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Supervisory Control of Quantum Discrete Event Systems

Daowen Qiu Institute of Quantum Computing and Computer Theory, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China (e-mail: [email protected]).

Abstract. Discrete event systems (DES) have been deeply developed and applied in practice, but state complexity in DES still is an important problem to be better solved with innovative methods. With the development of quantum computing and quantum control, a natural problem is to simulate DES by means of quantum computing models and to establish quantum DES (QDES). The motivation is twofold: on the one hand, QDES have potential applications when DES are simulated and processed by quantum computers, where quantum systems are employed to simulate the evolution of states driven by discrete events, and on the other hand, QDES may have essential advantages over DES concerning state complexity for imitating some practical problems. So, the goal of this paper is to establish a basic framework of QDES by using quantum finite automata (QFA) as the modelling formalisms, and the supervisory control theorems of QDES are established and proved. Then we present a polynomial-time algorithm to decide whether or not the controllability condition holds. In particular, we construct a number of new examples of QFA to illustrate the supervisory control of QDES and to verify the essential advantages of QDES over classical DES in state complexity.

Key words: Discrete Event Systems, Quantum Finite Automata, Quantum Computing, Supervisory Control, State Complexity.

AMS subject classification. 93C65, 81P68, 93B05, 68Q45

1 Introduction

Discrete Event Systems (DES) and Continuous-Variable Dynamic Systems (CVDS) are two important classes of control systems [1]. Roughly speaking, the goal of systems to be controlled is to achieve some desired specifications, and feedback control means using any available information from the system’s behavior to adjust the control’s input [1]. CVDES are time-varying dynamic systems and their state transitions are time-driven, but the state transitions of DES are event-driven. In general, the study of CVDS relies on differential-equation-based models, and DES are simulated usually by automata and Petri nets [1, 2].

More exactly, DES are formally dynamical systems whose states are discrete and the evolutions of its states are driven by the occurrence of events [3, 1, 2]. As Kornyak mentioned [4], the study of discrete systems is also important from the practical point of view since many physical objects are in fact discrete. As a precise model in logic level, DES have been applied to many real-world systems, such as traffic systems, manufacturing systems, smart grids systems, database management systems, communication protocols, and logistic (service) systems, etc. However, for some practical systems modeled by large-scale states [2], the complexity of processing systems still needs to be solved appropriately.

Supervisory Control Theory (SCT) of DES is a basic and important subject in DES [3, 1, 2], and it was originally proposed by Ramadge and Wonham [3, 1, 2]. A DES and the control specification are modeled as automata. The task of supervisors is to ensure that the supervised (or closed-loop) system generates a certain language called specification language.

SCT of DES exactly supports the formulation of various control problems of standard types, and it usually is automaton-based. Briefly, a DES is modeled as the generator (an automaton) of a formal language, and certain events (transitions) can be disabled by an external controller. The idea is to construct this controller so that the events it currently disables depend on the past behavior of the DES in a suitable way.

Automata form the most basic class of DES models [3, 1, 2]. They are intuitive, easy to use, amenable to composition operations, and amenable to analysis as well (in the finite-state case). However, the (conventional) DES model cannot characterize the probability of probabilistic systems and the possibility of fuzzy systems that exist commonly in engineering field and the real-world problems with fuzziness, impreciseness, and subjectivity. So, probabilistic DES and fuzzy DES were proposed [5, 6, 7, 8, 9, 10]. However, to my best knowledgement, the state complexity problems still have not been studied in probabilistic and fuzzy DES, and QFA have certain advantages over probabilistic finite automata (PFA) in state complexity for some problems, so, with the development of quantum computing [11], how to establish quantum DES (QDES) and show certain advantages of state complexity are a pending problem, and this is the goal of the paper.

