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Detection of Network and Genuine Network Quantum Steering

Zhihua Chen    Kai Wu School of Science, Jimei University, Xiamen 361021,China    Shao-Ming Fei School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
Abstract

The quantum network correlations play significant roles in long distance quantum communication, quantum cryptography and distributed quantum computing. Generally it is very difficult to characterize the multipartite quantum network correlations such as nonlocality, entanglement and steering. In this paper, we propose the network and the genuine network quantum steering models from the aspect of probabilities in the star network configurations. Linear and nonlinear inequalities are derived to detect the network and genuine network quantum steering when the central party performs one fixed measurement. We show that our criteria can detect more quantum network steering than that from the violation of the nn-locality quantum networks. Moreover, it is shown that biseparable assemblages can demonstrate genuine network steering in the star network configurations.

I Introduction

Strong quantum correlations like quantum nonlocality in quantum networks can be established among distant parties sharing physical resources emitted by independent sources. The quantum network is important in quantum information processing such as quantum cryptography, quantum communication and distributed quantum computation. Compared with the case of the parties sharing quantum entanglement from a single common source, it is much more difficult to characterize the correlations in quantum networks because of the non-convexity of the local network spaces. In quantum network scenarios, the parties hold the physical systems from different sources which are assumed to be independent with each other. Entanglement swapping is the simplest network scenario, where Alice, Bob and Charlie share two independent sources. When Bob performs the joint measurements on his two parties, Alice and Charlie can share the entanglement [1]. In the bilocality scenario Alice and Charlie admit two independent hidden variables. It is then generalized to the nn-locality scenarios.

Many efficient methods have been proposed to detect the quantum nonlocality in quantum networks, such as the explicit parametrization of network local models [2, 3], hierarchies of relaxations of the sets of compatible correlations [4], inflation technique [6, 5], network Bell inequalities [8, 7, 13, 9, 10, 11] and numerical approaches [15, 14].

Motivated by the quantum network nonlocality, the network quantum steering has been proposed in [16], in which the intermediate parties are untrusted while the endpoints are trusted. Under the measurement performed on the intermediate parties, quantum network steering emerges when the sub-normalized state is entangled. Inequalities have been constructed to detect the network steering in the star network scenario with the central party trusted and all the edge parties untrusted [17]. But

In this paper, we introduce the network steering and the genuine network steering from the central parties to the edge parties in star networks, from the aspect of the probability theory. Linear and nonlinear inequalities are constructed to verify the network steering and the genuine network steering for both linear and star networks when the central party performs the fixed measurement. These inequalities can detect more network steering than those of the network n-nonlocality. We demonstrate that the biseparable sub-normalized state is genuine network steerable by example.

II EPR-Steering

Consider the bipartite EPR-steering scenario [18, 19]. Alice and Bob share a bipartite quantum state ρAB\rho_{AB}. Alice performs a set of black-box measurements 𝐱\mathbf{x} on ρAB\rho_{AB} with outcomes 𝐚,\mathbf{a}, denoted by M𝐱𝐚M_{\mathbf{x}}^{\mathbf{a}}. The set of sub-normalized states {τ𝐱𝐚}𝐱,𝐚\{\tau_{\mathbf{x}}^{\mathbf{a}}\}_{\mathbf{x},\mathbf{a}} on Bob’s side is called an assemblage. Each element in the assemblage is given by τ𝐱𝐚=TrA[(M𝐱𝐚I2)ρAB],\tau_{\mathbf{x}}^{\mathbf{a}}={\rm{Tr}}_{A}[(M_{\mathbf{x}}^{\mathbf{a}}\otimes\rm{I}_{2})\rho_{AB}], where I2\rm{I}_{2} is the identity matrix. Alice can not steer Bob if τ𝐱𝐚\tau_{\mathbf{x}}^{\mathbf{a}} admits a local hidden state (LHS) model as follows,

τ𝐱𝐚=λp(λ)p(𝐚|𝐱,λ)ρλ,\displaystyle\begin{aligned} \tau_{\mathbf{x}}^{\mathbf{a}}=\sum\limits_{\lambda}p(\lambda)p(\mathbf{a}|\mathbf{x},\lambda)\rho_{\lambda},\end{aligned} (1)

where λ\lambda denotes the classical random variable distributed according to p(λ)p(\lambda) satisfying λp(λ)=1,\sum\limits_{\lambda}p(\lambda)=1, ρλ\rho_{\lambda} is the hidden local state of Bob and p(𝐚|𝐱,λ)p(\mathbf{a}|\mathbf{x},\lambda) is the local response function of Alice. If there are measurements such that τ𝐱𝐚\tau_{\mathbf{x}}^{\mathbf{a}} does not admit an LHS model, ρAB\rho_{AB} is said to be steerable from Alice to Bob.

The EPR-steering of ρAB\rho_{AB} from Alice to Bob can also be described by the joint probability. Bob performs measurements 𝐲\mathbf{y} on the assemblage {τ𝐱𝐚}𝐱,𝐚\{\tau_{\mathbf{x}}^{\mathbf{a}}\}_{\mathbf{x},\mathbf{a}} with outcomes 𝐛,\mathbf{b}, denoted by M𝐲𝐛M_{\mathbf{y}}^{\mathbf{b}}. The joint probabilities are p(𝐚,b|x,y)=Tr[Mybσxa]p(\mathbf{a},\textsl{b}|\textsl{x},\textsl{y})={\rm{Tr}}[M_{\textsl{y}}^{\textsl{b}}\sigma_{\textsl{x}}^{\textsl{a}}]. ρAB\rho_{AB} is steerable from Alice to Bob if there exist measurements MxaM_{\textsl{x}}^{\textsl{a}} and MybM_{\textsl{y}}^{\textsl{b}} such that the joint probability does dot admit the following local hidden variable-local hidden state (LHV-LHS) model,

p(a,b|x,y)=λp(λ)p(a|x,λ)pQ(b|y,ρλ).\displaystyle\begin{aligned} p(\textsl{a},\textsl{b}|\textsl{x},\textsl{y})=\sum\limits_{\lambda}p(\lambda)p(\textsl{a}|\textsl{x},\lambda)p_{Q}(\textsl{b}|\textsl{y},\rho_{\lambda}).\end{aligned} (2)

Consider the EPR-steering scenario of tripartite state ρABC\rho_{ABC} shared by Alice, Bob and Charlie [20, 21, 23, 22]. Alice, Bob and Charlie perform measurements x,\textsl{x}, y and z with outcomes a,\textsl{a}, b and c,\textsl{c}, denoted by Mxa,M_{\textsl{x}}^{\textsl{a}}, MybM_{\textsl{y}}^{\textsl{b}} and MzcM_{\textsl{z}}^{\textsl{c}}, respectively. The joint probability is given by p(a,b,c|x,y,z)=Tr[ρABC(MxaMybMzc)]p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z})=\rm{Tr}[\rho_{ABC}(M_{\textsl{x}}^{\textsl{a}}\otimes M_{\textsl{y}}^{\textsl{b}}\otimes M_{\textsl{z}}^{\textsl{c}})]. ρABC\rho_{ABC} is said to be tripartite steerable from Alice and Bob to Charlie if there are measurements Mxa,M_{\textsl{x}}^{\textsl{a}}, MybM_{\textsl{y}}^{\textsl{b}} and MzcM_{\textsl{z}}^{\textsl{c}} such that the joint probability p(a,b,c|x,y,z)p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z}) does not satisfy the fully local hidden variable and local hidden state model as follows,

p(a,b,c|x,y,z)=λp(λ)p(a|x,λ)p(b|y,λ)pQ(c|z,ρλC).\displaystyle\begin{aligned} &p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z})\\ =&\sum\limits_{\lambda}p(\lambda)p(\textsl{a}|\textsl{x},\lambda)p(\textsl{b}|\textsl{y},\lambda)p_{Q}(\textsl{c}|\textsl{z},\rho^{C}_{\lambda}).\end{aligned} (3)

The state ρABC\rho_{ABC} is tripartite steerable from Alice to Bob and Charlie if there exist measurements Mxa,M_{\textsl{x}}^{\textsl{a}}, MybM_{\textsl{y}}^{\textsl{b}} and MzcM_{\textsl{z}}^{\textsl{c}} such that the joint probability p(a,b,c|x,y,z)p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z}) does not satisfy the local hidden variable and fully local hidden state model as follows,

p(a,b,c|x,y,z)=λp(λ)p(a|x,λ)pQ(b|y,ρλB)pQ(c|z,ρλC).\displaystyle\begin{aligned} &p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z})\\ =&\sum\limits_{\lambda}p(\lambda)p(\textsl{a}|\textsl{x},\lambda)p_{Q}(\textsl{b}|\textsl{y},\rho^{B}_{\lambda})p_{Q}(\textsl{c}|\textsl{z},\rho^{C}_{\lambda}).\end{aligned} (4)

where ρλB\rho_{\lambda}^{B} and ρλC\rho_{\lambda}^{C} are the local hidden states of Bob and Charlie, respectively.

The other tripartite steering of ρABC\rho_{ABC} from Alice to Bob and Charlie is defined as in [23], for which there exist measurements Mxa,M_{\textsl{x}}^{\textsl{a}}, MybM_{\textsl{y}}^{\textsl{b}} and MzcM_{\textsl{z}}^{\textsl{c}} such that the joint probability p(a,b,c|x,y,z)p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z}) does not satisfy the local hidden variable and bipartite local hidden state model as follows,

p(a,b,c|x,y,z)=λp(λ)p(a|x,λ)pQ(b,c|y,z,ρλBC),\displaystyle\begin{aligned} &p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{y},\textsl{z})\\ =&\sum\limits_{\lambda}p(\lambda)p(\textsl{a}|\textsl{x},\lambda)p_{Q}(\textsl{b},\textsl{c}|\textsl{y},\textsl{z},\rho^{BC}_{\lambda}),\end{aligned} (5)

where ρλBC\rho_{\lambda}^{BC} is the local hidden state of Bob and Charlie. In both scenarios given by Eq.(4) and Eq.(5), a source sends a classical message λ\lambda to Alice with the probability p(λ)p(\lambda). The difference between (4) and (5) is that the corresponding local quantum states sent to Bob and Charlie are separable and entangled respectively.

III Network quantum steering

Consider the networks that the nn parties are arranged in the star networks. For the simplest scenario which has three parties and two sources, the first two parties Alice and Bob1 share the state ρAB1\rho_{AB_{1}} and the last two parties Bob2 and Charlie share the state ρB2C.\rho_{B_{2}C}. The intermediate party Bob, Bob1 and Bob2, performs the fixed measurement y (without the input) with outcomes b,denoted as MybM_{\textsl{y}}^{\textsl{b}}, The sub-normalized state under the measurement is

σbAC=TrB[(IAMybIC)(ρAB1ρB2C)].\sigma_{\textsl{b}}^{AC}={\rm{Tr}}_{B}[(\rm{I}_{\it{A}}\otimes M^{\textsl{b}}_{\textsl{y}}\otimes\rm{I}_{\textit{C}})(\rho_{\textit{AB}_{1}}\otimes\rho_{\textit{B}_{2}\textit{C}})]. (6)

σbAC\sigma_{b}^{AC} admits a network local hidden state model (NLHS) if σbAC\sigma_{b}^{AC} satisfies the following condition,

σbAC=λ1,λ2p(λ1)p(λ2)p(b|λ1,λ2)ρλ1Aρλ2C.\displaystyle\begin{aligned} \sigma_{\textsl{b}}^{AC}=\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})p(\textsl{b}|\lambda_{1},\lambda_{2})\rho_{\lambda_{1}}^{A}\otimes\rho_{\lambda_{2}}^{C}.\end{aligned} (7)

The network state ρABC=ρAB1ρB2C\rho_{ABC}=\rho_{AB_{1}}\otimes\rho_{B_{2}C} demonstrates the network steering from the central party to the endpoint parties if there exists a fixed measurement MybM_{\textsl{y}}^{\textsl{b}} such that σbAC\sigma_{\textsl{b}}^{AC} does not admit NLHS [16].

