Research article

Clustering quantum Markov chains on trees associated with open quantum random walks

  • Received: 14 May 2023 Revised: 15 June 2023 Accepted: 19 June 2023 Published: 19 July 2023
  • MSC : 35Qxx, 60Jxx, 81-XX

  • In networks, the Markov clustering (MCL) algorithm is one of the most efficient approaches in detecting clustered structures. The MCL algorithm takes as input a stochastic matrix, which depends on the adjacency matrix of the graph network under consideration. Quantum clustering algorithms are proven to be superefficient over the classical ones. Motivated by the idea of a potential clustering algorithm based on quantum Markov chains, we prove a clustering property for quantum Markov chains (QMCs) on Cayley trees associated with open quantum random walks (OQRW).

    Citation: Luigi Accardi, Amenallah Andolsi, Farrukh Mukhamedov, Mohamed Rhaima, Abdessatar Souissi. Clustering quantum Markov chains on trees associated with open quantum random walks[J]. AIMS Mathematics, 2023, 8(10): 23003-23015. doi: 10.3934/math.20231170

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  • In networks, the Markov clustering (MCL) algorithm is one of the most efficient approaches in detecting clustered structures. The MCL algorithm takes as input a stochastic matrix, which depends on the adjacency matrix of the graph network under consideration. Quantum clustering algorithms are proven to be superefficient over the classical ones. Motivated by the idea of a potential clustering algorithm based on quantum Markov chains, we prove a clustering property for quantum Markov chains (QMCs) on Cayley trees associated with open quantum random walks (OQRW).



    Markov chains and random walks find widespread applications in several areas. Markov chains-based algorithms play crucial in unsupervised Machine learning and networks, such as the Markov clustering algorithm [41,42], which is proven to be one of the most powerful approaches for detecting clustered structures. Quantum Markov Chains (QMCs) [1] have been introduced long ago [2,6] and found important applications in physics [7,14,15,16,40]. QMCs on trees [4,5,22,23,30,36,38] have been studied in connection with statistical mechanical models [3,24,27,36]. In particular, quantum phase transitions were investigated for Pauli type models [19,20,21]. A variety of aspects of quantum Markov states on trees have been investigated [26,27,28,29].

    Over the last few decades, quantum random walks [8] have been the subject of a significant amount of research due to their utility in a variety of domains, such as quantum information and networks [33,34]. In [8], OQRWs have been introduced in the unitary case. In [9], Attal et al. extended this approach and also considered OQRWs on graphs. The inclusion of OQRWs in the general frame of QMCs has been established in [12,18] and then extended to QMCs on trees [31,32]. Stopping rules and recurrence for QMCs were introduced in [37]. In addition, in recent works [31,32], QMCs on trees have been associated with OQRWs. This led to further applications, such as quantum phase transitions and recurrence of QMCs on trees [11,37].

    In quantum statistical mechanics, the clustering property for a state indicates the absence of long-range order [17,35]. In [30], we investigate the clustering property for a class of QMCs on the Comb graph. In [20,39], it was shown that a QMC associated with an XY-Ising model on the Cayley tree satisfy the clustering property. In the present paper, we show that the QMC associated with the disordered phase of a quantum system based on OQRW does not satisfy the clustering property. To the best of our knowledge, non-clustering QMCs on tree have not been addressed previously in the literature. Further relevant problems can be investigated, such as the types of von Neumann algebras associated with the QMCs under consideration, such as [25]. The obtained results can have important and promising implications in Markov models in data science.

    The paper is organized as follows: Section 2 is devoted to some preliminaries on trees. In Section 3, we introduce QMCs associated with OQRW on trees. Section 4 is dedicated to the main result of the paper.

    Let Γk+=(V,E) be semi-infinite Cayley tree of order k. Denote oV the root of the tree. Two vertices x and y are called nearest-neighbors if there exists an edge joining them, we denote xy. Let u and v be two different vertices, we call edge-path with length nN joining u to v a finite list of vertices u1,u2,,un such that uu1u2un=v. It is well known that, a tree can be characterized through the property that any two distinct vertices are joined by means of a unique edge-path. The distance on the tree d(u,v) between u and v is the length of the unique edge-path joining them. The hierarchical structure of Γk+ allows to define the levels

    Wm:={uV:d(u,o)=m}.