Quantum computers were first conceived by Benioff [12] and Feynman [13] in the early of 1980s, and in particular, Feynman [13] indicated it needs exponential time to simulate the evolution of quantum systems in classical computers but quantum computers can perform efficient simulation. In 1985, Deutsch [14] elaborated and formalized Benioff and Feynman’s ideas by defining the notion of quantum Turing machines, and proposed Quantum Strong Church-Turing Thesis: A quantum Turing machine can efficiently simulate any realistic model of computation, which is an extension of the traditional Strong Church-Turing Thesis:A probabilistic Turing machine can efficiently simulate any realistic model of computation. In a way, this also inspires to establish QDES since it is natural to develop control systems from classical models to quantum ones.

In fact, after Shor’s discovery [15] of a polynomial-time algorithm on quantum computers for prime factorization, quantum computation has become a very active research area in quantum physics, computer science, and quantum control [16, 17, 18, 19]. The study of quantum control usually has been focused on time-varying systems, and coherent feedback control (i.e., feedback control using a fully quantum system) has been deeply investigated [17, 18, 19].

Classical automata consist of the most fundamental class of DES models [1, 2], but they may lead to large-scale state spaces when modeling complex systems [20]. Though there are strategies to attack the problem of large state spaces [2, 20], we still hope to discover new methods for solving the state complexity from a different point of view. In fact, quantum finite automata (QFA) can be employed as a powerful tool, since QFA have significant advantages over crisp finite automata concerning state complexity [21, 22]. An excellent and comprehensive survey on QFA was presented by Ambainis and Yakaryilmaz [22]. Moreover, QFA have been studied in physical experiment [23, 24, 25], and in particular, recently Plachta et al. [24] demonstrated an experimental implementation of multiqubit QFA using the orbital angular momentum (OAM) of single photons, and showed that a high-dimensional QFA can outperform classical finite automata in terms of the required memory in a way.

Actually, QFA have been interestingly applied to interactive proof systems [26], in which QFA are verifiers. In addition, QFA have been used to simulate chemical reactions with certain advantages of time complexity [27].

Since QFA have better advantages over classical finite automata in state complexity [21], QDES likely can solve such problems with essential advantages of states complexity over DES. Therefore, the purpose of this paper is to initial the study of QDES. As classical DES are modeled by one-way (probabilistic or fuzzy) finite automata [3, 1, 2, 5, 6, 7, 8, 9, 10], we would employ one of one-way QFA (1QFA) to simulate QDES.

QFA can be thought of as a theoretical model of quantum computers in which the memory is finite and described by a finite-dimensional state space [22, 28, 29, 30]. Measure-once 1QFA (MO-1QFA) were initiated by Moore and Crutchfield [31] and measure-many 1QFA (MM-1QFA) were studied first by Kondacs and Watrous [32], where “1” means “one-way”, that is, the tape-head moves only from left side to right side. MO-1QFA and MM-1QFA were deeply studied by Ambainis and Freivalds [21], Brodsky and Pippenger [33], and other authors (see, e.g., [22]). Then other 1QFA were also proposed and studied by Ambainis et al., Nayak, Hirvensalo, Yakaryilmaz and Say, Paschen, Ciamarra, Bertoni et al., Qiu and Mateus et al. as well other authors (e.g., the references in [22, 28, 30]). These 1QFA include [22]: Latvian-1QFA (La-1QFA), Nayak-1QFA (Na-1QFA), General-1QFA (G-1QFA), 1QFA with ancilla qubits (1QFA-A), fully 1QFA (Ci-1QFA), 1QFA with control languages (1QFA-CL), and one-way quantum finite automata together with classical states (1QFAC), where G-1QFA, 1QFA-A, Ci-1QFA, 1QFA-CL, and 1QFAC can recognize all regular languages with bounded error.

Qiu et al. [28, 34, 35, 36] studied the decision problems regarding equivalence of QFA and minimization of states of QFA, where the equivalence method will be utilized in this paper for checking a controllability condition.

