{Tr}(\rho^{\prime}_{1}) =Tr(k=1d2Ek1ρ1Ek1)=k=1d2Tr(Ek1ρ1Ek1)\displaystyle=\text{Tr}\left(\sum_{k=1}^{d^{2}}{E^{1}_{k}\rho_{1}E^{1\dagger}_{k}}\right)=\sum_{k=1}^{d^{2}}\text{Tr}(E^{1}_{k}\rho_{1}E^{1\dagger}_{k}) (19)
=k=1d2Tr(Ek1Ek1ρ1)=Tr(k=1d2Ek1Ek1ρ1)\displaystyle=\sum_{k=1}^{d^{2}}\text{Tr}(E^{1\dagger}_{k}E^{1}_{k}\rho_{1})=\text{Tr}\left(\sum_{k=1}^{d^{2}}{E^{1\dagger}_{k}}E^{1}_{k}\rho_{1}\right)
=Tr[(k=1d2Ek1Ek1)ρ1]\displaystyle=\text{Tr}[\left(\sum_{k=1}^{d^{2}}{E^{1\dagger}_{k}}E^{1}_{k}\right)\rho_{1}]

Noticing that k=1d2Ek1Ek1=𝟏\sum_{k=1}^{d^{2}}{E^{1\dagger}_{k}}E^{1}_{k}=\mathbf{1}, we conclude

Tr(ρ1)=Tr(ρ1)=1.\text{Tr}(\rho^{\prime}_{1})=\text{Tr}(\rho_{1})=1. (20)

Next we prove symmetry of ρ1\rho^{\prime}_{1}. For convenience we denote ρij,ρij\rho_{ij},\rho_{ij} as the matrix entries of ρ1,ρ1\rho_{1},\rho^{\prime}_{1} respectively. It suffices to assume that every matrix Ek1E^{1}_{k} is written in the form similar as the one in (16). We want to show that

ω(j,k)=Ei1j1ρ1Ei2j2+Ei2j2ρ1Ei1j1\omega(j,k)=E_{i_{1}j_{1}}\rho_{1}E^{\dagger}_{i_{2}j_{2}}+E_{i_{2}j_{2}}\rho_{1}E^{\dagger}_{i_{1}j_{1}} (21)

is also symmetric for all choices of i1,i2,j1,j2di_{1},i_{2},j_{1},j_{2}\leqslant d. Here Ei1j1E_{i_{1}j_{1}} is a dd by dd matrix where every entry is zero except (i1,j1)(i_{1},j_{1}). By computing entry-wisely, we notice that

[Ei1j1ρ1Ei2j2]kl={ai1j1ρi1i2ai2j2ifk=i1,k=i2,0else[E_{i_{1}j_{1}}\rho_{1}E^{\dagger}_{i_{2}j_{2}}]_{kl}=\begin{cases}a_{i_{1}j_{1}}\rho_{i_{1}i_{2}}a_{i_{2}j_{2}}&\text{if}\;k=i_{1},\;k=i_{2},\\ 0&\mbox{else}\end{cases} (22)

Similarly,

[Ei2j2ρ1Ei1j1]kl={ai2j2ρi2i1ai1j1ifk=i2,k=i1,0else.[E_{i_{2}j_{2}}\rho_{1}E^{\dagger}_{i_{1}j_{1}}]_{kl}=\begin{cases}a_{i_{2}j_{2}}\rho_{i_{2}i_{1}}a_{i_{1}j_{1}}&\text{if}\;k=i_{2},\;k=i_{1},\\ 0&\mbox{else}.\end{cases} (23)

Substituting (22) and (23) into (21) and considering ρi1i2=ρi2i1\rho_{i_{1}i_{2}}=\rho_{i_{2}i_{1}} due to symmetry of ρ1\rho_{1}, we get ω(j,k)\omega(j,k) is indeed symmetric. Since every matrix can be written as a linear combination of {Ej}1jd2\{E_{j}\}_{1\leqslant j\leqslant d^{2}}, we get that Ek1ρ1Ek1E^{1}_{k}\rho_{1}E^{1}_{k} is symmetric for all 1kd21\leqslant k\leqslant d^{2}. As a consequence, ρ1\rho^{\prime}_{1} as expressed in (14) is symmetric too.