    On the levels, a coordinate structure is assigned as follows. For mN and xWm is identified to a n-uplet x(1,,m), where j{1,,k}, 1jm. The coordinate structure is illustrated in Figure 1 in the case of the Cayley tree of order two. In the above notations, we write

    Wm={(1,2,,m);j=1,2,,k}.
    Figure 1.  Coordinate structure on Γ3+.

    Define

    Λn=nj=0Wj;Λ[m,n]=nj=mWj.

    For 1,2.,n{1,2,,k} and u=(1,2,,n)Wn there exists a unique path joining it to the root o given as follows:

    ou1=(1)u2=(1,2)un1=(1,2,,n1)u.

    Let u=(1,2,,m)Wm, the shift αu on the tree is defined as

    αu(u)=(1,2,,n,1,2,m)Wn+m.

    In particular, αu(o)=u. For each uWn, we define its set of direct successors by

    S(u)={vWn+1:uv}={(u,1),(u,2),,(u,k)}. (2.1)

    Put

    Vu={v=uu:uV}. (2.2)

    Recall that a graph isomorphism [13] is an edge-preserving bijection from a graph G1=(V1,E1) onto a graph G2=(V2,E2) such that:

    - α is a bijective map from V1 onto V2;

    - for every x,yV1 one has xy if and only if α(x)α(y).

    The sub-tree Γk+,u=(Vu,Eu), whose vertex set is Vu, is isomorphic to Γk+. For each nN, we define

    Wu; n={vVu:d(u,v)=n}=αu(Wn),Λu; n=nj=0Wu; j=αu(Λn).

    The map αu is a graph isomorphism from Γk+=(V,E) onto Γk+,u=(Vu,Eu), we denote its inverse isomorphism by α1u.

    To each vertex xV, we assign the C–algebra of observables Ax=A with unit 1Ix. For any finite region VV, we consider the local algebra AV=xVAx. In particular, for each n, one defines AΛn=uΛnAu. One has the embedding

    AΛnAΛn1IWn+1AΛn+1,

    where for each finite region VV, one has 1IV=uV1Iu. We obtain the following local algebra associated with the increasing set {AΛn}n0

    AV,loc=nNAΛn,

    and its C-closure [10] is the following quasi-local algebra

    AV=¯AV,locC.

    For aA and xV, we denote a(x)=a1IV{x}, where a appears at the component Au of the infinite tensor product AV. Notice that, the graph isomorphism αu defines a isomorphism ˜αu from AV into AVu satisfying

    ˜αu(xΛnax)=yΛu;na(y)α1u(y), (2.3)

    where for each yΛu;n by α1u(y) we mean the element xΛn satisfying αu(x)=y.

    Let CB be two C-algebras. We call transition expectation (TE), any completely positive identity preserving (CP1) from B into C. Let CBA be unitary C–algebras. Recall that:

    ● A quasi-conditional expectation (QCE) is a CP1 linear map E:AB such that

    E(ca)=cE(a),aA,cC.

    ● A TE is any CP1 linear map between two unitary C-algebras.

    The set of states on a C–algebra A will be denoted by S(A).

    For a given TE EWn from AΛ[n,n+1] into AWn, the map

    EΛn=idAΛn1EWn (2.4)

    is a TE w.r.t. the triplet AΛn1AΛnAΛn+1. The hierarchical structure of the Cayley tree manifests in the fact that

    Wn+1=uWnS(u).

    This allows to consider local TE Eu from A{u}S(u) into Au. Then the map

    En:=uWnEu

    defines a TE from AΛ[n,n+1] into AWn.

    Definition 2.1. [6] A (backward) QMC on AV is defined to be a triplet (ϕo,(En)n0,(hn)n), where

    ϕoS(Ao) is an initial state,

    for each n, En is a TE from AΛ[n,n+1] into AWn,

    for each n, hnAWn,+ is a positive boundary condition,

    such that for each aAV the limit

    φ(a):=limnϕ0EΛ0EΛ1EΛn(h1/2n+1ah1/2n+1), (2.5)

    exists in the weak-*-topology and defines a state φ on AV, which will be also referred as QMC.