In general, the languages for simulating DES (and QDES) should be prefix-closured and regular [3, 1, 2], but we will prove that MO-1QFA cannot recognize with cut-point (i.e., unbounded error) any prefix-closured regular language. So, MO-1QFA are not suitable for simulating QDES. In addition, it is more complete if the 1QFA modeling QDES can recognize all regular languages and such 1QFA are relatively concise. Actually, 1QFAC proposed in [37] is a hybrid of MO-1QFA and deterministic finite automata (DFA), and both MO-1QFA and DFA are two special models of 1QFAC. 1QFAC can also recognize all regular languages and the computing procedure of 1QFAC are concise in a way. So, in this paper, we would like to use 1QFAC to model QDES.

The remainder of the paper is organized as follows. In Section 2 we first introduce the basics of quantum computing and then present the definitions of QFA (MO-1QFA and 1QFAC; DFA and PFA are also recalled) and related properties, as well as we recall the decidability method of equivalence for 1QFAC. In Section 3, we first recollect the supervisory control of DES (the language and automaton models of DES and related parallel composition operation), then we present QDES and corresponding supervisory control formalization of QDES (we define QDES by means of QFA, define quantum supervisors, and the supervisory control of QDES is formulated); parallel composition of QDES and related properties are also given. In Section 4, we first prove that MO-1QFA can not recognize any prefix-closured language, but, as we know [37], 1QFAC can do it; also a number of new prefix-closured languages are constructed to show 1QFAC’s state complexity advantages over PFA and DFA. Therefore 1QFAC are employed to simulate QDES. Then we establish a number of supervisory control theorems of QDES, and in particular, the new examples are given to illustrate the supervisory control dynamics of QDES, and to verify the advantages of QDES over DES concerning state complexity. In Section 5, we give a method to determine the control condition of QDES. More specifically, the detailed polynomial-time algorithm for testing the existence of supervisors is provided. Finally in Section 6, we summarize the main results we obtain and mention related problems for further developing QDES.

2 One-way Quantum Finite Automata (1QFA)

In this section we serve to review the definitions of MO-1QFA and 1QFAC together with related properties, and the decidability method of equivalence for 1QFAC is recalled. In the interest of readability, we first recall some basics of quantum computing that we will use in the paper. For the details concerning quantum computing, we can refer to [11], and here we just briefly introduce some notation to be used in this paper.

2.1 Some notation on quantum computing

Let \mathbb{C} denote the set of all complex numbers, \mathbb{R} the set of all real numbers, and n×m\mathbb{C}^{n\times m} the set of n×mn\times m matrices having entries in \mathbb{C}. Given two matrices An×mA\in\mathbb{C}^{n\times m} and Bp×qB\in\mathbb{C}^{p\times q}, their tensor product is the np×mqnp\times mq matrix, defined as

AB=[A11BA1mBAn1BAnmB].A\otimes B=\begin{bmatrix}A_{11}B&\dots&A_{1m}B\\ \vdots&\ddots&\vdots\\ A_{n1}B&\dots&A_{nm}B\ \end{bmatrix}.

We get (AB)(CD)=ACBD(A\otimes B)(C\otimes D)=AC\otimes BD if the operations ACAC and BDBD can be done in terms of multiplication of matrices.

Matrix Mn×nM\in\mathbb{C}^{n\times n} is said to be unitary if MM=MM=IMM^{\dagger}=M^{\dagger}M=I, where \dagger denotes conjugate-transpose operation. MM is said to be Hermitian if M=MM=M^{\dagger}. For nn-dimensional row vector x=(x1,,xn)x=(x_{1},\dots,x_{n}), its norm denoted by x||x|| is defined as x=(i=1nxixi)12||x||=\big{(}\sum_{i=1}^{n}x_{i}x_{i}^{*}\big{)}^{\frac{1}{2}}, where symbol * denotes conjugate operation. Unitary matrices preserve the norm, i.e., xM=x||xM||=||x|| for each x1×nx\in\mathbb{C}^{1\times n} and unitary matrix Mn×nM\in\mathbb{C}^{n\times n}.