Finally we can show that ρ10\rho^{\prime}_{1}\geqslant 0. Denote γk=Ek1ρ1Ek1\gamma_{k}=E^{1}_{k}\rho_{1}E^{1\dagger}_{k}. Since ρ10\rho_{1}\geqslant 0, we know by the property of positive definite matrix [18] that γk\gamma_{k} are all positive definite matrices for all kk. Again by the property of closure under additivity we conclude that ρ1=k=1d2γk\rho^{\prime}_{1}=\sum_{k=1}^{d^{2}}{\gamma_{k}} is a positive definite operator.

Summarizing claim 1 and claim 2, we conclude that it is equivalent to characterize the attacker’s action by E1B()E^{1}\in B(\mathcal{H}) or by ρ1𝒮\rho^{\prime}_{1}\in\mathcal{S}. Similar arguments can be made regarding E0B()E^{0}\in B(\mathcal{H}) and ρ0𝒮\rho^{\prime}_{0}\in\mathcal{S}. This concludes the proof of lemma 1.   

5.2 Proof of Proposition 1

Proof. We obtain the attacker’s optimal strategies by solving the optimization problem (6). First of all, we can make sure that the optimal strategies ρ1,ρ0\rho^{\prime*}_{1},\rho^{\prime*}_{0} exist. Since the objective function uAu_{A} is convex in terms of ρ1\rho_{1} and ρ0\rho_{0}, We apply first-order conditions by taking partial derivatives of uAu_{A} in terms of ρ0,ρ1\rho^{\prime}_{0},\rho^{\prime}_{1} and set them to be zero, respectively:

0\displaystyle 0 uAρ1=Π1+λ(lnρ1lnρ1),\displaystyle\equiv\frac{\partial u_{A}}{\partial\rho^{\prime}_{1}}=\Pi^{*}_{1}+\lambda(\ln\rho^{\prime}_{1}-\ln\rho_{1}), (24)
0\displaystyle 0 uAρ0=lnρ0lnρ0,\displaystyle\equiv\frac{\partial u_{A}}{\partial\rho^{\prime}_{0}}=\ln\rho^{\prime}_{0}-\ln\rho_{0}, (25)

with equality and inequality constraints, which are requirements for ρ1,ρ0\rho^{\prime*}_{1},\rho^{\prime*}_{0} being density operators, as follows:

Tr(ρ1)=1,Tr(ρ0)=0,\displaystyle\text{Tr}(\rho^{\prime}_{1})=1,\;\text{Tr}(\rho^{\prime}_{0})=0, (26)
ρ1=ρ1T,ρ0=ρ0T,\displaystyle\rho^{\prime}_{1}=\rho^{\prime T}_{1},\;\rho^{\prime}_{0}=\rho^{\prime T}_{0},
ρ10,ρ00.\displaystyle\rho^{\prime}_{1}\geqslant 0,\;\rho^{\prime}_{0}\geqslant 0.

By solving (24) and (25) we obtain

ρ1\displaystyle\rho^{\prime*}_{1} =1Z1exp(lnρ11λΠ1),\displaystyle=\frac{1}{Z_{1}}\exp(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1}), (27)
ρ0\displaystyle\rho^{\prime*}_{0} =1Z0ρ0,\displaystyle=\frac{1}{Z_{0}}\rho_{0},

where Z1,Z0Z_{1},Z_{0} are normalization constants. Referring to the equality constraints we conclude that Z0=1Z_{0}=1 and Z1=Tr(exp(lnρ11λΠ1))Z_{1}=\text{Tr}(\exp(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1})) and arrive at the the solution in (8)(9).   

5.3 Proof of Proposition 2

We make the following assumptions:

Assumption 1

We assume the following conditions in proposition 2:

  1. 1.

    ri1>0r^{1}_{i}>0 and arranges in a strictly descending order (every eigenvalue is algebraically simple) in terms of ii;

  2. 2.

    The basis {|φi}\{|\varphi_{i}\rangle\} is orthonormal;

  3. 3.

    The eigenstates |ηj|\eta_{j}\rangle are orthonormal; The eigenstates |ηj|\eta_{j}\rangle are also arranged in a descending order regarding the eigenvalues of ρ1τρ0\rho_{1}-\tau\rho_{0}; The eigenvalues ηj\eta_{j} are positive whenever jk0j\geqslant k_{0};

  4. 4.