The entanglement of σbAC\sigma_{\textsl{b}}^{AC} can rule out the NLHS model from Bob to Alice and Charlie, but entanglement detection is a difficult problem, especially for high dimensional quantum states. In [16], the authors only investigated the network steering scenarios with respect to some special states such as separable and unsteerable ones. To investigate the network steering for general quantum states, we define the network steering from the aspect of joint probabilities. We derive inequalities to detect if the sub-normalized states under the measurement performed by the central party admit the NLHS model, thus detecting the network steering.

Bob performs a fixed measurement y with outcome b. The endpoint parties Alice and Charlie perform the measurements x and z on the sub-normalized state σbAC\sigma_{\textsl{b}}^{AC} with outcomes a and c. The joint probability p(a,b,c|x,z)=Tr[(MxaMyb)σbAC]p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{z})={\rm{Tr}}[(M_{\textsl{x}}^{\textsl{a}}\otimes M_{\textsl{y}}^{\textsl{b}})\sigma_{\textsl{b}}^{\textit{AC}}] admits a network local hidden variable and local hidden state model (NLHV-LHS) from the central party Bob to the endpoint parties Alice and Charlie if the joint probability satisfies the following condition,

p(a,b,c|x,z)=λ1,λ2p(λ1)p(λ2)pQ(a|x,ρλ1A)×p(b|λ1,λ2)pQ(c|z,ρλ2C),\displaystyle\begin{aligned} &p(\textsl{a},\textsl{b},\textsl{c}|\textsl{x},\textsl{z})\\ =&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})p_{Q}(\textsl{a}|\textsl{x},\rho_{\lambda_{1}}^{A})\\ &\times p(\textsl{b}|\lambda_{1},\lambda_{2})p_{Q}(\textsl{c}|\textsl{z},\rho_{\lambda_{2}}^{C}),\end{aligned} (8)

where pQ(a|x,ρλ1A)p_{Q}(\textsl{a}|\textsl{x},\rho_{\lambda_{1}}^{A}) (pQ(c|z,ρλ2C)p_{Q}(\textsl{c}|\textsl{z},\rho_{\lambda_{2}}^{C})) is the probability generated from Alice’s (Charlie’s) system, p(b|λ1,λ2)p(\textsl{b}|\lambda_{1},\lambda_{2}) is the probability from Bob’s system B1B2B_{1}B_{2}.

Consider the network composed of three parties Alice, Bob and Charlie and two resources λ1\lambda_{1} and λ2\lambda_{2}. Alice and Charlie perform the mutually unbiased measurements xix^{i} with outcomes aa and ziz^{i} with outcomes cc (i=1,2,a,c=0,1)(i=1,2,\,a,c=0,1) and Bob performs the fixed measurement y={G00,G01,G10,G11}y=\{G_{00},G_{01},G_{10},G_{11}\} with four possible outcomes b{b1,b2}{0,1}.b\equiv\{b_{1},b_{2}\}\in\{0,1\}. Denote y1=G00G11(G01G10),y_{1}=G_{00}-G_{11}-(G_{01}-G_{10}), y2=G00G11+G01G10y_{2}=G_{00}-G_{11}+G_{01}-G_{10} and y3=G00+G11G01G10y_{3}=G_{00}+G_{11}-G_{01}-G_{10}. Then

xiyjzk=a,b,c(1)a+tb+cp(a,b,c|xi,zk),\displaystyle\begin{aligned} &\langle x^{i}\otimes y^{j}\otimes z^{k}\rangle=\sum\limits_{a,b,c}(-1)^{a+t\cdot b+c}p(a,b,c|x^{i},z^{k}),\\ \end{aligned}

with {b1,b2}\{b_{1},b_{2}\} the string of 2 bits representing b=0,1,2b=0,1,2 and 3,3, tt the string of 2 bits representing j=1,2,3.j=1,2,3. We have the following Theorem, see proof in Appendix.

Theorem 1. For line network, the probabilities that admit the NLHS-LHV model from Bob to Alice and Charlie satisfy the following inequalities,

i=13|xiyizi|1.\displaystyle\sum_{i=1}^{3}|\langle x^{i}\otimes y^{i}\otimes z^{i}\rangle|\leq 1. (9)

For the case of star networks, let us consider a star network composed of a central party B=B1BnB=B_{1}\cdots B_{n} and nn edge parties AiA_{i} shared by Bob and Alicei, i=1,,ni=1,\cdots,n. The central party is separately connected to the nn edge parties. The edge parties perform the measurements k=1nMxkak\bigotimes\limits_{k=1}^{n}M_{\textsl{x}_{k}}^{\textsl{a}_{k}} and the central party performs the fixed measurement MybM_{\textsl{y}}^{\textsl{b}}. The quantum state ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits an NLHV-LHS model from the central party to the edge parties if the joint probability satisfies

p(a1,,an,b|x1,,xn)=Tr[(k=1nMxkakMyb)k=1nρAkBk]=λkp(λ1)p(λn)pQ(a1|x1,ρλ1A1)pQ(an|xn,ρλnAn)×p(b|λ1,,λn)\displaystyle\begin{aligned} &p(\textsl{a}_{1},\cdots,\textsl{a}_{n},\textsl{b}|\textsl{x}_{1},\cdots,\textsl{x}_{n})=\rm{Tr}[(\bigotimes\limits_{k=1}^{n}M_{\textsl{x}_{\it{k}}}^{\textsl{a}_{\it{k}}}\otimes M_{\textsl{y}}^{\textsl{b}})\bigotimes\limits_{k=1}^{n}\rho_{\it{A}_{\it{k}}\it{B}_{\it{k}}}]\\ &=\sum\limits_{\lambda_{k}}p(\lambda_{1})\cdots p(\lambda_{n})p_{Q}(\textsl{a}_{1}|\textsl{x}_{1},\rho_{\lambda_{1}}^{A_{1}})...p_{Q}(\textsl{a}_{\textsl{n}}|\textsl{x}_{n},\rho_{\lambda_{n}}^{A_{n}})\\ &\times p(\textsl{b}|\lambda_{1},\cdots,\lambda_{n})\end{aligned}

with b={b1bn}\textsl{b}=\{\textsl{b}_{1}\cdots\textsl{b}_{n}\} and pQ(ak|xk,λk)=Tr[MxkakρλkAk].p_{Q}(\textsl{a}_{k}|\textsl{x}_{k},\lambda_{k})=\rm{Tr}[M_{\textsl{x}_{\it{k}}}^{\textsl{a}_{\it{k}}}\rho_{\lambda_{k}}^{\it{A}_{\it{k}}}].

Let the edge parties perform the mutually unbiased measurements xkik(k=1,,n)x_{k}^{i_{k}}(k=1,\cdots,n) and the fixed measurement performed by Bob is given by y={G00,G001,,G11}\textsl{y}=\{G_{0\cdots 0},G_{0\cdots 01},\cdots,G_{1\cdots 1}\} with 2n2^{n} possible outcomes b{b1,,bn}{0,1}n\textsl{b}\equiv\{b_{1},\cdots,b_{n}\}\in\{0,1\}^{n}. Set

yi1in={t1=0,t2tn(1)IT(Gt1tnGt¯1t¯n),i1inCt1=0,t2tn(1)I0T(Gt1tn+Gt¯1t¯n),i1inCy^{i_{1}\cdots i_{n}}=\left\{\begin{aligned} &\sum\limits_{t_{1}=0,t_{2}\cdots t_{n}}(-1)^{I\cdot T}(G_{t_{1}\cdots t_{n}}-G_{\bar{t}_{1}\cdots\bar{t}_{n}}),\\ &\hskip 142.26378pti_{1}\cdots i_{n}\in C\\ &\sum\limits_{t_{1}=0,t_{2}\cdots t_{n}}(-1)^{I_{0}\cdot T}(G_{t_{1}\cdots t_{n}}+G_{\bar{t}_{1}\cdots\bar{t}_{n}}),\\ &\hskip 142.26378pti_{1}\cdots i_{n}\in C^{\prime}\end{aligned}\right. (10)

where I={i11,,in1},I=\{i_{1}-1,\cdots,i_{n}-1\}, T={t1,,tn}T=\{t_{1},\cdots,t_{n}\}, ``"``\cdot" represents the inner product, I0={i1,,in},I_{0}=\{i_{1},\cdots,i_{n}\}, y11=y11,y_{1}^{1}=y^{1\cdots 1}, y12=y22y_{1}^{2}=y^{2\cdots 2} and y13=y33,y_{1}^{3}=y^{3\cdots 3}, C={1111,11122,,2211,222211,}C=\{111\cdots 1,11\cdots 122,\cdots,221\cdots 1,\cdots 22221\cdots 1,\cdots\} (each string has either zero or even number of 2) and C={3300,00033,,3300,333300,}C^{\prime}=\{330\cdots 0,00\cdots 033,\cdots,330\cdots 0,\cdots 33330\cdots 0,\cdots\} (each string has even number of 3). Then we have

x1i1x2i2xninyi1in=akik,b(1)kakik+p(a1i1,,anin,b|x1i1,,xnin),\displaystyle\begin{aligned} &\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle\\ =&\sum\limits_{a_{k}^{i_{k}},\textsl{b}}(-1)^{\sum\limits_{k}a_{k}^{i_{k}}+\Re}p(a_{1}^{i_{1}},\cdots,a_{n}^{i_{n}},\textsl{b}|x_{1}^{i_{1}},\cdots,x_{n}^{i_{n}}),\\ \end{aligned}

where =IΔ+b1\Re=I\cdot\Delta+b_{1} when i1inC,i_{1}\cdots i_{n}\in C, and =I0Δ\Re=I_{0}\cdot\Delta when i1inC,i_{1}\cdots i_{n}\in C^{\prime}, Δ={b1,,bn}\Delta=\{b_{1},\cdots,b_{n}\} the string of 2 bits representing b=0,1,,2n1b=0,1,\cdots,2^{n}-1 when b1=0b_{1}=0 and Δ={b¯1,,b¯n}\Delta=\{\bar{b}_{1},\cdots,\bar{b}_{n}\} when b1=1.b_{1}=1.

We have the following conclusions, see proof in Appendix.

Theorem 2. (a) If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS from the central party to the edge parties when Bob performs the fixed measurement in the two measurement settings, we have
(1) When nn is an odd number,

i1,,inC|x1i1x2i2xninyi1in|2n2n2\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &\leq 2^{n-2}\end{aligned} (11)

and

i1,,inC|x1i1x2i2xninyi1in|1n2n22,\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{n}}\\ &\leq 2^{n-2}\sqrt{2},\end{aligned} (12)

(2) When nn is an even number,

|x11x21xn1y11|2n+|x12x22xn2y12|2n1.\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}\leq 1.\end{aligned} (13)

and

|x11x21xn1y11|1n+|x12x22xn2y12|1n2.\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{1}{n}}\leq\sqrt{2}.\end{aligned} (14)

(b) If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS from the central party to the edge parties when Bob performs the fixed measurement in three measurement settings, we have
(1) When nn is an odd number,

i1,,inCC|x1i1x2i2xninyi1in|2n2n2+2n21\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &\leq 2^{n-2}+2^{n-2}-1\end{aligned} (15)

and

i1,,inCC|x1i1x2i2xninyi1in|1n2n23+2n21.\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{n}}\\ &\leq 2^{n-2}\sqrt{3}+2^{n-2}-1.\end{aligned} (16)

(2) When nn is an even number,

|x11x21xn1y11|2n+|x12x22xn2y12|2n+|x13x23xn3y13|2n1\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{3}_{1}\otimes x^{3}_{2}\otimes\cdots\otimes x^{3}_{n}\otimes y_{1}^{3}\rangle|^{\frac{2}{n}}\leq 1\end{aligned} (17)

and

|x11x21xn1y11|1n+|x12x22xn2y12|1n+|x13x23xn3y13|1n3,\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{3}_{1}\otimes x^{3}_{2}\otimes\cdots\otimes x^{3}_{n}\otimes y_{1}^{3}\rangle|^{\frac{1}{n}}\leq\sqrt{3},\end{aligned} (18)

where xkikx_{k}^{i_{k}} (ik=1,2,3)(i_{k}=1,2,3) are all mutually unbiased measurements for k=1,,n,k=1,\cdots,n, x10=x20=xn0=I2x_{1}^{0}=x_{2}^{0}=\cdots x_{n}^{0}=\rm{I}_{2} are the identity matrices.