Any quantum system can be described by a state of Hilbert space. More specifically, for a quantum system with a finite basic state set Q={q1,,qn}Q=\{q_{1},\dots,q_{n}\}, every basic state qiq_{i} can be represented by an nn-dimensional row vector qi|=(010)\langle q_{i}|=(0\dots 1\dots 0) having only 1 at the iith entry (where |\langle\cdot| is Dirac notation, i.e., bra-ket notation). At any time, the state of this system is a superposition of these basic states and can be represented by a row vector ϕ|=i=1nciqi|\langle\phi|=\sum_{i=1}^{n}c_{i}\langle q_{i}| with cic_{i}\in\mathbb{C} and i=1n|ci|2=1\sum_{i=1}^{n}|c_{i}|^{2}=1; |ϕ|\phi\rangle represents the conjugate-transpose of ϕ|\langle\phi|. So, the quantum system is described by the Hilbert space Q{\cal H}_{Q} spanned by the base {|qi:i=1,2,,n}\{|q_{i}\rangle:i=1,2,\dots,n\}, i.e. Q=span{|qi:i=1,2,,n}{\cal H}_{Q}=\text{span}\{|q_{i}\rangle:i=1,2,\dots,n\}.

The evolution of quantum system’s states complies with unitarity. More exactly, suppose the current state of system is |ϕ|\phi\rangle. If it is acted on by some unitary matrix M1M_{1}, then the system’s state is changed to the new current state M1|ϕM_{1}|\phi\rangle; the second unitary matrix, say M2M_{2}, is acted on M1|ϕM_{1}|\phi\rangle, the state is further changed to M2M1|ϕM_{2}M_{1}|\phi\rangle. So, after a series of unitary matrices M1,M2,,MkM_{1},M_{2},\ldots,M_{k} are performed in sequence, the system’s state becomes MkMk1M1|ϕM_{k}M_{k-1}\cdots M_{1}|\phi\rangle.

If we want to get some information from a quantum system, then a measurement is made on its current state. Here we consider projective measurement (i.e., von Neumann measurement). A projective measurement is described by an observable that is a Hermitian matrix 𝒪=c1P1++csPs{\cal O}=c_{1}P_{1}+\dots+c_{s}P_{s}, where cic_{i} is its eigenvalue and, PiP_{i} is the projector onto the eigenspace corresponding to cic_{i}. In this case, the projective measurement of 𝒪{\cal O} has result set {ci}\{c_{i}\} and projector set {Pi}\{P_{i}\}. For example, given state |ψ|\psi\rangle is made by the measurement 𝒪{\cal O}, then the probability of obtaining result cic_{i} is Pi|ψ2\|P_{i}|\psi\rangle\|^{2} and the state |ψ|\psi\rangle collapses to Pi|ψPi|ψ\frac{P_{i}|\psi\rangle}{\|P_{i}|\psi\rangle\|}.

2.2 Definitions of 1QFA

For non-empty set Σ\Sigma, by Σ\Sigma^{*} we mean the set of all finite length strings over Σ\Sigma, and Σn\Sigma^{n} denotes the set of all strings over Σ\Sigma with length nn. For uΣu\in\Sigma^{*}, |u||u| is the length of uu; for example, if u=x1x2xmΣu=x_{1}x_{2}\ldots x_{m}\in\Sigma^{*} where xiΣx_{i}\in\Sigma, then |u|=m|u|=m. For set SS, |S||S| denotes the cardinality of SS. First we recall the definitions of deterministic finite automata (DFA) and probabilistic finite automata (PFA).