    Definition 2.2. [38] The triplet φ(ϕo,(EΛn)n0,(hn)n) is called a tree-homogeneous QMC (THQMC) if there exists a TE E:A{o}S(o)Ao such that for each n

    EΛn=idAΛn1]uWnαuEα1u (2.6)

    where idAΛn1 is the identity map on AΛn1 and

    hn = uWnαu(h) (2.7)

    for some boundary condition hAo;+.

    In the sequel, for the sake of simplicity we denote a(u):=αu(a) for each aA and uV.

    Theorem 2.1. Let ϕo be a state on Ao and E:AΛ1Ao a TE. For hA+, if

    ϕo(h(o))=1, (2.8)
    E(1I(o)h(1)h(2)h(k))=h(o), (2.9)

    then (ϕo,E,h) is a THQMC on the algebra AV.

    Let H and K be two separable Hilbert spaces. Let B(H) (respectively B(K)) be the algebra of all bounded operators over H (respectively K) with identity 1IH (respectively 1IK). Let {|i:iΛ} be an orthonormal basis of K, where Λ is a connected graph. The algebra of observables at a given site uV is Au=B(H)B(K)B(HK) with identity 1Iu=1IH1IK. In the notations of the previous section, for eachaB(H)B(K) we denote αu(a)=a(u)Au. For each (i,j)Λ2, the quantum transition from the state |j into the state |i is implemented by an operator BijB(H) such that

    iΛBijBij=1IH. (3.1)

    Consider a density operator ρB(HK), of the form

    ρ=iΛρi|ii|,ρiB(H)+{0},

    where B(H)+ is the cone of positive operators over H.

    For each uV, we set

    Mij=Bij|ij|B(H)B(K). (3.2)

    Put

    Aij:=1Tr(ρj)1/2ρ1/2j|ij|,withi,jΛ, (3.3)
    Kij:=Mij(u)vS(u)Aij(v)A{u}S(u), (3.4)

    where b(x)=αx(b) for every bB(HK) and xV.

    Let

    E(a)=Tru]((i,j)Λ2Kija(i,j)Λ2Kij),

    where Tru] is the partial trace defined by linear extension of

    Tru](aua(u,1)a(u,k))=Tr(a(u,1))Tr(a(u,k))au.

    For a=auau,1au,k one shows that

    E(a)=(i,j,j)Λ3Mij(u)auMij(u)(k=1φj,j(au,)), (3.5)

    where

    φjj(b):=1Tr(ρj)1/2Tr(ρj)1/2Tr(ρ1/2jρ1/2j|jj|b),bB(H)B(K). (3.6)

    Theorem 3.1. With the above notations, if ωoAo;+ is an initial state and hAo;+ is a boundary condition such that

    Tr(ωoho)=1, (3.7)
    i,j,jΛMijMijk=1φj,j(h(u,))=hu. (3.8)

    Then the triplet (ωo,(Eu)uV,(hu)uV) defines a quantum Markov chain φ on the algebra AV. Moreover, for each a=uΛnauAΛn one has

    φ(a)=j,jΛTr(ωoMjj(ao))uΛ[1,n]ψj,j(au)vΛn+1φj,j(h(v)), (3.9)

    where Eu is given by (3.5), the functional φjj is given by (3.6), and

    Mjj()=iΛMijMij, (3.10)
    ψj,j(b)=1Tr(ρj)1/2Tr(ρj)1/2iΛTr(Bijρ1/2jρ1/2jBij|ii|b). (3.11)

    Proof. See [31].