In the above studies we have concerned the sub-normalized state under one fixed measurement performed by the central party. When the central party performs four measurements BiB_{i} (i=1,,4)(i=1,\cdots,4), and the edge parties performs three settings of measurements, we have the following, see proof in Appendix.

Theorem 3. The star network state ρA1AnB\rho_{A_{1}\cdots A_{n}B} admits NLHS model from the central party to the edge parties if

|J1|1n+|J2|1n+|J3|1n+|J4|1n4,\displaystyle\begin{aligned} |J_{1}|^{\frac{1}{n}}+|J_{2}|^{\frac{1}{n}}+|J_{3}|^{\frac{1}{n}}+|J_{4}|^{\frac{1}{n}}\leq 4,\end{aligned} (19)

where

J1=i=1n(xi1+xi2+xi3)B1,J2=i=1n(xi1+xi2xi3)B2,J3=i=1n(xi1xi2+xi3)B3,J4=i=1n(xi1+xi2+xi3)B4\displaystyle\begin{aligned} &J_{1}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}+x_{i}^{3})\otimes B_{1}\rangle,\\ &J_{2}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}-x_{i}^{3})\otimes B_{2}\rangle,\\ &J_{3}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}-x_{i}^{2}+x_{i}^{3})\otimes B_{3}\rangle,\\ &J_{4}=\langle\bigotimes\limits_{i=1}^{n}(-x_{i}^{1}+x_{i}^{2}+x_{i}^{3})\otimes B_{4}\rangle\\ \end{aligned}

and xijx_{i}^{j} (i=1,,n,j=1,2,3)(i=1,\cdots,n,j=1,2,3) are all mutually unbiased measurements.

As an example let us consider the star network ρA1AnB=i=1nρAkBk\rho_{A_{1}\cdots A_{n}B}=\bigotimes\limits_{i=1}^{n}\rho_{A_{k}B_{k}}, where ρAkBk=1p4I4+p|ϕϕ|\rho_{A_{k}B_{k}}=\frac{1-p}{4}\rm{I}_{4}+p|\phi\rangle\langle\phi| with |ϕ=12(|00+|11).|\phi\rangle=\frac{1}{\sqrt{2}}(|00\rangle+|11\rangle). Let B1=i=1n(xi1+xi2+xi3),B_{1}=\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}+x_{i}^{3}), B2=i=1n(xi1+xi2xi3),B_{2}=\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}-x_{i}^{3}), B3=i=1n(xi1xi2+xi3)B_{3}=\bigotimes\limits_{i=1}^{n}(x_{i}^{1}-x_{i}^{2}+x_{i}^{3}) and B4=xi1+xi2+xi3.B_{4}=-x_{i}^{1}+x_{i}^{2}+x_{i}^{3}. From the theorem 3, we have that ρA1AnB\rho_{A_{1}\cdots A_{n}B} demonstrates the network steering when p>33.p>\frac{\sqrt{3}}{3}. However, it has been shown that the state demonstrates the network nonlocality for p>32p>\frac{\sqrt{3}}{2} when the edge parties perform three settings of measurements [12].

In the following examples, we set xkikx_{k}^{i_{k}} to be pauli matrices, Gt1tn=|ψt1tn+ψt1tn+|G_{t_{1}\cdots t_{n}}=|\psi^{+}_{t_{1}\cdots t_{n}}\rangle\langle\psi^{+}_{t_{1}\cdots t_{n}}| and Gt¯1t¯n=|ψt1tnψt1tn|G_{\bar{t}_{1}\cdots\bar{t}_{n}}=|\psi^{-}_{t_{1}\cdots t_{n}}\rangle\langle\psi^{-}_{t_{1}\cdots t_{n}}|, where

|ψt1tn+=12(|t1tn+|t¯1t¯n),|\psi^{+}_{t_{1}\cdots t_{n}}\rangle=\frac{1}{\sqrt{2}}(|t_{1}\cdots t_{n}\rangle+|\bar{t}_{1}\cdots\bar{t}_{n}\rangle),
|ψt1tn=12(|t1tn|t¯1t¯n)|\psi^{-}_{t_{1}\cdots t_{n}}\rangle=\frac{1}{\sqrt{2}}(|t_{1}\cdots t_{n}\rangle-|\bar{t}_{1}\cdots\bar{t}_{n}\rangle)

with t¯\bar{t} representing the bit flip of t,t, t1=0t_{1}=0 and ti{0,1}t_{i}\in\{0,1\} for i2i\geq 2. For the special case of n=2,n=2, we have G00=|ψ00+ψ00+|,G_{00}=|\psi_{00}^{+}\rangle\langle\psi_{00}^{+}|, G11=|ψ00ψ00|,G_{11}=|\psi_{00}^{-}\rangle\langle\psi_{00}^{-}|, G01=|ψ01+ψ01+|G_{01}=|\psi_{01}^{+}\rangle\langle\psi_{01}^{+}| and G10=|ψ01ψ01|,G_{10}=|\psi_{01}^{-}\rangle\langle\psi_{01}^{-}|, where |ψ00±=12(|00±|11)|\psi_{00}^{\pm}\rangle=\frac{1}{\sqrt{2}}(|00\rangle\pm|11\rangle) and |ψ01±=12(|01±|10).|\psi_{01}^{\pm}\rangle=\frac{1}{\sqrt{2}}(|01\rangle\pm|10\rangle).

As another example let us consider the star network ρA1AnB=ρA1B1ρAnBn\rho_{A_{1}\cdots A_{n}B}=\rho_{A_{1}B_{1}}\otimes\cdots\otimes\rho_{A_{n}B_{n}} with ρAiBi=14(I4+c1σ1σ1+c2σ2σ2+c3σ3σ3)\rho_{A_{i}B_{i}}=\frac{1}{4}(\rm{I}_{4}+c_{1}\sigma_{1}\otimes\sigma_{1}+c_{2}\sigma_{2}\otimes\sigma_{2}+c_{3}\sigma_{3}\otimes\sigma_{3}) and B=B1B2BnB=B_{1}B_{2}\cdots B_{n}. From (LABEL:2-set-neven-1), ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable from the central party to the edge parties when max{c12+c22,c12+c32,c22+c31}>1\max\{c_{1}^{2}+c_{2}^{2},c_{1}^{2}+c_{3}^{2},c_{2}^{2}+c_{3}^{1}\}>1 in two measurement settings (which is the same as the result in [26]), and from (LABEL:3-set-neven-1) ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable when c12+c22+c32>1c_{1}^{2}+c_{2}^{2}+c_{3}^{2}>1 in three measurement settings if nn is even. When nn is odd, from (LABEL:2-set-nodd-1) and (LABEL:2-set-nodd-2) ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable from the central party to the edge parties when

i1inC(ci1cin)2n>2n2\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[n]{(c_{i_{1}}\cdots c_{i_{n}})^{2}}>2^{n-2}

or

i1inC|ci1cin|n>2n22\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[n]{|c_{i_{1}}\cdots c_{i_{n}}|}>2^{n-2}\sqrt{2}

in two measurement settings. From (LABEL:3-set-nodd-1) and (LABEL:3-set-nodd-2), ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable when

i1inCC(ci1cin)2n>2n11\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[n]{(c_{i_{1}}\cdots c_{i_{n}})^{2}}>2^{n-1}-1

or

i1inCC|ci1cin|n>2n23+2n21\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[n]{|c_{i_{1}}\cdots c_{i_{n}}|}>2^{n-2}\sqrt{3}+2^{n-2}-1

in three measurement settings with c0=1c_{0}=1.

In particular, when |c1|=|c2|=|c3|=p,|c_{1}|=|c_{2}|=|c_{3}|=p, we have that ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable for p>12p>\frac{1}{\sqrt{2}} in two measurement settings, and for p>13p>\frac{1}{\sqrt{3}} (even nn) or p>p0p>p_{0} (odd nn) in three measurement settings, where p0p_{0} is the solutions of 2n1p+Cnn1pn1n++Cn2p2n=2n23+2n212^{n-1}p+C_{n}^{n-1}p^{\frac{n-1}{n}}+\cdots+C_{n}^{2}p^{\frac{2}{n}}=2^{n-2}\sqrt{3}+2^{n-2}-1 (p0=0.589p_{0}=0.589 for n=3n=3). Nevertheless, it has been shown in [12] that ρA1AnB\rho_{A_{1}\cdots A_{n}B} is not of n-locality when p>32p>\frac{\sqrt{3}}{2} in three measurement settings and when p>0.741p>0.741 in four measurement settings.

The general two-qubit quantum state ρAiBi=14(I4+j(ajσjI2+bjI2σj)+klcklσkσl)\rho_{A_{i}B_{i}}=\frac{1}{4}(\rm{I}_{4}+\sum\limits_{j}(a_{j}\sigma_{j}\otimes\rm{I}_{2}+b_{j}\rm{I}_{2}\otimes\sigma_{j})+\sum\limits_{kl}c_{kl}\sigma_{k}\otimes\sigma_{l}) is locally unitary equivalent to the state ρAiBi=14(I4+j(ajσjI2+bjI2σj)+kdkσkσk)\rho^{\prime}_{A_{i}B_{i}}=\frac{1}{4}(\rm{I}_{4}+\sum\limits_{j}(a^{\prime}_{j}\sigma^{\prime}_{j}\otimes\rm{I}_{2}+b^{\prime}_{j}\rm{I}_{2}\otimes\sigma^{\prime}_{j})+\sum\limits_{k}d_{k}\sigma^{\prime}_{k}\otimes\sigma^{\prime}_{k}), where C=[ckl]=U.D.VC=[c_{kl}]=U.D.V^{*} is the singular value decomposition of CC and D=Diag{d1,d2,d3}D=\rm{Diag}\{d_{1},d_{2},d_{3}\}. Then ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable from the central party to the edge parties when max{d12+d22,d12+d32,d22+d32}>1\max\{d^{2}_{1}+d^{2}_{2},d_{1}^{2}+d_{3}^{2},d_{2}^{2}+d_{3}^{2}\}>1 in two measurement settings, and when d12+d22+d32=Tr[CCT]>1d_{1}^{2}+d_{2}^{2}+d_{3}^{2}=\rm{Tr}[CC^{T}]>1 in three measurement settings for even nn. For odd nn ρA1AnB\rho_{A_{1}\cdots A_{n}B} is steerable from the central party to the edge parties when

i1inC|di1din|2n>2n2\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[n]{|d_{i_{1}}\cdots d_{i_{n}}|^{2}}>2^{n-2}

or

i1inC|di1din|n>2n22\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[n]{|d_{i_{1}}\cdots d_{i_{n}}|}>2^{n-2}\sqrt{2}

in two measurement settings, and when

i1inCC(di1din)2n>2n11\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[n]{(d_{i_{1}}\cdots d_{i_{n}})^{2}}>2^{n-1}-1

or

i1inCC|di1din|n>2n23+2n21\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[n]{|d_{i_{1}}\cdots d_{i_{n}}|}>2^{n-2}\sqrt{3}+2^{n-2}-1

in three measurement settings.

The inequalities in Theorem 1 and Theorem 2 can be used to detect the network steering when the central party performs a single unknown measurement in the star network scenarios composed of bell-diagonal states and general two-qubit states. Compared with the violation of nn-locality, the violations of our inequalities detect more network steering for the star network scenarios composed of Werner states.