2.2.1 DFA and PFA

A DFA [38] can be described by a five-tuple 𝒜=(Q,Σ,δ,q0,Qa){\cal A}=(Q,\Sigma,\delta,q_{0},Q_{a}), where QQ is the finite set of states; Σ\Sigma is a finite alphabet of input; δ:Q×ΣQ\delta:Q\times\Sigma\rightarrow Q is the transition function (In what follows, 𝒫(X){\cal P}(X) represents the power set of set XX.); q0Qq_{0}\in Q is the initial state; and QaQQ_{a}\subseteq Q is called the set of accepting (or called as “marked” in DES) states. Indeed, transition function δ\delta can be naturally extended to Q×ΣQ\times\Sigma^{*} in the following manner: For any qQq\in Q, any sΣs\in\Sigma^{*} and σΣ\sigma\in\Sigma, δ(q,ϵ)=ϵ,andδ(q,sσ)=δ(δ(q,s),σ).\delta(q,\epsilon)=\epsilon,\hskip 5.69054pt{\rm and}\hskip 5.69054pt\delta(q,s\sigma)=\delta(\delta(q,s),\sigma).

The language recognized by 𝒜{\cal A} is as {wΣ:δ(q0,w)Qa}\{w\in\Sigma^{*}:\delta(q_{0},w)\in Q_{a}\}. We can depict it as Fig. 1.

Refer to caption
Figure 1: The dynamics of DFA.

A PFA [39] is also defined as a five-tuple =(S,Σ,π,{M(σ)}σΣ,η)\mathcal{M}=(S,\Sigma,\pi,\{M(\sigma)\}_{\sigma\in\Sigma},\eta), where S={s1,s2,,sn}S=\{s_{1},s_{2},\cdots,s_{n}\} is a finite set of states; Σ\Sigma is a finite alphabet of input; π\pi is an nn-dimensional row vector, denoted as an initial distribution over SS; σΣ\forall\sigma\in\Sigma, M(σ)M(\sigma) is an n×nn\times n random matrix (i.e., each row is a vector of transfer probabilities) and M(σ)(i,j)M(\sigma)(i,j) denotes the probability of transferring from state sis_{i} to state sjs_{j} after the machine \mathcal{M} reads σ\sigma; η=[η1,,ηn]T\eta=\begin{bmatrix}\eta_{1},\cdots,\eta_{n}\end{bmatrix}^{T} is an nn-dimensional column vector with elements 0 or 11, and ηi=1\eta_{i}=1 means sis_{i} is an accepting state. For any input string x=x1x2xnΣx=x_{1}x_{2}\cdots x_{n}\in\Sigma^{*}, the probability that \mathcal{M} accepts xx is

P(x)=πM(x1)M(x2)M(xn)η.P_{\mathcal{M}}(x)=\pi M(x_{1})M(x_{2})\cdots M(x_{n})\eta. (1)

2.2.2 MO-1QFA and 1QFAC

MO-1QFA are the simplest quantum computing models proposed first by Moore and Crutchfield [31]. In this model, the transformation on any symbol in the input alphabet is realized by a unitary operator. A unique measurement is performed at the end of a computation. More formally, an MO-1QFA with nn states and the input alphabet Σ\Sigma is a five-tuple

=(Q,|ψ0,{U(σ)}σΣ,Qa,Qr),{\cal M}=(Q,|\psi_{0}\rangle,\{U(\sigma)\}_{\sigma\in\Sigma},Q_{a},Q_{r}), (2)

where Q={|q1,,|qn}Q=\{|q_{1}\rangle,\dots,|q_{n}\rangle\} consist of an orthonormal base that spans a Hilbert space Q{\cal H}_{Q};|ψ0|\psi_{0}\rangle\in{\cal H} is the initial state; for any σΣ\sigma\in\Sigma, U(σ)n×nU(\sigma)\in\mathbb{C}^{n\times n} is a unitary matrix; Qa,QrQQ_{a},Q_{r}\subseteq Q with QaQr=QQ_{a}\cup Q_{r}=Q and QaQr=Q_{a}\cap Q_{r}=\emptyset are the accepting and rejecting states, respectively, and they describe an observable by using the projectors P(a)=|qiQa|qiqi|P(a)=\sum_{|q_{i}\rangle\in Q_{a}}|q_{i}\rangle\langle q_{i}| and P(r)=|qiQr|qiqi|P(r)=\sum_{|q_{i}\rangle\in Q_{r}}|q_{i}\rangle\langle q_{i}|, with the result set {a,r}\{a,r\} of which ‘aa’ and ‘rr’ denote “accept” and “reject”, respectively. Here QQ consists of accepting and rejecting sets.