    The difference between eigenvalues are much larger than the projection:

    ji,|φi|Π1|φjri1rj1|<1\forall j\neq i,\;\;\sum\Big{|}\frac{\langle\varphi_{i}|\Pi^{*}_{1}|\varphi_{j}\rangle}{r^{1}_{i}-r^{1}_{j}}\Big{|}<1 (28)

The assumptions above make sense since ρ1\rho_{1} is positive definite, symmetric operator. Also, the detector’s optimal strategy Π1\Pi^{*}_{1} is a projection operator with finite rank. We can now state the proof.

Proof. We adopt the classic perturbation theory for symmetric finite-dimensional linear operators [19]. Let |αj|\alpha_{j}\rangle be the eigenstates of the operator lnρ11λΠ1\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1} with eigenvalue eαje^{\alpha_{j}}. Then we can write

exp(lnρ11λΠ1)=jeαj|αjαj|.\exp\left(\ln\rho_{1}-\frac{1}{\lambda}\Pi^{*}_{1}\right)=\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|}. (29)

Therefore

P¯D(Π1)=Tr(Π1ρ1)\displaystyle\bar{P}_{D}(\Pi^{*}_{1})=\text{Tr}(\Pi^{*}_{1}\rho^{\prime*}_{1}) (30)
=1Z1Tr(kk0|ηkηk|jeαj|αjαj|)\displaystyle=\frac{1}{Z_{1}}\text{Tr}\Big{(}\sum_{k\geqslant k_{0}}{|\eta_{k}\rangle\langle\eta_{k}|}\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|}\Big{)}
=1Z1Tr(kk0|ηkηk|i|ηiηi|jeαj|αjαj|i|ηiηi|)\displaystyle=\frac{1}{Z_{1}}\text{Tr}\Big{(}\sum_{k\geqslant k_{0}}{|\eta_{k}\rangle\langle\eta_{k}|}\sum_{i}{|\eta_{i}\rangle\langle\eta_{i}|}\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|}\sum_{i^{\prime}}{|\eta_{i^{\prime}}\rangle\langle\eta_{i^{\prime}}|}\Big{)}
=1Z1Tr(kk0,i|ηk(jeαj|αjαj|ηi)ηi|)\displaystyle=\frac{1}{Z_{1}}\text{Tr}\Big{(}\sum_{k\geqslant k_{0},i}{|\eta_{k}\rangle\Big{(}\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|\eta_{i^{\prime}}\rangle\Big{)}\langle\eta_{i^{\prime}}|}}\Big{)}
=jeαjjeαjkk0|αj|ηk|2\displaystyle=\sum_{j}{\frac{e^{\alpha_{j}}}{\sum_{j^{\prime}}{e^{\alpha_{j^{\prime}}}}}\sum_{k\geqslant k_{0}}{|\langle\alpha_{j}|\eta_{k}\rangle}|^{2}}

On the other hand, we know

PD(Π1)=Tr(Π1ρ1)\displaystyle P_{D}(\Pi^{*}_{1})=\text{Tr}(\Pi^{*}_{1}\rho_{1}) (31)
=Tr(kk0|ηkηk|jeαj|αjαj|)\displaystyle=\text{Tr}\Big{(}\sum_{k\geqslant k_{0}}{|\eta_{k}\rangle\langle\eta_{k}|}\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|}\Big{)}
=Tr(kk0|ηkηk|i|ηiηi|jrj1|φjφj|i|ηiηi|)\displaystyle=\text{Tr}\Big{(}\sum_{k\geqslant k_{0}}{|\eta_{k}\rangle\langle\eta_{k}|}\sum_{i}{|\eta_{i}\rangle\langle\eta_{i}|}\sum_{j}{r^{1}_{j}|\varphi_{j}\rangle\langle\varphi_{j}|}\sum_{i^{\prime}}{|\eta_{i^{\prime}}\rangle\langle\eta_{i^{\prime}}|}\Big{)}
=Tr(kk0,i|ηk(jeαj|αjαj|ηi)ηi|)\displaystyle=\text{Tr}\Big{(}\sum_{k\geqslant k_{0},i}{|\eta_{k}\rangle\Big{(}\sum_{j}{e^{\alpha_{j}}|\alpha_{j}\rangle\langle\alpha_{j}|\eta_{i^{\prime}}\rangle\Big{)}\langle\eta_{i^{\prime}}|}}\Big{)}
=jrj1kk0|φj|ηk|2.\displaystyle=\sum_{j}{r^{1}_{j}\sum_{k\geqslant k_{0}}{|\langle\varphi_{j}|\eta_{k}\rangle}|^{2}}.