IV Genuine Network Quantum steering

Consider the star network with state ρA1A2A3B\rho_{\it{A_{1}A_{2}A_{3}B}}, where Bob performs one fixed joint measurement MybM_{\textsl{y}}^{\textsl{b}}. Entanglement can be generated among the edge parties when the central party performs the joint measurement. Here we consider if the network assemblage {σbA1A2A3}\{\sigma_{b}^{\it{A_{1}A_{2}A_{3}}}\} can be composed of the bi-separable local hidden states with σbA1A2A3=TrB[(I8Myb)ρA1A2A3B]\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}}=\rm{Tr}_{B}[(\rm{I}_{8}\otimes M_{\textsl{y}}^{\textsl{b}})\rho_{\it{A_{1}A_{2}A_{3}B}}]. The three sources send three classical messages λi\lambda_{i} (1i3)(1\leq i\leq 3) to the central party BB. One source generates a quantum state ρλ3A3\rho_{\lambda_{3}}^{\it{A_{3}}} (ρλ1A1\rho_{\lambda_{1}}^{\it{A_{1}}} or ρλ2A2\rho_{\lambda_{2}}^{\it{A_{2}}}) with probability p(λ3)p(\lambda_{3}) (p(λ1)p(\lambda_{1}) or p(λ2)p(\lambda_{2})). The other two sources generate entangled states ρλ1λ2A1A2,\rho_{\lambda_{1}\lambda_{2}}^{\it{A_{1}A_{2}}}, ρλ2λ3A2A3\rho_{\lambda_{2}\lambda_{3}}^{\it{A_{2}A_{3}}} or ρλ1λ3A1A3\rho_{\lambda_{1}\lambda_{3}}^{\it{A_{1}A_{3}}} with the probabilities p(λ1,λ2),p(\lambda_{1},\lambda_{2}), p(λ2,λ3)p(\lambda_{2},\lambda_{3}) and p(λ1,λ3)p(\lambda_{1},\lambda_{3}) randomly, which are sent to the edge parties. The total probability that A1A2\it{A_{1}A_{2}} (A2A3\it{A_{2}A_{3}}, A1A3\it{A_{1}A_{3}}) receive the entangled local hidden states is q1q_{1} (q2q_{2}, q3q_{3}).

Then the sub-normalized state σbA1A2A3\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}} admits bi-separable local hidden states (BLHS) if

σbA1A2A3=q1λ1,λ2,λ3p(λ1,λ2)p(λ3)p(b|λ1,λ2,λ3)ρλ1λ2A1A2ρλ3A3+q2λ1,λ2,λ3p(λ1)p(λ2,λ3)p(b|λ1,λ2,λ3)ρλ2λ3A2A3ρλ1A1+q3λ1,λ2,λ3p(λ2)p(λ1,λ3)p(b|λ1,λ2,λ3)ρλ1λ3A1A3ρλ2A2\displaystyle\begin{aligned} &\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}}\\ =&q_{1}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\lambda_{1},\lambda_{2})p(\lambda_{3})p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})\rho_{\lambda_{1}\lambda_{2}}^{\it{A_{1}A_{2}}}\otimes\rho_{\lambda_{3}}^{\it{A_{3}}}\\ +&q_{2}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\lambda_{1})p(\lambda_{2},\lambda_{3})p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})\rho_{\lambda_{2}\lambda_{3}}^{\it{A_{2}A_{3}}}\otimes\rho_{\lambda_{1}}^{\it{A_{1}}}\\ +&q_{3}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\lambda_{2})p(\lambda_{1},\lambda_{3})p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})\rho_{\lambda_{1}\lambda_{3}}^{\it{A_{1}A_{3}}}\otimes\rho_{\lambda_{2}}^{\it{A_{2}}}\\ \end{aligned}

with iqi=1.\sum\limits_{i}q_{i}=1. The sketch of bi-separable local hidden states model of σbA1A2A3\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}} is shown in Fig. (1). From the aspect of probabilities, we have that ρA1A2A3B\rho_{A_{1}A_{2}A_{3}B} admits network local hidden variable and bi-separable local hidden states (NLHV-BLHS) model if

p(a1,a2,a3,b|x1,x2,x3)=q1λ1,λ2,λ3p(b|λ1,λ2,λ3)p(λ1,λ2)p(λ3)×pQ(a1,a2|x1,x2,ρλ1λ2A1A2)pQ(a3|x3,ρλ3A3)+q2λ1,λ2,λ3p(b|λ1,λ2,λ3)p(λ2,λ3)p(λ1)×pQ(a2,a3|x2,x3,ρλ2λ3A2A3)pQ(a1|x1,ρλ1A1)+q3λ1,λ2,λ3p(b|λ1,λ2,λ3)p(λ1,λ3)p(λ2)×pQ(a1,a3|x1,x3,ρλ1λ3A1A3)pQ(a2|x2,ρλ2A2),\displaystyle\begin{aligned} &p(\textsl{a}_{1},\textsl{a}_{2},\textsl{a}_{3},\textsl{b}|\textsl{x}_{1},\textsl{x}_{2},\textsl{x}_{3})\\ =&q_{1}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})p(\lambda_{1},\lambda_{2})p(\lambda_{3})\\ &\times p_{Q}(\textsl{a}_{1},\textsl{a}_{2}|\textsl{x}_{1},\textsl{x}_{2},\rho_{\lambda_{1}\lambda_{2}}^{\it{A_{1}A_{2}}})p_{Q}(\textsl{a}_{3}|\textsl{x}_{3},\rho_{\lambda_{3}}^{\it{A_{3}}})\\ +&q_{2}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})p(\lambda_{2},\lambda_{3})p(\lambda_{1})\\ &\times p_{Q}({\textsl{a}}_{2},\textsl{a}_{3}|\textsl{x}_{2},\textsl{x}_{3},\rho_{\lambda_{2}\lambda_{3}}^{\it{A_{2}A_{3}}})p_{Q}(\textsl{a}_{1}|\textsl{x}_{1},\rho_{\lambda_{1}}^{\it{A_{1}}})\\ +&q_{3}\sum\limits_{\lambda_{1},\lambda_{2},\lambda_{3}}p(\textsl{b}|\lambda_{1},\lambda_{2},\lambda_{3})p(\lambda_{1},\lambda_{3})p(\lambda_{2})\\ &\times p_{Q}(\textsl{a}_{1},\textsl{a}_{3}|\textsl{x}_{1},\textsl{x}_{3},\rho_{\lambda_{1}\lambda_{3}}^{\it{A_{1}A_{3}}})p_{Q}(\textsl{a}_{2}|\textsl{x}_{2},\rho_{\lambda_{2}}^{\it{A_{2}}}),\end{aligned} (20)
Refer to caption
Figure 1: The bi-separable local hidden state model of σbA1A2A3\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}}. σbA1A2A3\sigma_{\textsl{b}}^{\it{A_{1}A_{2}A_{3}}} is represented by the region outside of the blue triangle. The entangled local hidden states ρλ1λ2A1A2\rho_{\lambda_{1}\lambda_{2}}^{\it{A_{1}A_{2}}}, ρλ1λ3A1A3\rho_{\lambda_{1}\lambda_{3}}^{\it{A_{1}A_{3}}} and ρλ2λ3A2A3\rho_{\lambda_{2}\lambda_{3}}^{\it{A_{2}A_{3}}} are shown in the purple, red and green boxes, respectively.

Generally we have star networks with state ρA1AnB=i=1nρAiBi\rho_{A_{1}\cdots A_{n}B}=\bigotimes\limits_{i=1}^{n}\rho_{A_{i}B_{i}}. Bob performs one fixed measurement y defined in Theorem 2. It admits bi-separable local hidden states (BLHS) model if

σbA1An=λ1λnp(b|λ1,,λn)×s=1n2t=1Cnsqstp(Λst)ρΛstΓstp(Λ~st)ρΛ~stΓ~st\displaystyle\begin{aligned} &\sigma_{\textsl{b}}^{A_{1}\cdots A_{n}}\\ =&\sum\limits_{\lambda_{1}\cdots\lambda_{n}}p(\textsl{b}|\lambda_{1},\cdots,\lambda_{n})\\ &\times\sum\limits_{s=1}^{\lfloor\frac{n}{2}\rfloor}\sum\limits_{t=1}^{C_{n}^{s}}q^{t}_{s}p(\Lambda^{t}_{s})\rho^{\Gamma^{t}_{s}}_{\Lambda^{t}_{s}}\otimes p(\tilde{\Lambda}^{t}_{s})\rho^{\tilde{\Gamma}^{t}_{s}}_{\tilde{\Lambda}^{t}_{s}}\end{aligned} (21)

where s,tqst=1,\sum\limits_{s,t}q_{s}^{t}=1, and Λst\Lambda^{t}_{s} and Λ~st\tilde{\Lambda}^{t}_{s} are two disjoint subsets of the set {λ1,,λn}\{\lambda_{1},\cdots,\lambda_{n}\} with the number of the elements ss and nsn-s respectively. The number of the sets Λst\Lambda_{s}^{t} is CnsC_{n}^{s} with CnsC_{n}^{s} being the combinations. Let Λst={λi|iKst}\Lambda^{t}_{s}=\{\lambda_{i}|i\in K^{t}_{s}\} and Λ~st={λi|iKst¯}\tilde{\Lambda}^{t}_{s}=\{\lambda_{i}|i\in\overline{K^{t}_{s}}\} with KstK^{t}_{s} being the set obtained by selecting ss elements from {1,2,,n}\{1,2,\cdots,n\} and Kst¯\overline{K^{t}_{s}} being the complement of Kst.K^{t}_{s}. Then Γst={Ai|iKst}\Gamma^{t}_{s}=\{\textit{A}_{i}|i\in K^{t}_{s}\} and Γ~st={Ai|iKst¯}.\tilde{\Gamma}^{t}_{s}=\{\textit{A}_{i}|i\in\overline{K^{t}_{s}}\}.

From the aspect of probabilities, we have ρA1AnB\rho_{A_{1}\cdots A_{n}B} admits network local hidden variable and bi-separable local hidden states (NLHV-BLHS) model if

p(a1,,an,b|x1,,xn)=λ1λnp(b|λ1,,λn)s=1n2t=1Cnsqstp(Λst)p(Λ~st)×pQ(aKst|xKst,ρΛstΓst)pQ(aKst¯|xKst¯,ρΛ~stΓ~st).\displaystyle\begin{aligned} &p(\textsl{a}_{1},\cdots,\textsl{a}_{n},\textsl{b}|\textsl{x}_{1},\cdots,\textsl{x}_{n})\\ =&\sum\limits_{\lambda_{1}\cdots\lambda_{n}}p(\textsl{b}|\lambda_{1},\cdots,\lambda_{n})\sum\limits_{s=1}^{\lfloor\frac{n}{2}\rfloor}\sum\limits_{t=1}^{C_{n}^{s}}q_{s}^{t}p(\Lambda_{s}^{t})p(\tilde{\Lambda}_{s}^{t})\\ &\times p_{Q}(\textsl{a}_{K_{s}^{t}}|x_{K_{s}^{t}},\rho_{\Lambda_{s}^{t}}^{\Gamma_{s}^{t}})p_{Q}(\textsl{a}_{\overline{K_{s}^{t}}}|x_{\overline{K_{s}^{t}}},\rho_{\tilde{\Lambda}_{s}^{t}}^{\tilde{\Gamma}_{s}^{t}}).\end{aligned}

The genuine multipartite entanglement of σbA1An\sigma_{b}^{\it{A_{1}\cdots A_{n}}} can rule out BLHS. But it is also a difficult problem to detect the genuine entanglement. In addition, BLHS model is different from the bi-separable state model as all the sub-normalized states in the network assemblage admit the BLHS model with the same set of the bi-separable local hidden states. To investigate the BLHS model further, we give the witness to detect the genuine network steering composed of general quantum states, see proof in Appendix. Consider the state ρA1A2A3B\rho_{\it{A_{1}A_{2}A_{3}B}}. The central party performs the fixed measurement Π={G000,,G111}\Pi=\{G_{000},\cdots,G_{111}\} defined in Theorem 2. The edge parties Ai\it{A_{i}} perform the mutually unbiased observables xij.x_{i}^{j}. The state ρA1A2A3B\rho_{\it{A_{1}A_{2}A_{3}B}} admits BLHS if

|x11x21x31y111|+|x11x22x32y122|+|x12x21x32y212|+|x12x22x31y221|22\displaystyle\begin{aligned} &\sqrt{|\langle x_{1}^{1}\otimes x_{2}^{1}\otimes x_{3}^{1}\otimes y^{111}\rangle|}+\sqrt{|\langle x_{1}^{1}\otimes x_{2}^{2}\otimes x_{3}^{2}\otimes y^{122}\rangle|}\\ +&\sqrt{|\langle x_{1}^{2}\otimes x_{2}^{1}\otimes x_{3}^{2}\otimes y^{212}\rangle|}+\sqrt{|\langle x_{1}^{2}\otimes x_{2}^{2}\otimes x_{3}^{1}\otimes y^{221}\rangle|}\\ \leq&2\sqrt{2}\end{aligned}

and

|x11x21x31y111|+|x11x22x32y122|+|x12x21x32y212|+|x12x22x31y221|+|x13x23I2y330|+|x13I2x33y303|+|I2x23x33y033|23+1.\displaystyle\begin{aligned} &\sqrt{|\langle x_{1}^{1}\otimes x_{2}^{1}\otimes x_{3}^{1}\otimes y^{111}\rangle|}+\sqrt{|\langle x_{1}^{1}\otimes x_{2}^{2}\otimes x_{3}^{2}\otimes y^{122}\rangle|}\\ +&\sqrt{|\langle x_{1}^{2}\otimes x_{2}^{1}\otimes x_{3}^{2}\otimes y^{212}\rangle|}+\sqrt{|\langle x_{1}^{2}\otimes x_{2}^{2}\otimes x_{3}^{1}\otimes y^{221}\rangle|}\\ +&\sqrt{|\langle x_{1}^{3}\otimes x_{2}^{3}\otimes I_{2}\otimes y^{330}\rangle|}+\sqrt{|\langle x_{1}^{3}\otimes I_{2}\otimes x_{3}^{3}\otimes y^{303}\rangle|}\\ +&\sqrt{|\langle I_{2}\otimes x_{2}^{3}\otimes x_{3}^{3}\otimes y^{033}\rangle|}\leq 2\sqrt{3}+1.\end{aligned}

For the star network ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}}, we have the following result, see proof in Appendix.