Given an MO-1QFA {\cal M} and an input word s=x1xnΣs=x_{1}\dots x_{n}\in\Sigma^{*}, then starting from |ψ0|\psi_{0}\rangle, U(x1),,U(xn)U(x_{1}),\dots,U(x_{n}) are applied in succession, and at the end of the word, a measurement {P(a),P(r)}\{P(a),P(r)\} is performed with the result that {\cal M} collapses into accepting states or rejecting states with corresponding probabilities. Hence, the probability L(x1xn)L_{{\cal M}}(x_{1}\dots x_{n}) of {\cal M} accepting ww is defined as:

L(x1xn)=P(a)Us|ψ02,\displaystyle L_{\cal M}(x_{1}\dots x_{n})=\|P(a)U_{s}|\psi_{0}\rangle\|^{2}, (3)

where we denote Us=UxnUxn1Ux1U_{s}=U_{x_{n}}U_{x_{n-1}}\cdots U_{x_{1}}. MO-1QFA can be depicted as Figure 2, in which if these unitary transformations are replaced by stochastic matrices and some stochastic vectors take place of quantum states, then it is a PFA [39].

Refer to caption
Figure 2: MO-1QFA dynamics as an acceptor of languages.

Next we introduce 1QFAC proposed in [37]. A 1QFAC 𝒜{\cal A} [37] is formally defined by a 8-tuple

=(S,Q,Σ,s0,|ψ0,δ,𝕌,𝒫),{\cal M}=(S,Q,\Sigma,s_{0},|\psi_{0}\rangle,\delta,\mathbb{U},{\cal P}),

where:

  • Σ\Sigma is a finite set (the input alphabet);

  • SS is a finite set (the set of classical states);

  • QQ is a finite set (the set of quantum basis states);

  • s0s_{0} is an element of SS (the initial classical state);

  • |ψ0|\psi_{0}\rangle is a unit vector in the Hilbert space (Q){\cal H}(Q) (the initial quantum state);

  • δ:S×ΣS\delta:S\times\Sigma\rightarrow S is a map (the classical transition map);

  • 𝕌={Usσ}sS,σΣ\mathbb{U}=\{U_{s\sigma}\}_{s\in S,\sigma\in\Sigma} where Usσ:(Q)(Q)U_{s\sigma}:{\cal H}(Q)\rightarrow{\cal H}(Q) is a unitary operator for each ss and σ\sigma (the quantum transition operator at ss and σ\sigma);

  • 𝒫={𝒫s}sS{\cal P}=\{{\cal P}_{s}\}_{s\in S} where each 𝒫s{\cal P}_{s} is a projective measurement over (Q){\cal H}(Q) with outcomes accepting (denoted by aa) or rejecting (denoted by rr) (the measurement operator at ss).

Hence, each 𝒫s={Ps,a,Ps,r}{\cal P}_{s}=\{P_{s,a},P_{s,r}\} such that Ps,a+Ps,r=IP_{s,a}+P_{s,r}=I and Ps,aPs,r=OP_{s,a}P_{s,r}=O. Furthermore, if the machine is in classical state ss and quantum state |ψ|\psi\rangle after reading the input string, then Ps,γ|ψ2\|P_{s,\gamma}|\psi\rangle\|^{2} is the probability of the machine producing outcome γ\gamma on that input.

δ\delta can be extended to a map δ:ΣS\delta^{*}:\Sigma^{*}\rightarrow S as usual. That is, δ(s,ϵ)=s\delta^{*}(s,\epsilon)=s; for any string xΣx\in\Sigma^{*} and any σΣ\sigma\in\Sigma, δ(s,σx)=δ(δ(s,σ),x)\delta^{*}(s,\sigma x)=\delta^{*}(\delta(s,\sigma),x). For the sake of convenience, we denote the map μ:ΣS\mu:\Sigma^{*}\rightarrow S, induced by δ\delta, as μ(x)=δ(s0,x)\mu(x)=\delta^{*}(s_{0},x) for any string xΣx\in\Sigma^{*}. We further describe the computing process of 𝒜{\cal A} for input string x=σ1σ2σnx=\sigma_{1}\sigma_{2}\cdots\sigma_{n} where σiΣ\sigma_{i}\in\Sigma for i=1,2,,ni=1,2,\cdots,n.