    The forward Markov operator associated with the TE (3.5) is defined from Au into itself as follows

    Px;f(ax):=Ex(ax1Ix,11Ix,k) (3.12)

    and for each {1,2,,k}, the th backward Markov operator is defined on Ax, into Ax by

    Px,;b(ax,):=Ex(1Ix1Ix,11Ix,1ax,1Ix,+11Ix,k). (3.13)

    In previous works [31,38], it was shown that the boundary condition h=1I corresponds to the QMC associated with the disordered phase of the system. One finds

    Px;f(ax)=i,j,jMijaxMij(φjj(1I))k=i,jMijaxMij (3.14)

    and

    Px,;b(ax,)=i,j,jMijMij(φjj(1I))k1φjj(ax,)(3.1)=j1IH|jj|φjj(ax,). (3.15)

    In this section, we restrict ourselves to the case h=1I. Indeed, thanks to (3.1) the boundary condition h=1I is solution of (3.8). The corresponding QMC evaluated on localized elements a=xΛmax is given by

    φ(a)=jΛTr(ωoMjj(ao))uΛ[1,n]ψjj(au). (4.1)

    Definition 4.1. A state ψ on AV is said to be clustering (mixing) if

    limuV;|u|φ(aαu(b))=φ(a)φ(b),(a,b)AV, (4.2)

    where |u|=d(u,o).

    Theorem 4.1. Let φ(ϕo,E,h=1I) then

    (i) Let m0 be an integer, for every a,bAΛm,

    limu;|u|φ(aαu(b))=jΛϕo(Mjj(a0))xΛ[1,m]ψjj(ax)φjj(ˆb), (4.3)

    where

    ˆb:=Eo(boEW1(bW1EWm(bWm1IWm+1))). (4.4)

    (ii) The QMC φ given by (3.9) is clustering if and only if |Λ|=1.

    Proof. (i) Let u=(1,2,,n). Let a,bAV;loc. Without lose of generality, we can assume that a=xΛmax,b=xΛmbxAΛm. For FΛm, we denote bF=xFbx, one can see that

    αu(bWj)=vWu;jb(v)α1u(v)=:vWu;jbv=bWu;j.

    We find

    αu(ˆb)=Eu(b(u)oEWu;1(b(Wu;1)W1EWu;m(b(Wu;m)Wm1IWm+1))).

    On the other hand, we have

    EWn+m(bWu;m1IWn+mWu;m1IWn+m)=vWu;mEv(bv1IS(v))wWn+mWu;mEw(1IwS(w))(3.12)=vWu;mPv;f(bv)1IWn+mWu;m.

    Then

    EWn+m1(bWu;m1IWn+m1Wu;m1EWn+m(bWu;m1IWn+mWu;m1IWn+m))=EWn+m1(bWu;m1IWn+m1Wu;m1vWu;mPv;f(bv)1IWn+mWu;m)=wWu;m1Ew(bwvS(w)Pv;f(bv))1IWn+m1Wu;m1,

    iterating the above procedure, we get

    EWn(buEWn+1(bWu;1EWn+m(bWu;m1IWn+m)))(4.4)=ˆbu1IWn{u}.

    Denote uj=(1,2,,j) for each j{1,2,,n}. For cjAuj, we have

    EWj1(1IWj1cuj1IWj{uj})(3.13)=Puj1;;b(cuj)1IWj1{uj1}.

    It follows that

    φ(aαu(b))=ϕo(E0(aoEW1(aW1EWm(aWmEWm+1(1IWm+1EWn1(1IWm1EWn(buEWn+1(bWu;1EWn+m(bWu;m1IWn+m)))))))))=ϕo(E0(aoEW1(aW1EWm(aWmEWm+1(1IWm+1EWn1(1IWm1ˆbu1IWn{u}))))))=ϕo(E0(aoEW1(aW1EWm(aWmEWm+1(1IWm+1EWn2(1IWm2Pun1;n;b(ˆbu)1IWn1{un1}))))))=ϕo(E0(aoEW1(aW1EWm(aWm˜Punum+1;b(ˆbu)1IWm+1{um+1})))),

    where

    ˜Punum+1;b(c)=Pum+1;m+2;bPum+2;m+3;bPun1;n;b(c),cAu.