Theorem 4. If ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}} admits NLHV-BLHS model from the central party to the edge parties when Bob performs the fixed measurement given in Theorem 2, we have

i1,,inC|x1i1x2i2xninyi1in|122n22\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\\ &\leq 2^{n-2}\sqrt{2}\end{aligned} (22)

and

i1,,inCC|x1i1x2i2xninyi1in|122n23+2n21.\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\\ &\leq 2^{n-2}\sqrt{3}+2^{n-2}-1.\end{aligned} (23)

As an example, let us consider the star network ρA1AnB=ρA1B1ρAnBn\rho_{\it{A_{1}\cdots A_{n}B}}=\rho_{\it{A_{1}B_{1}}}\otimes\cdots\otimes\rho_{\it{A_{n}B_{n}}}, where ρAiBi=14(I4+c1σ1σ1+c2σ2σ2+c3σ3σ3)\rho_{\it{A_{i}B_{i}}}=\frac{1}{4}(\rm{I}_{4}+c_{1}\sigma_{1}\otimes\sigma_{1}+c_{2}\sigma_{2}\otimes\sigma_{2}+c_{3}\sigma_{3}\otimes\sigma_{3}) and B=B1B2Bn\it{B}=\it{B_{1}B_{2}\cdots B_{n}}. We have that ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}} is genuine steerable from the central party to the edge parties when

i1inC|ci1cin|2>2n22\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[2]{|c_{i_{1}}\cdots c_{i_{n}}|}>2^{n-2}\sqrt{2}

in two measurement settings by using the inequality (LABEL:2-set-gen), and

i1inCC|ci1cin|2>2n23+2n21\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[2]{|c_{i_{1}}\cdots c_{i_{n}}|}>2^{n-2}\sqrt{3}+2^{n-2}-1

in three measurement settings with c0=1c_{0}=1 by using the inequality (LABEL:3-set-gen).

Specially, when |c1|=|c2|=|c3|=p|c_{1}|=|c_{2}|=|c_{3}|=p, ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}} is genuine steerable for p>12np>\frac{1}{\sqrt[n]{2}} in two measurement settings and for p>p0p>p_{0} in three measurement settings, where p0p_{0} the solutions of 2n1pn2+Cn2p22++Cnnpn2=2n23+2n212^{n-1}p^{\frac{n}{2}}+C_{n}^{2}p^{\frac{2}{2}}+\cdots+C_{n}^{n}p^{\frac{n}{2}}=2^{n-2}\sqrt{3}+2^{n-2}-1 (nn is even) or 2n1pn2+Cn2p22++Cnn1pn12=2n23+2n212^{n-1}p^{\frac{n}{2}}+C_{n}^{2}p^{\frac{2}{2}}+\cdots+C_{n}^{n-1}p^{\frac{n-1}{2}}=2^{n-2}\sqrt{3}+2^{n-2}-1 (nn is odd), p0=0.703p_{0}=0.703 when n=3n=3 and p0=0.769p_{0}=0.769 when n=4n=4. However, since σbA1A2A3/Tr[σbA1A2A3]\sigma_{b}^{\it{A_{1}A_{2}A_{3}}}/\rm{Tr}[\sigma_{b}^{\it{A_{1}A_{2}A_{3}}}] is genuine tripartite entangled if and only if p>0.7154p>0.7154, interestingly we have that the separable assemblage may have genuine network steering.

If ρAiBi=14(I+j(ajσjI2+bjI2σj)+klcklσkσl)\rho_{A_{i}B_{i}}=\frac{1}{4}(\rm{I}+\sum\limits_{j}(a_{j}\sigma_{j}\otimes\rm{I}_{2}+b_{j}\rm{I}_{2}\otimes\sigma_{j})+\sum\limits_{kl}c_{kl}\sigma_{k}\otimes\sigma_{l}), which is locally unitary equivalent to ρAiBi=14(I+j(ajσjI2+bjI2σj)+kdkσkσk)\rho_{\it{A_{i}B_{i}}}=\frac{1}{4}(\rm{I}+\sum\limits_{j}(a^{\prime}_{j}\sigma^{\prime}_{j}\otimes\rm{I}_{2}+b^{\prime}_{j}\rm{I}_{2}\otimes\sigma^{\prime}_{j})+\sum\limits_{k}d_{k}\sigma^{\prime}_{k}\otimes\sigma^{\prime}_{k}), we have that ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}} is genuine steerable from the central party to the edge parties when

i1inC|di1din|2>2n22\sum\limits_{i_{1}\cdots i_{n}\in C}\sqrt[2]{|d_{i_{1}}\cdots d_{i_{n}}|}>2^{n-2}\sqrt{2}

in two measurement settings, and

i1inCC|di1din|2>2n23+2n21\sum\limits_{i_{1}\cdots i_{n}\in C\bigcup C^{\prime}}\sqrt[2]{|d_{i_{1}}\cdots d_{i_{n}}|}>2^{n-2}\sqrt{3}+2^{n-2}-1

in three measurement settings.

The genuine network steering can be detected in the star network scenarios composed of bell-diagonal states and general two-qubit states by the inequalities we derived in Theorem 4. Interestingly, the bi-separable states under the joint measurements performed by the central party do not admit BLHS model for the star network composed of Werner states.

V Conclusion

We have investigated the network steering and genuine network steering in star network scenarios from the aspect of the probabilities, when the central party performs the fixed measurement. We have constructed the linear and nonlinear inequalities to verify the network steering and the genuine network steering in two and three measurement settings. It has been shown that more quantum network steering can be detected compared with the violation of the nn-locality quantum networks, and the biseparable assemblages may show genuine network steering in star network configurations. It would be also interesting to explore the applications of the network genuine steering in quantum processing tasks. Our results may also highlight further investigations on multipartite quantum network steering and genuine multipartite quantum network steering in high dimensional systems.


ACKNOWLEDGEMENTS    This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 12071179, 12075159 and 12171044; the Academician Innovation Platform of Hainan Province.

VI Appendix

Theorem 1. For the simple network, the probabilities that admit the NLHS-LHV model from Bob to Alice and Charlie satisfy the following inequalities,

i=13|xiyizi|1.\displaystyle\sum_{i=1}^{3}|\langle x^{i}\otimes y^{i}\otimes z^{i}\rangle|\leq 1. (24)

Proof of Theorem 1:

By definition if Bob can not steer Alice and Charlie, we have the following inequality,

|x1y1z1|+|x2y2z2|+|x3y3z3|=|λ1,λ2p(λ1)p(λ2)a(1)apQ(a|λ1,x1)×b(1)b2p(b|λ1,λ2)c(1)cpQ(c|λ2,z1)|+|λ1,λ2p(λ1)p(λ2)a(1)apQ(a|λ1,x2)×b(1)b1p(b|λ1,λ2)c(1)cpQ(c|λ2,z2)|+|λ1,λ2p(λ1)p(λ2)a(1)apQ(a|λ1,x3)×b(1)b1+b2p(b|λ1,λ2,y2)c(1)cpQ(c|λ2,z3)|=|λ1,λ2p(λ1)p(λ2)x1λ1Qy1λ1,λ2z1λ2Q|+|λ1,λ2p(λ1)p(λ2)x2λ1Qy2λ1,λ2z2λ2Q|+|λ1,λ2p(λ1)p(λ2)x3λ1Qy3λ1,λ2z3λ2Q|λ1,λ2p(λ1)p(λ2)|x1λ1Qy1λ1,λ2z1λ2Q|+λ1,λ2p(λ1)p(λ2)|x2λ1Qy2λ1,λ2z2λ2Q|+λ1,λ2p(λ1)p(λ2)|x3λ1Qy3λ1,λ2z3λ2Q|λ1,λ2p(λ1)p(λ2)(x1λ1Q)2+(|x2λ1Q)2+(|x3λ1Q)2×(z1λ2Q)2+(|z2λ2Q)2+(|z3λ2Q)21,\displaystyle\begin{aligned} &|\langle x^{1}\otimes y^{1}\otimes z^{1}\rangle|+|\langle x^{2}\otimes y^{2}\otimes z^{2}\rangle|+|\langle x^{3}\otimes y^{3}\otimes z^{3}\rangle|\\ =|&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\sum\limits_{a}(-1)^{a}p_{Q}(a|\lambda_{1},x^{1})\\ &\times\sum\limits_{b}(-1)^{b_{2}}p(b|\lambda_{1},\lambda_{2})\sum\limits_{c}(-1)^{c}p_{Q}(c|\lambda_{2},z^{1})|\\ &+|\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\sum\limits_{a}(-1)^{a}p_{Q}(a|\lambda_{1},x^{2})\\ &\times\sum\limits_{b}(-1)^{b_{1}}p(b|\lambda_{1},\lambda_{2})\sum\limits_{c}(-1)^{c}p_{Q}(c|\lambda_{2},z^{2})|\\ &+|\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\sum\limits_{a}(-1)^{a}p_{Q}(a|\lambda_{1},x^{3})\\ &\times\sum\limits_{b}(-1)^{b_{1}+b_{2}}p(b|\lambda_{1},\lambda_{2},y_{2})\sum\limits_{c}(-1)^{c}p_{Q}(c|\lambda_{2},z^{3})|\\ =&|\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\langle x^{1}\rangle^{Q}_{\lambda_{1}}\langle y^{1}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{1}\rangle^{Q}_{\lambda_{2}}|\\ +&|\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\langle x^{2}\rangle^{Q}_{\lambda_{1}}\langle y^{2}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{2}\rangle^{Q}_{\lambda_{2}}|\\ +&|\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\langle x^{3}\rangle^{Q}_{\lambda_{1}}\langle y^{3}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{3}\rangle^{Q}_{\lambda_{2}}|\\ \leq&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})|\langle x^{1}\rangle^{Q}_{\lambda_{1}}\langle y^{1}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{1}\rangle^{Q}_{\lambda_{2}}|\\ +&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})|\langle x^{2}\rangle^{Q}_{\lambda_{1}}\langle y^{2}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{2}\rangle^{Q}_{\lambda_{2}}|\\ +&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})|\langle x^{3}\rangle^{Q}_{\lambda_{1}}\langle y^{3}\rangle_{\lambda_{1},\lambda_{2}}\langle z^{3}\rangle^{Q}_{\lambda_{2}}|\\ \leq&\sum\limits_{\lambda_{1},\lambda_{2}}p(\lambda_{1})p(\lambda_{2})\sqrt{(\langle x^{1}\rangle^{Q}_{\lambda_{1}})^{2}+(|\langle x^{2}\rangle^{Q}_{\lambda_{1}})^{2}+(|\langle x^{3}\rangle^{Q}_{\lambda_{1}})^{2}}\\ &\times\sqrt{(\langle z^{1}\rangle^{Q}_{\lambda_{2}})^{2}+(|\langle z^{2}\rangle^{Q}_{\lambda_{2}})^{2}+(|\langle z^{3}\rangle^{Q}_{\lambda_{2}})^{2}}\leq 1,\end{aligned}

where the equality is due to the definitions of the expectation and the network local hidden variable and local hidden state model. The first inequality is due to the inequality satisfied by absolute values, namely, |isi|i|si||\sum\limits_{i}s_{i}|\leq\sum\limits_{i}|s_{i}|. The second inequality is due to the inequality |yiλ1,λ2|1|\langle y^{i}\rangle_{\lambda_{1},\lambda_{2}}|\leq 1 and the Cauchy-Schwarz inequality. The last inequality is from that (x1λ1,λ2Q)2+(x2λ1,λ2Q)1+(x3λ1,λ2Q)21(\langle x^{1}\rangle^{Q}_{\lambda_{1},\lambda_{2}})^{2}+(\langle x^{2}\rangle^{Q}_{\lambda_{1},\lambda_{2}})^{1}+(\langle x^{3}\rangle^{Q}_{\lambda_{1},\lambda_{2}})^{2}\leq 1 and (z1λ2Q)2+(z2λ2Q)2+(z3λ2Q)21(\langle z^{1}\rangle_{\lambda_{2}}^{Q})^{2}+(\langle z^{2}\rangle_{\lambda_{2}}^{Q})^{2}+(\langle z^{3}\rangle_{\lambda_{2}}^{Q})^{2}\leq 1 for the mutually unbiased measurements.