Using the theories of perturbation, consider lnρ1\ln\rho_{1} as the unperturbed operator, Π1\Pi^{*}_{1} as the perturbation, with 1λ\frac{1}{\lambda} controlling the amplitude of the perturbation. Then the eigenvectors, under assumption 1, can be written as a series of 1λ\frac{1}{\lambda} as follows:

|αj=|φj1λkjφk|Π1|φjrk1rj1|φk+o(1λ2)|\alpha_{j}\rangle=|\varphi_{j}\rangle-\frac{1}{\lambda}\sum_{k^{\prime}\neq j}{\frac{\langle\varphi_{k^{\prime}}|\Pi^{*}_{1}|\varphi_{j}\rangle}{r^{1}_{k^{\prime}}-r^{1}_{j}}|\varphi_{k^{\prime}}\rangle}+o\left(\frac{1}{\lambda^{2}}\right) (32)

As a consequence,

|ηk|αj|2\displaystyle|\langle\eta_{k}|\alpha_{j}\rangle|^{2} (33)
=|ηk|φj|22ληk|kjφk|Π1|φjrk1rj1|φkηk|φj\displaystyle=|\langle\eta_{k}|\varphi_{j}\rangle|^{2}-\frac{2}{\lambda}\langle\eta_{k}|\sum_{k^{\prime}\neq j}{\frac{\langle\varphi_{k^{\prime}}|\Pi^{*}_{1}|\varphi_{j}\rangle}{r^{1}_{k^{\prime}}-r^{1}_{j}}|\varphi_{k^{\prime}}\rangle}\langle\eta_{k}|\varphi_{j}\rangle
+o(1λ2)|ηk|φj|2.\displaystyle+o\left(\frac{1}{\lambda^{2}}\right)\leqslant|\langle\eta_{k}|\varphi_{j}\rangle|^{2}.

We can also write out the perturbed eigenvalue αj\alpha_{j} in terms of the unperturbed eigenvalue rj1r^{1}_{j} as well as the series of the scale 1λ\frac{1}{\lambda} as follows:

αj=lnrj11λφj|Π1|φj+o(1λ2).\alpha_{j}=\ln r^{1}_{j}-\frac{1}{\lambda}\langle\varphi_{j}|\Pi^{*}_{1}|\varphi_{j}\rangle+o\left(\frac{1}{\lambda^{2}}\right). (34)

As a result,

eαj=rj1exp(1λφj|Π1|φj+o(1λ2))e^{\alpha_{j}}=r^{1}_{j}\exp\left(-\frac{1}{\lambda}\langle\varphi_{j}|\Pi^{*}_{1}|\varphi_{j}\rangle+o\left(\frac{1}{\lambda^{2}}\right)\right) (35)

For sufficiently small 1/λ1/\lambda the higher order term o(1λ2)o(\frac{1}{\lambda^{2}}) vanishes. We get that for all kk0k\geqslant k_{0} and j=1,2,,dj=1,2,\dots,d,

eαjjeαj|αj|ηk|2\displaystyle\frac{e^{\alpha_{j}}}{\sum_{j^{\prime}}{e^{\alpha_{j^{\prime}}}}}|\langle\alpha_{j}|\eta_{k}\rangle|^{2} (36)
=rj1exp(1λφj|Π1|φj)jrj1exp(1λφj|Π1|φj)\displaystyle=\frac{r^{1}_{j}\exp\left(-\frac{1}{\lambda}\langle\varphi_{j}|\Pi^{*}_{1}|\varphi_{j}\rangle\right)}{\sum_{j^{\prime}}{r^{1}_{j^{\prime}}\exp\left(-\frac{1}{\lambda}\langle\varphi_{j^{\prime}}|\Pi^{*}_{1}|\varphi_{j^{\prime}}\rangle\right)}}
(|ηk|φj|22ληk|kjφk|Π1|φjrk1rj1|φkηk|φj)\displaystyle\left(|\langle\eta_{k}|\varphi_{j}\rangle|^{2}-\frac{2}{\lambda}\langle\eta_{k}|\sum_{k^{\prime}\neq j}{\frac{\langle\varphi_{k^{\prime}}|\Pi^{*}_{1}|\varphi_{j}\rangle}{r^{1}_{k^{\prime}}-r^{1}_{j}}|\varphi_{k^{\prime}}\rangle}\langle\eta_{k}|\varphi_{j}\rangle\right)