The machine 𝒜{\cal A} starts at the initial classical state s0s_{0} and initial quantum state |ψ0|\psi_{0}\rangle. On reading the first symbol σ1\sigma_{1} of the input string, the states of the machine change as follows: the classical state becomes μ(σ1)\mu(\sigma_{1}); the quantum state becomes Us0σ1|ψ0U_{s_{0}\sigma_{1}}|\psi_{0}\rangle. Afterward, on reading σ2\sigma_{2}, the machine changes its classical state to μ(σ1σ2)\mu(\sigma_{1}\sigma_{2}) and its quantum state to the result of applying Uμ(σ1)σ2U_{\mu(\sigma_{1})\sigma_{2}} to Us0σ1|ψ0U_{s_{0}\sigma_{1}}|\psi_{0}\rangle.

The process continues similarly by reading σ3\sigma_{3}, σ4\sigma_{4}, \cdots, σn\sigma_{n} in succession. Therefore, after reading σn\sigma_{n}, the classical state becomes μ(x)\mu(x) and the quantum state is as follows:

Uμ(σ1σn2σn1)σnUμ(σ1σn3σn2)σn1Uμ(σ1)σ2Us0σ1|ψ0.\displaystyle U_{\mu(\sigma_{1}\cdots\sigma_{n-2}\sigma_{n-1})\sigma_{n}}U_{\mu(\sigma_{1}\cdots\sigma_{n-3}\sigma_{n-2})\sigma_{n-1}}\cdots U_{\mu(\sigma_{1})\sigma_{2}}U_{s_{0}\sigma_{1}}|\psi_{0}\rangle. (4)

Let 𝒰(Q){\cal U}(Q) be the set of unitary operators on Hilbert space (Q){\cal H}(Q). For the sake of convenience, we denote the map v:Σ𝒰(Q)v:\Sigma^{*}\rightarrow{\cal U}(Q) as: v(ϵ)=Iv(\epsilon)=I and

v(x)=Uμ(σ1σn2σn1)σnUμ(σ1σn3σn2)σn1Uμ(σ1)σ2Us0σ1v(x)=U_{\mu(\sigma_{1}\cdots\sigma_{n-2}\sigma_{n-1})\sigma_{n}}U_{\mu(\sigma_{1}\cdots\sigma_{n-3}\sigma_{n-2})\sigma_{n-1}}\cdots U_{\mu(\sigma_{1})\sigma_{2}}U_{s_{0}\sigma_{1}} (5)

for i=1,2,,ni=1,2,\cdots,n, and II denotes the identity operator on (Q){\cal H}(Q), indicated as before.

By means of the denotations μ\mu and vv, for any input string xΣx\in\Sigma^{*}, after 𝒜{\cal A} reading xx, the classical state is μ(x)\mu(x) and the quantum state is v(x)|ψ0v(x)|\psi_{0}\rangle.

Finally, we obtain the probability L(x)L_{{\cal M}}(x) for accepting xx:

L(x)=Pμ(x),av(x)|ψ02.L_{{\cal M}}(x)=\|P_{\mu(x),a}v(x)|\psi_{0}\rangle\|^{2}. (6)

1QFAC can be depicted as Figure 3.

Refer to caption
Figure 3: 1QFAC dynamics as an acceptor of languages.

2.3 Determining the equivalence for quantum finite automata

In order to study the decision of controllability condition, in this subsection we introduce the method of how to determine the equivalence between 1QFAC, and the details are referred to [36, 37, 28].

Definition 1.