Theorem 2: (a) If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS from the central parties to the edge parties when Bob performs the fixed measurement in the two measurement settings, we have
(1) When nn is an odd number,

i1,,inC|x1i1x2i2xninyi1in|2n2n2\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &\leq 2^{n-2}\end{aligned} (25)

and

i1,,inC|x1i1x2i2xninyi1in|1n2n22,\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{n}}\\ &\leq 2^{n-2}\sqrt{2},\end{aligned} (26)

Proof of Theorem 2 (a) (1): If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS from the central parties to the edge parties when Bob performs the fixed measurement in the two measurement settings, then

i1,,inC|x1i1x2i2xninyi1in|2n=i1,,inC|λkakikk=1np(λk)akik(1)akikpQ(akik|xkik,λk)×b(1)p(b|λ1λn)|2n=i1,,inC|λkk=1np(λk)xkikλkQyi1inλ1λn|)2ni1,,inCλk(k=1np(λk)|xkikλkQ|)2nk=1n{λkp(λk)ikC[xkikλkQ]2}1n2n2,\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &=\sum\limits_{i_{1},\cdots,i_{n}\in C}|\sum\limits_{\lambda_{k}}\sum\limits_{a_{k}^{i_{k}}}\prod\limits_{k=1}^{n}p(\lambda_{k})\sum\limits_{a_{k}^{i_{k}}}(-1)^{a_{k}^{i_{k}}}p_{Q}(a_{k}^{i_{k}}|x_{k}^{i_{k}},\lambda_{k})\\ &\times\sum\limits_{b}(-1)^{\Re}p(b|\lambda_{1}\cdots\lambda_{n})|^{\frac{2}{n}}\\ &=\sum\limits_{i_{1},\cdots,i_{n}\in C}|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}\langle y^{i_{1}\cdots i_{n}}\rangle_{\lambda_{1}\cdots\lambda_{n}}|)^{\frac{2}{n}}\\ &\leq\sum\limits_{i_{1},\cdots,i_{n}\in C}\sum\limits_{\lambda_{k}}(\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}|)^{\frac{2}{n}}\\ &\leq\prod\limits_{k=1}^{n}\{\sum\limits_{\lambda_{k}}p(\lambda_{k})\sum\limits_{i_{k}\in C}[\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}]^{2}\}^{\frac{1}{n}}\\ &\leq 2^{n-2},\end{aligned}

where the equalities are attained according to the definitions of expectation value and NLHV-LHS model, the first inequality is attained by using the absolute value inequality and |yi1inλ1λn|1,|\langle y^{i_{1}\cdots i_{n}}\rangle_{\lambda_{1}\cdots\lambda_{n}}|\leq 1, the second inequality is attained by using the Lemma 1 in [25]. There are 2n12^{n-1} elements in CC. Hence, the number of 11 and the number of 22 are all 2n22^{n-2} for each kk. Then we have ikC[xkikλkQ]22n2\sum\limits_{i_{k}\in C}[\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}]^{2}\leq 2^{n-2} by using (xk1λkQ)2+(xk2λkQ)21({\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}}})^{2}+({\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}})^{2}\leq 1 for the mutually unbiased measurements, which gives rise to the last inequality.

Theorem 2: (a) (2) When nn is an even number,

|x11x21xn1y11|2n+|x12x22xn2y12|2n1.\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}\leq 1.\end{aligned} (27)

and

|x11x21xn1y11|1n+|x12x22xn2y12|1n2.\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{1}{n}}\leq\sqrt{2}.\end{aligned} (28)

Proof of Theorem 2. (a) (2): By straightforward calculation,

|k=1nxk1y11|2n+|k=1nxk2y12|2n=(|λkk=1np(λk)ak1(1)ak1pQ(ak1|xk1,λk)b(1)×p(b|λ1λn)|)2n+(|λkk=1np(λk)ak2(1)ak2pQ(ak2|xk2,λk)b(1)×p(b|λ1λn)|)2n=(|λkk=1np(λk)xk1λkQy11λ|)2n+(|λkk=1np(λk)xk2λkQy12λ|)2nλk(|k=1np(λk)|xk1λkQy11λ|)2n+λk(|k=1np(λk)|xk2λkQy12λ|)2nk=1n(λkp(λk)((xk1λkQ)2+(xk2λkQ)2)1n1,\displaystyle\begin{aligned} &|\langle\bigotimes\limits_{k=1}^{n}x_{k}^{1}\otimes y^{1}_{1}\rangle|^{\frac{2}{n}}+|\langle\bigotimes\limits_{k=1}^{n}x_{k}^{2}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}\\ =&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\sum\limits_{a_{k}^{1}}(-1)^{a_{k}^{1}}p_{Q}(a_{k}^{1}|x_{k}^{1},\lambda_{k})\sum\limits_{b}(-1)^{\Re}\\ &\times p(b|\lambda_{1}\cdots\lambda_{n})|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\sum\limits_{a_{k}^{2}}(-1)^{a_{k}^{2}}p_{Q}(a_{k}^{2}|x_{k}^{2},\lambda_{k})\sum\limits_{b}(-1)^{\Re}\\ &\times p(b|\lambda_{1}\cdots\lambda_{n})|)^{\frac{2}{n}}\\ =&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{1}\rangle_{\lambda_{k}}^{Q}\langle y^{1}_{1}\rangle_{\lambda}|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}\langle y_{1}^{2}\rangle_{\lambda}|)^{\frac{2}{n}}\\ \leq&\sum\limits_{\lambda_{k}}(|\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{1}\rangle_{\lambda_{k}}^{Q}\langle y_{1}^{1}\rangle_{\lambda}|)^{\frac{2}{n}}\\ &+\sum\limits_{\lambda_{k}}(|\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{2}\rangle_{\lambda_{k}}^{Q}\langle y_{1}^{2}\rangle_{\lambda}|)^{\frac{2}{n}}\\ \leq&\prod\limits_{k=1}^{n}(\sum\limits_{\lambda_{k}}p(\lambda_{k})((\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}})^{2}+(\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}})^{2})^{\frac{1}{n}}\\ \leq&1,\end{aligned}

where the equalities are attained by using the definition of expectation values and NLHV-LHS model, the second inequality is attained by using the Lemma 1 in [25], the last inequality uses (xk1λkQ)2+(xk2λkQ)21({\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}}})^{2}+({\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}})^{2}\leq 1 for the mutually unbiased measurements.

Theorem 2(b) If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS from the central parties to the edge parties when Bob performs the fixed measurement in three measurement settings, we have
(1) When nn is an odd number,

i1,,inCC|x1i1x2i2xninyi1in|2n2n2+2n21\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &\leq 2^{n-2}+2^{n-2}-1\end{aligned} (29)

and

i1,,inCC|x1i1x2i2xninyi1in|1n2n23+2n21.\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{n}}\\ &\leq 2^{n-2}\sqrt{3}+2^{n-2}-1.\end{aligned} (30)

Proof of Theorem 2(b) (1).

i1,,inCC|x1i1x2i2xniny1i1in|2ni1,,inCC|λkk=1np(λk)(1)akikpQ(akik|xkik,λk)×(1)p(b|λ1λn)|2n=i1,,inCC|λkk=1np(λk)xkikλkQ|)2ni1,,inCCλk(k=1np(λk)|xkikλkQ|)2nk=1n{λkp(λk)ikCC[xkikλkQ]2}1n2n2+Cn12++Cn1n1=2n2+2n21.\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\bigcup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y_{1}^{i_{1}\cdots i_{n}}\rangle|^{\frac{2}{n}}\\ &\leq\sum\limits_{i_{1},\cdots,i_{n}\in C\bigcup C^{\prime}}|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})(-1)^{a_{k}^{i_{k}}}p_{Q}(a_{k}^{i_{k}}|x_{k}^{i_{k}},\lambda_{k})\\ &\times(-1)^{\Re}p(b|\lambda_{1}\cdots\lambda_{n})|^{\frac{2}{n}}\\ &=\sum\limits_{i_{1},\cdots,i_{n}\in C\bigcup C^{\prime}}|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}|)^{\frac{2}{n}}\\ &\leq\sum\limits_{i_{1},\cdots,i_{n}\in C\bigcup C^{\prime}}\sum\limits_{\lambda_{k}}(\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}|)^{\frac{2}{n}}\\ &\leq\prod\limits_{k=1}^{n}\{\sum\limits_{\lambda_{k}}p(\lambda_{k})\sum\limits_{i_{k}\in C\bigcup C^{\prime}}[\langle x_{k}^{i_{k}}\rangle_{\lambda_{k}}^{Q}]^{2}\}^{\frac{1}{n}}\\ &\leq 2^{n-2}+C_{n-1}^{2}+\cdots+C_{n-1}^{n-1}=2^{n-2}+2^{n-2}-1.\end{aligned}

The proof is similar to that of (𝐚)(1),{(\bf{a})}(1), but the last inequality is attained due to that the number of 33 in CC^{\prime} is 2n22^{n-2} and the number of 0 in CC^{\prime} is Cn12++Cn1n1C_{n-1}^{2}+\cdots+C_{n-1}^{n-1} for each kk.

Theorem 2(b)(2) When nn is an even number,

|x11x21xn1y11|2n+|x12x22xn2y12|2n+|x13x23xn3y13|2n1\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}\\ +&|\langle x^{3}_{1}\otimes x^{3}_{2}\otimes\cdots\otimes x^{3}_{n}\otimes y_{1}^{3}\rangle|^{\frac{2}{n}}\leq 1\end{aligned} (31)

and

|x11x21xn1y11|1n+|x12x22xn2y12|1n+|x13x23xn3y13|1n3,\displaystyle\begin{aligned} &|\langle x^{1}_{1}\otimes x^{1}_{2}\otimes\cdots\otimes x^{1}_{n}\otimes y_{1}^{1}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{2}_{1}\otimes x^{2}_{2}\otimes\cdots\otimes x^{2}_{n}\otimes y_{1}^{2}\rangle|^{\frac{1}{n}}\\ +&|\langle x^{3}_{1}\otimes x^{3}_{2}\otimes\cdots\otimes x^{3}_{n}\otimes y_{1}^{3}\rangle|^{\frac{1}{n}}\leq\sqrt{3},\end{aligned} (32)

where xkikx_{k}^{i_{k}} (ik=1,2,3)(i_{k}=1,2,3) are all mutually unbiased measurements for k=1,,n,k=1,\cdots,n, x10=x20=xn0=I2x_{1}^{0}=x_{2}^{0}=\cdots x_{n}^{0}=\rm{I}_{2} are the identity matrices.