    For cAui, we have

    Pui;i+1;bPui+1;i+2;b(c)(3.15)=Pui;i+1;b(j1IH|jj|φjj(c))=j1IH|jj|φjj(j1IH|jj|φjj(c))(3.6)=j,j1IH|jj|δj,jφjj(c)=j1IH|jj|φjj(c).

    This means that elements of the form c=j1IH|jj|φjj(ci) are invariant for the all the backward Markov operators Pui;;b. Then

    ˜Punum+1;b(ˆbu)=j1IH|jj|φjj(ˆbu).

    Therefore, using (3.9) we find (4.3).

    (ii) On the other hand, we have

    φ(a)φ(b)=φ(a)ϕo(ˆb)=jΛϕo(Mjj(a0))xΛ[1,m]ψjj(ax)ϕo(ˆb).

    Fix jΛ. For a=(1IH|jj|)(1), we get

    φ(aαu(b))=φjj(ˆb);φ(a)=1.

    Therefore, the QMC φ satisfies (4.2) if and only if φjj=ϕo, jΛ. If |Λ|>1, then for jj we have φjj(1IH|jj|)=10=φjj(1IH|jj|). If Λ is reduced to a singleton {j0}, the state φ is a product state. It is enough to take ϕ0=φj0j0 to get the clustering property. This finishes the proof.

    Remark 4.1. In (4.3), if a=1I we get

    limu; |u|φ(αu(b))=jΛϕo(1I|jj|)φjj(ˆb)=:φ(ˆb), (4.5)

    the limiting state φ on A is equitably distributed between the state φjj with respect to the initial state ϕo.

    From Theorem 4.1 the state φ is clustering if and only the OQRW is trivial and the walker occupies a single site Λ={i0}. In this case the QMC φ is a product state.

    Example 4.1. Let H=K=C2 with canonical basis (|1,|2) and Λ={1,2}. The algebra of observable is A=M2(C)M2(C)M4(C). The transitions of the OQRW are given by

    B11=(α00β),B12=(0100),B21=(γ00δ),B22=(1000),

    where α,β,γ,δC such that

    |α|2+|γ|2=|β|2+|δ|2=1andαγ0. (4.6)

    Put

    ρ1=(1000),ρ2=(0001),σ=(1001).

    The initial state is the normalized trace given by ϕo=14Tr. One has φjj(b)=Tr(ρj|jj|b) for each jΛ.

    Let a=b=σ|22|, from (3.5) we find

    Eo(a1I)=i,jMijaMij=jB2jσB2j|jj|=(|γ|200|δ|2)|11|+(1000)|22|.

    Therefore

    φ(a)=ϕ0(Eo(a))=14(|β|2+|γ|2).

    Since bAo then from (4.4) we have ˆb=Eo(b1I). It follows that

    φ11(ˆb)=|γ|2;φ22(ˆb)=0.

    In addition

    M11(a)=iMi1aMi1=B2 1|11|aB11|11|+B1 1|12|aB11|21|=(|γ|200|δ|2)|11|.

    Then (4.3) implies that

    limu;|u|φ(aαu(b))=ϕo(M11(a))φ11(ˆb)=14|γ|2(|γ|2|δ|2).

    Thus

    φ(a)ϕ(b)=116(|β|2+|γ|2)214|γ|2(|γ|2|δ|2)=φ(aαu(b)).

    Therefore, the state φ does not satisfy the clustering property. Moreover, from (4.5) limiting state is given by

    φ=122j=1φjjϕo.

    If in addition |β||γ|, then for uVΛ1, we have

    φ(b)=14(|β|2+|γ|2)φ(αu(b))=φ(ˆb)=12|γ|2,

    then the QMC φ is not invariant under the translation τu.

    In prior studies, significant characteristics of QMCs on trees, such as phase transition and recurrence, have been investigated. In the present paper, we examine the clustering property of a QMC approach on the Cayley tree associated OQRWs. This analysis reveals an additional ergodic property within the disordered phase of the quantum system under examination. Notably, our research shows promise in relation to the development of data clustering algorithms.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This research is funded by the "Researchers Supporting Project number (RSPD2023R683), King Saud University, Riyadh, Saudi Arabia".

    The authors have no conflicts of interest to declare.



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