Denote

sj1\displaystyle s^{1}_{j} =rj1exp(1λφj|Π1|φj)jrj1exp(1λφj|Π1|φj)\displaystyle=\frac{r^{1}_{j}\exp\left(-\frac{1}{\lambda}\langle\varphi_{j}|\Pi^{*}_{1}|\varphi_{j}\rangle\right)}{\sum_{j^{\prime}}{r^{1}_{j^{\prime}}\exp\left(-\frac{1}{\lambda}\langle\varphi_{j^{\prime}}|\Pi^{*}_{1}|\varphi_{j^{\prime}}\rangle\right)}} (37)
=rj1exp(1λkk0|φj|ηk|2)jrj1exp(1λkk0|φj|ηk|2).\displaystyle=\frac{r^{1}_{j}\exp\left(-\frac{1}{\lambda}\sum_{k^{\prime}\geqslant k_{0}}{|\langle\varphi_{j}|\eta_{k^{\prime}}\rangle|^{2}}\right)}{\sum_{j^{\prime}}{r^{1}_{j^{\prime}}\exp\left(-\frac{1}{\lambda}\sum_{k^{\prime}\geqslant k_{0}}{|\langle\varphi_{j}|\eta_{k^{\prime}}\rangle|^{2}}\right)}}.

Noticing that jrj1=jsj1=1\sum_{j}{r^{1}_{j}}=\sum_{j}{s^{1}_{j}}=1. Also if for some j1,j2dj_{1},j_{2}\leqslant d, kk0|φj|ηk|2kk0|φj|ηk|2\sum_{k\geqslant k_{0}}{|\langle\varphi_{j}|\eta_{k}\rangle|^{2}}\leqslant\sum_{k\geqslant k_{0}}{|\langle\varphi_{j^{\prime}}|\eta_{k}\rangle|^{2}} is large, then we must have sj1sj1s^{1}_{j^{\prime}}\geqslant s^{1}_{j}. By generalization of AM-GM inequality we conclude that

jkk0eαjjeαj|αj|ηk|2jkk0rj1|φj|ηk|2.\sum_{j}{\sum_{k\geqslant k_{0}}{\frac{e^{\alpha_{j}}}{\sum_{j^{\prime}}{e^{\alpha_{j^{\prime}}}}}|\langle\alpha_{j}|\eta_{k}\rangle|^{2}}}\leqslant\sum_{j}{\sum_{k\geqslant k_{0}}{r^{1}_{j}|\langle\varphi_{j}|\eta_{k}\rangle|^{2}}}. (38)

Noticing the expression of PD(Π1)P_{D}(\Pi^{*}_{1}) in (31) and P¯D(Π1)\bar{P}_{D}(\Pi^{*}_{1}) in (30), we get

P¯D(Π1)PD(Π1).\bar{P}_{D}(\Pi^{*}_{1})\leqslant P_{D}(\Pi^{*}_{1}). (39)

On the other hand, sj1rj1e1λ11λ+1λ2s^{1}_{j}\geqslant\frac{r^{1}_{j}e^{-\frac{1}{\lambda}}}{1-\frac{1}{\lambda}+\frac{1}{\lambda^{2}}} for all jj. We also notice

|αj|ηk|2(11λ)|φj|ηk|2,j,kk0.|\langle\alpha_{j}|\eta_{k}\rangle|^{2}\geqslant(1-\frac{1}{\lambda})|\langle\varphi_{j}|\eta_{k}\rangle|^{2},\;\forall j,k\geqslant k_{0}. (40)

As a result, we have

P¯D(Π1)PD(Π1)e1λ,\bar{P}_{D}(\Pi^{*}_{1})\geqslant P_{D}(\Pi^{*}_{1})e^{-\frac{1}{\lambda}}, (41)

which concludes the proof.