Proof of Theorem 2 b(2). By straightforward calculation, we have

|k=1nxk1y11|2n+|k=1nxk2y12|2n+|k=1nxk3y13|2n=(|λkk=1np(λk)(1)ak1pQ(ak1|xk1,λk)(1)×p(b|λ1λn)|)2n+(|λkk=1np(λk)(1)ak2pQ(ak2|xk2,λk)(1)×p(b|λ1λn)|)2n+(|λkk=1np(λk)(1)ak3pQ(ak3|xk3,λk)(1)×p(b|λ1λn)|)2n=(|λkk=1np(λk)xk1λkQy11λ|)2n+(|λkk=1np(λk)xk2λkQy12λ|)2n+(|λkk=1np(λk)xk3λkQy13λ|)2nλk(|k=1np(λk)|xk1λkQy11λ|)2n+λk(|k=1np(λk)|xk2λkQy12λ|)2n+λk(|k=1np(λk)|xk3λkQy13λ|)2nk=1n(λkp(λk)((xk1λkQ)2+(xk2λkQ)2+(xk3λkQ)2)1n1,\displaystyle\begin{aligned} &|\langle\bigotimes\limits_{k=1}^{n}x_{k}^{1}\otimes y^{1}_{1}\rangle|^{\frac{2}{n}}+|\langle\bigotimes\limits_{k=1}^{n}x_{k}^{2}\otimes y_{1}^{2}\rangle|^{\frac{2}{n}}+|\langle\bigotimes\limits_{k=1}^{n}x_{k}^{3}\otimes y_{1}^{3}\rangle|^{\frac{2}{n}}\\ =&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})(-1)^{a_{k}^{1}}p_{Q}(a_{k}^{1}|x_{k}^{1},\lambda_{k})(-1)^{\Re}\\ &\times p(b|\lambda_{1}\cdots\lambda_{n})|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})(-1)^{a_{k}^{2}}p_{Q}(a_{k}^{2}|x_{k}^{2},\lambda_{k})(-1)^{\Re}\\ &\times p(b|\lambda_{1}\cdots\lambda_{n})|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})(-1)^{a_{k}^{3}}p_{Q}(a_{k}^{3}|x_{k}^{3},\lambda_{k})(-1)^{\Re}\\ &\times p(b|\lambda_{1}\cdots\lambda_{n})|)^{\frac{2}{n}}\\ =&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{1}\rangle_{\lambda_{k}}^{Q}\langle y_{1}^{1}\rangle_{\lambda}|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}\langle y_{1}^{2}\rangle_{\lambda}|)^{\frac{2}{n}}\\ +&(|\sum\limits_{\lambda_{k}}\prod\limits_{k=1}^{n}p(\lambda_{k})\langle x_{k}^{3}\rangle^{Q}_{\lambda_{k}}\langle y_{1}^{3}\rangle_{\lambda}|)^{\frac{2}{n}}\\ \leq&\sum\limits_{\lambda_{k}}(|\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{1}\rangle_{\lambda_{k}}^{Q}\langle y^{1}_{1}\rangle_{\lambda}|)^{\frac{2}{n}}\\ &+\sum\limits_{\lambda_{k}}(|\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{2}\rangle_{\lambda_{k}}^{Q}\langle y_{1}^{2}\rangle_{\lambda}|)^{\frac{2}{n}}\\ &+\sum\limits_{\lambda_{k}}(|\prod\limits_{k=1}^{n}p(\lambda_{k})|\langle x_{k}^{3}\rangle_{\lambda_{k}}^{Q}\langle y_{1}^{3}\rangle_{\lambda}|)^{\frac{2}{n}}\\ \leq&\prod\limits_{k=1}^{n}(\sum\limits_{\lambda_{k}}p(\lambda_{k})((\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}})^{2}+(\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}})^{2}+(\langle x_{k}^{3}\rangle^{Q}_{\lambda_{k}})^{2})^{\frac{1}{n}}\\ \leq&1,\end{aligned}

where the equalities are attained by using the definition of expectation values and the NLHV-LHS model. The first inequality is attained by using the absolute value inequality. The second inequality is attained by using the Lemma 1 in [25], the last inequality uses (xk1λkQ)2+(xk2λkQ)2+(xk3λkQ)21.({\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}}})^{2}+({\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}})^{2}+({\langle x_{k}^{3}\rangle^{Q}_{\lambda_{k}}})^{2}\leq 1. The proof of inequalities (LABEL:2-set-nodd-2),(LABEL:2-set-neven-2),(LABEL:3-set-nodd-2) and (LABEL:3-set-neven-2) is similar, but we use (xk1λkQ)+(xk2λkQ)2({\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}}})+({\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}})\leq\sqrt{2} and (xk1λkQ)+(xk2λkQ)+(xk3λkQ)3({\langle x_{k}^{1}\rangle^{Q}_{\lambda_{k}}})+({\langle x_{k}^{2}\rangle^{Q}_{\lambda_{k}}})+({\langle x_{k}^{3}\rangle^{Q}_{\lambda_{k}}})\leq\sqrt{3} in the last step.


Theorem 3. The star network state ρA1AnB\rho_{A_{1}\cdots A_{n}B} admits NLHS model from the central party to the edge parties if

|J1|1n+|J2|1n+|J3|1n+|J4|1n4,\displaystyle\begin{aligned} |J_{1}|^{\frac{1}{n}}+|J_{2}|^{\frac{1}{n}}+|J_{3}|^{\frac{1}{n}}+|J_{4}|^{\frac{1}{n}}\leq 4,\end{aligned} (33)

where

J1=i=1n(xi1+xi2+xi3)B1,J2=i=1n(xi1+xi2xi3)B2,J3=i=1n(xi1xi2+xi3)B3,J4=i=1n(xi1+xi2+xi3)B4\displaystyle\begin{aligned} &J_{1}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}+x_{i}^{3})\otimes B_{1}\rangle,\\ &J_{2}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}+x_{i}^{2}-x_{i}^{3})\otimes B_{2}\rangle,\\ &J_{3}=\langle\bigotimes\limits_{i=1}^{n}(x_{i}^{1}-x_{i}^{2}+x_{i}^{3})\otimes B_{3}\rangle,\\ &J_{4}=\langle\bigotimes\limits_{i=1}^{n}(-x_{i}^{1}+x_{i}^{2}+x_{i}^{3})\otimes B_{4}\rangle\\ \end{aligned}

and xijx_{i}^{j} (i=1,,n,j=1,2,3)(i=1,\cdots,n,\,j=1,2,3) are all mutually unbiased measurements.

Proof of Theorem 3:

If ρAB=k=1nρAkBk\rho_{AB}=\bigotimes\limits_{k=1}^{n}\rho_{A_{k}B_{k}} admits NLHV-LHS model from the central parties to the edge parties in three measurement settings, using the definite of NLHV-NLHS model and |Bi|1|\langle B_{i}\rangle|\leq 1, we have

|J1|=i=1n|(xi1+xi2+xi3)B1|i=1n|xi1+xi2+xi3|,|J2|=i=1n|(xi1+xi2xi3)B2|i=1n|xi1+xi2xi3|,|J3|=i=1n|(xi1xi2+xi3)B3|i=1n|xi1xi2+xi3|,|J4|=i=1n|(xi1+xi2+xi3)B4|i=1n|xi1+xi2+xi3|\displaystyle\begin{aligned} &|J_{1}|=\bigotimes\limits_{i=1}^{n}|(\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle)\langle B_{1}\rangle|\\ &\quad\quad\leq\bigotimes\limits_{i=1}^{n}|\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|,\\ &|J_{2}|=\bigotimes\limits_{i=1}^{n}|(\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle-\langle x_{i}^{3}\rangle)\langle B_{2}\rangle|\\ &\quad\quad\leq\bigotimes\limits_{i=1}^{n}|\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle-\langle x_{i}^{3}\rangle|,\\ &|J_{3}|=\bigotimes\limits_{i=1}^{n}|(\langle x_{i}^{1}\rangle-\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle)\langle B_{3}\rangle|\\ &\quad\quad\leq\bigotimes\limits_{i=1}^{n}|\langle x_{i}^{1}\rangle-\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|,\\ &|J_{4}|=\bigotimes\limits_{i=1}^{n}|(-\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle)\langle B_{4}\rangle|\\ &\quad\quad\leq\bigotimes\limits_{i=1}^{n}|-\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|\end{aligned}

and hence

|J1|1n+|J2|1n+|J3|1n+|J4|1ni=1n[|xi1+xi2+xi3|+|xi1+xi2xi3|+|xi1xi2+xi3|+|xi1+xi2+xi3|]1n4,\displaystyle\begin{aligned} &|J_{1}|^{\frac{1}{n}}+|J_{2}|^{\frac{1}{n}}+|J_{3}|^{\frac{1}{n}}+|J_{4}|^{\frac{1}{n}}\\ \leq&\bigotimes\limits_{i=1}^{n}[|\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|+|\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle-\langle x_{i}^{3}\rangle|\\ +&|\langle x_{i}^{1}\rangle-\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|+|-\langle x_{i}^{1}\rangle+\langle x_{i}^{2}\rangle+\langle x_{i}^{3}\rangle|]^{\frac{1}{n}}\\ \leq&4,\end{aligned}

where the first inequality is attained by using Lemma 1 in [25], and the second inequality is attained by using |xi1±xi2±xi3|=±(xi1±xi2±xi3).|\langle x_{i}^{1}\rangle\pm\langle x_{i}^{2}\rangle\pm\langle x_{i}^{3}\rangle|=\pm(\langle x_{i}^{1}\rangle\pm\langle x_{i}^{2}\rangle\pm\langle x_{i}^{3}\rangle).

Theorem 4. If ρA1AnB\rho_{\it{A_{1}\cdots A_{n}B}} admits NLHV-BLHS model from the central party to the edge parties when Bob performs the fixed measurement given in Theorem 2 and the edge parties perform mutually unbiased measurements, we have

i1,,inC|x1i1x2i2xniny1i1in|122n22\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y_{1}^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\\ &\leq 2^{n-2}\sqrt{2}\end{aligned} (34)

and

i1,,inCC|x1i1x2i2xniny1i1in|122n23+2n21.\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y_{1}^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\\ &\leq 2^{n-2}\sqrt{3}+2^{n-2}-1.\end{aligned} (35)

Proof of Theorem 4

Firstly we prove the following lemmas:

Lemma (1):i1,,in=12|x1i1x2i2xninQ|2n12.\sum\limits_{i_{1},\cdots,i_{n}=1}^{2}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\rangle^{Q}|\leq 2^{n-1}\sqrt{2}.

Lemma (2): i1,,in=12|x1i1x2i2xninQ|+i1,,inC0|x1i1x2i2xninQ|2n13+2n11\sum\limits_{i_{1},\cdots,i_{n}=1}^{2}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\rangle^{Q}|+\sum\limits_{i_{1},\cdots,i_{n}\in C_{0}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\rangle^{Q}|\leq 2^{n-1}\sqrt{3}+2^{n-1}-1, where C0C_{0} is the set consisting of all the strings with at least one 3,3, namely the set {300,0300,,0003,330,0,,00,033,\{30\cdots 0,030\cdots 0,\cdots,00\cdots 03,330\cdots,0,\cdots,00\cdots,033, ,33,3}\cdots,33\cdots,3\}. Here xQ\langle x\rangle^{Q} represents the expectation value of xx with respect to the quantum states.

Proof of Lemma (1): Firstly we have

|x11Q|+|x12Q|maxk1,k2{ek1,k2}=maxk1,k2Tk1,k222,\displaystyle\begin{aligned} &|\langle x_{1}^{1}\rangle^{Q}|+|\langle x_{1}^{2}\rangle^{Q}|\\ \leq&\max\limits_{k_{1},k_{2}}\{e_{k_{1},k_{2}}\}=\max\limits_{k_{1},k_{2}}\|T_{k_{1},k_{2}}\|_{2}\leq\sqrt{2},\end{aligned}

where Tk1k2=(1)k1x11+(1)k2x12,T_{k_{1}k_{2}}=(-1)^{k_{1}}x_{1}^{1}+(-1)^{k_{2}}x_{1}^{2}, ek1,k2e_{k_{1},k_{2}} is the maximum eigenvalue of Tk1k2,T_{k_{1}k_{2}}, i.e., the 22-norm of Tk1k2.T_{k_{1}k_{2}}. ek1,k2=2e_{k_{1},k_{2}}=\sqrt{2} for the mutually unbiased measurements x11x_{1}^{1} and x22.x_{2}^{2}.

From

i1,i2=12|x1i1x2i2Q|maxkj,kj,kj′′Tk1k2x21+Tk1k2x22Tk1k22x212+Tk1k22x222Tk1k22+Tk1k2222,\displaystyle\begin{aligned} &\sum\limits_{i_{1},i_{2}=1}^{2}|\langle x_{1}^{i_{1}}\otimes x_{2}^{i_{2}}\rangle^{Q}|\\ \leq&\max\limits_{k_{j},k_{j}^{\prime},k_{j}^{{}^{\prime\prime}}}\|T_{k_{1}k_{2}}\otimes x_{2}^{1}+T_{k^{\prime}_{1}k^{\prime}_{2}}\otimes x_{2}^{2}\|\\ \leq&\|T_{k_{1}k_{2}}\|_{2}\|x_{2}^{1}\|_{2}+\|T_{k^{\prime}_{1}k^{\prime}_{2}}\|_{2}\|x_{2}^{2}\|_{2}\\ \leq&\|T_{k_{1}k_{2}}\|_{2}+\|T_{k^{\prime}_{1}k^{\prime}_{2}}\|_{2}\leq 2\sqrt{2},\end{aligned}

where the first inequality is attained by using the definition of i1,i2=12|x1i1x2i2Q|\sum\limits_{i_{1},i_{2}=1}^{2}|\langle x_{1}^{i_{1}}\otimes x_{2}^{i_{2}}\rangle^{Q}|, the second and the third inequalities are attained using the norm inequality and xkik21,\|x_{k}^{i_{k}}\|_{2}\leq 1, and the fourth inequality is due to Tk1k222\|T_{k_{1}k_{2}}\|_{2}\leq\sqrt{2} and Tk1k222.\|T_{k^{\prime}_{1}k^{\prime}_{2}}\|_{2}\leq\sqrt{2}. Using the same method and the mathematical induction, we can prove the Lemma (1).

Proof of Lemma (2): Firstly we have

|x11Q|+|x12Q|+|x13Q|maxk1,k2=0,1{ek1,k2}=maxk1,k2=0,1Tk1,k223,\displaystyle\begin{aligned} &|\langle x_{1}^{1}\rangle^{Q}|+|\langle x_{1}^{2}\rangle^{Q}|+|\langle x_{1}^{3}\rangle^{Q}|\\ \leq&\max\limits_{k_{1},k_{2}=0,1}\{e_{k_{1},k_{2}}\}=\max\limits_{k_{1},k_{2}=0,1}\|T_{k_{1},k_{2}}\|_{2}\leq\sqrt{3},\end{aligned}

where Tk1k2=(1)k1x11+(1)k2x12+x13=[1,(1)k1(1)k2i;(1)k1+(1)k2i,1],T_{k_{1}k_{2}}=(-1)^{k_{1}}x_{1}^{1}+(-1)^{k_{2}}x_{1}^{2}+x_{1}^{3}=[1,(-1)^{k_{1}}-(-1)^{k_{2}}\rm{i};(-1)^{k_{1}}+(-1)^{k_{2}}\rm{i},-1], ek1,k2e_{k_{1},k_{2}} is the maximum eigenvalue of Tk1k2.T_{k_{1}k_{2}}. ek1,k2=3e_{k_{1},k_{2}}=\sqrt{3} for the mutually unbiased measurements x11,x_{1}^{1}, x12x_{1}^{2} and x23.x_{2}^{3}.

Moreover,

i1,i2=12|x1i1x2i2Q|+i1,i2C0|x1i1x2i2Q|maxk1,,k5e(Tk1k5)+|x13x23Q|23+1,\displaystyle\begin{aligned} &\sum\limits_{i_{1},i_{2}=1}^{2}|\langle x_{1}^{i_{1}}\otimes x_{2}^{i_{2}}\rangle^{Q}|+\sum\limits_{i_{1},i_{2}\in C_{0}}|\langle x_{1}^{i_{1}}\otimes x_{2}^{i_{2}}\rangle^{Q}|\\ \leq&\max\limits_{k_{1},\cdots,k_{5}}e(T_{k_{1}\cdots k_{5}})+|\langle x_{1}^{3}\otimes x_{2}^{3}\rangle^{Q}|\\ \leq&2\sqrt{3}+1,\end{aligned}

where Tk1k5=(1)k1x11x21+(1)k2x11x22+(1)k3x12x21+(1)k4x12x22+(1)k5x10x23+x13x20T_{k_{1}\cdots k_{5}}=(-1)^{k_{1}}x_{1}^{1}\otimes x_{2}^{1}+(-1)^{k_{2}}x_{1}^{1}\otimes x_{2}^{2}+(-1)^{k_{3}}x_{1}^{2}\otimes x_{2}^{1}+(-1)^{k_{4}}x_{1}^{2}\otimes x_{2}^{2}+(-1)^{k_{5}}x_{1}^{0}\otimes x_{2}^{3}+x_{1}^{3}\otimes x_{2}^{0} and e(Tk1k5)e(T_{k_{1}\cdots k_{5}}) is the maximum eigenvalue of Tk1k5.T_{k_{1}\cdots k_{5}}. The first inequality is attained by using the definition of |x1i1x2i2Q||\langle x_{1}^{i_{1}}\otimes x_{2}^{i_{2}}\rangle^{Q}|. The maximum value of e(Tk1k5)e(T_{k_{1}\cdots k_{5}}) is attained when Tk1k5=[2,0,0,2±i;0,0,0,0;0,0,0,0;2i,0,0,2].T_{k_{1}\cdots k_{5}}=[2,0,0,2\pm\rm{i};0,0,0,0;0,0,0,0;2\mp\rm{i},0,0,-2].

Similarly we can prove the inequality for general nn. C0C_{0} can be split into two subsets C1C_{1} and C2,C_{2}, where C1C_{1} contains the elements with odd number of 33 and C2=CC_{2}=C^{\prime} contains the elements with even number of 3.3. The maximum value of i1,,inC2|x1i1xninQ|\sum\limits_{i_{1},\cdots,i_{n}\in C_{2}}|\langle x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}}\rangle^{Q}| is 2n112^{n-1}-1 as each |x1i1xninQ|1|\langle x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}}\rangle^{Q}|\leq 1, and the number of the elements in C2C_{2} is 2n11.2^{n-1}-1. The maximum value of i1,,in=12|x1i1xninQ|+i1,,inC1|x1i1xninQ|maxeig(T)\sum\limits_{i_{1},\cdots,i_{n}=1}^{2}|\langle x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}}\rangle^{Q}|+\sum\limits_{i_{1},\cdots,i_{n}\in C_{1}}|\langle x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}}\rangle^{Q}|\leq\max\rm{eig}(T) is attained when T=i1,,in=12±x1i1xnin+i1,,inC1±x1i1xninT=\sum\limits_{i_{1},\cdots,i_{n}=1}^{2}\pm x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}}+\sum\limits_{i_{1},\cdots,i_{n}\in C_{1}}\pm x_{1}^{i_{1}}\otimes\cdots\otimes x_{n}^{i_{n}} is the matrix with entries T11=T2n,2n=2n1,T_{11}=-T_{2^{n},2^{n}}=2^{n-1}, T1,2n=2n1(1+i),T_{1,2^{n}}=2^{n-1}(1+\rm{i}), T2n,1=2n1(1i)T_{2^{n},1}=2^{n-1}(1-\rm{i}) and other entries being 0. Here maxeig(T)=2n13\max\rm{eig}(T)=2^{n-1}\sqrt{3} is the maximum eigenvalue of TT. Next we prove the theorem. If ρA1AnB\rho_{A_{1}\cdots A_{n}B} admits LHV-BLHS model, we have

i1,,inC|x1i1x2i2xniny1i1in|12=i1,,inC|λ1λns=1n2t=1Cnsqstp(Λst)p(Λ~st)yi1in×xKstiKstΛstQxKst¯iKst¯Λ~stQ|12s=1n2i1,,inCλ1λnt=1Cns|qstp(Λst)p(Λ~st)yi1in×xKstiKstΛstQxKst¯iKst¯Λ~stQ|12\displaystyle\begin{aligned} &\sum\limits_{i_{1},\cdots,i_{n}\in C}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y_{1}^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\\ =&\sum\limits_{i_{1},\cdots,i_{n}\in C}|\sum\limits_{\lambda_{1}\cdots\lambda_{n}}\sum\limits_{s=1}^{\lfloor\frac{n}{2}\rfloor}\sum\limits_{t=1}^{C_{n}^{s}}q_{s}^{t}p(\Lambda_{s}^{t})p(\tilde{\Lambda}_{s}^{t})\langle y^{i_{1}\cdots i_{n}}\rangle\\ &\times\langle x_{K_{s}^{t}}^{i_{K_{s}^{t}}}\rangle^{Q}_{\Lambda_{s}^{t}}\langle x_{\overline{K_{s}^{t}}}^{i_{\overline{K_{s}^{t}}}}\rangle^{Q}_{\tilde{\Lambda}_{s}^{t}}|^{\frac{1}{2}}\\ \leq&\sum\limits_{s=1}^{\lfloor\frac{n}{2}\rfloor}\sum\limits_{i_{1},\cdots,i_{n}\in C}\sum\limits_{\lambda_{1}\cdots\lambda_{n}}\sum\limits_{t=1}^{C_{n}^{s}}|q_{s}^{t}p(\Lambda_{s}^{t})p(\tilde{\Lambda}_{s}^{t})\langle y^{i_{1}\cdots i_{n}}\rangle\\ \times&\langle x_{K_{s}^{t}}^{i_{K_{s}^{t}}}\rangle^{Q}_{\Lambda_{s}^{t}}\langle x_{\overline{K_{s}^{t}}}^{i_{\overline{K_{s}^{t}}}}\rangle^{Q}_{\tilde{\Lambda}_{s}^{t}}|^{\frac{1}{2}}\\ \end{aligned}
s=1n2|t=1CnsΛstqstp(Λst)2n1sxKstiKstΛstQ|12×|t=1CnsΛ~ksqstp(Λ~st)iKst¯2s1xKst¯iKst¯Λ~stQ|122n22,\displaystyle\begin{aligned} \leq&\sum\limits_{s=1}^{\lfloor\frac{n}{2}\rfloor}|\sum\limits_{t=1}^{C_{n}^{s}}\sum\limits_{\Lambda_{s}^{t}}\sqrt{q_{s}^{t}}p(\Lambda_{s}^{t})2^{n-1-s}\langle x_{K_{s}^{t}}^{i_{K_{s}^{t}}}\rangle^{Q}_{\Lambda_{s}^{t}}|^{\frac{1}{2}}\\ &\times|\sum\limits_{t=1}^{C_{n}^{s}}\sum\limits_{\tilde{\Lambda}_{k}^{s}}\sqrt{q_{s}^{t}}p(\tilde{\Lambda}_{s}^{t})\sum\limits_{i_{\overline{K_{s}^{t}}}}2^{s-1}\langle x_{\overline{K_{s}^{t}}}^{i_{\overline{K_{s}^{t}}}}\rangle^{Q}_{\tilde{\Lambda}_{s}^{t}}|^{\frac{1}{2}}\\ \leq&2^{n-2}\sqrt{2},\end{aligned}

where the first equality is attained by the definition of LHV-BLHS model, the first inequality is attained by x1+xnx1++xn,\sqrt{x_{1}+\cdots x_{n}}\leq\sqrt{x_{1}}+\cdots+\sqrt{x_{n}}, the second inequality is attained by Cauchy-Schwarz inequality, and the last inequality is attained by the Lemma (1).

The inequality i1,,inCC|x1i1x2i2xniny1i1in|122n23+2n21\sum\limits_{i_{1},\cdots,i_{n}\in C\cup C^{\prime}}|\langle x^{i_{1}}_{1}\otimes x^{i_{2}}_{2}\otimes\cdots\otimes x^{i_{n}}_{n}\otimes y_{1}^{i_{1}\cdots i_{n}}\rangle|^{\frac{1}{2}}\leq 2^{n-2}\sqrt{3}+2^{n-2}-1 can be similarly proved, by using the Cauchy-Schwarz inequality and Lemma (2).

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