A bilinear machine (BLM) over the alphabet Σ\Sigma is tuple =(S,π,{M(σ)}σΣ,η)\mathcal{M}=(S,\pi,\{M(\sigma)\}_{\sigma\in\Sigma},\eta), where SS is a finite state set with |S|=n|S|=n, η1×n\eta\in\mathbb{C}^{1\times n}, πn×1\pi\in\mathbb{C}^{n\times 1}, and M(σ)n×nM(\sigma)\in\mathbb{C}^{n\times n} for σΣ\sigma\in\Sigma.

Associated to a BLM, the word function

L:ΣL_{\mathcal{M}}:\Sigma^{\ast}\longrightarrow\mathbb{C} (7)

is defined in the way:

L(w)=ηM(wm)M(wm1)M(w1)π,L_{\mathcal{M}}(w)=\eta M(w_{m})M(w_{m-1})\ldots M(w_{1})\pi, (8)

where w=w1w2wmΣw=w_{1}w_{2}\ldots w_{m}\in\Sigma^{\ast}. In particular, when L(w)L_{\cal M}(w)\in\mathbb{R} for every wΣw\in\Sigma^{*}, {\cal M} is called a real-valued bilinear machine (RBLM).

Remark 1.

For any two RBLM

i=(Si,πi,{Mi(σ)}σΣ,ηi),i=1,2,\mathcal{M}_{i}=(S_{i},\pi_{i},\{M_{i}(\sigma)\}_{\sigma\in\Sigma},\eta_{i}),i=1,2, (9)

it is easy to obtain that, for any wΣw\in\Sigma^{*},

L12(w)=L1(w)×L2(w);L_{\mathcal{M}_{1}\otimes\mathcal{M}_{2}}(w)=L_{\mathcal{M}_{1}}(w)\times L_{\mathcal{M}_{2}}(w); (10)
L12(w)=L1(w)+L2(w),L_{\mathcal{M}_{1}\oplus\mathcal{M}_{2}}(w)=L_{\mathcal{M}_{1}}(w)+L_{\mathcal{M}_{2}}(w), (11)

where 12\mathcal{M}_{1}\otimes\mathcal{M}_{2} and 12\mathcal{M}_{1}\oplus\mathcal{M}_{2} are defined as usual [31].

Definition 2.

Two BLM (RBLM, QFA) 1\mathcal{M}_{1} and 2\mathcal{M}_{2} over the same alphabet Σ\Sigma are said to be equivalent (resp. kk-equivalent) if L1(w)=L2(w)L_{{\cal M}_{1}}(w)=L_{{\cal M}_{2}}(w) for any wΣw\in\Sigma^{*} (resp. for any input string ww with |w|k|w|\leq k).

The following proposition determines the equivalence between BLM (RBLM) [40, 41, 42, 43].

Proposition 1.

Two BLM (RBLM) 1\mathcal{M}_{1} and 2\mathcal{M}_{2} with n1n_{1} and n2n_{2} states, respectively, are equivalent if and only if they are (n1+n21)(n_{1}+n_{2}-1)-equivalent. Furthermore, there exists a polynomial-time algorithm running in time O((n1+n2)3)O((n_{1}+n_{2})^{3}) that takes as input two BLM (RBLM) 1\mathcal{M}_{1} and 2\mathcal{M}_{2} and determines whether 1\mathcal{M}_{1} and 2\mathcal{M}_{2} are equivalent.

The following proposition [36, 37] is also useful in this paper.

Proposition 2.

Let BLM (RBLM) {\cal M} have nn states and the alphabet Σ{τ}\Sigma\cup\{\tau\} where τΣ\tau\notin\Sigma. Then we can give another BLM (RBLM) ^\hat{\cal M} over the alphabet Σ\Sigma with the same states, such that L(wτ)=L^(w)L_{{\cal M}}(w\tau)=L_{\hat{\cal M}}(w), for any wΣw\in\Sigma^{*}.

Lemma 1.

[37] For any given 1QFAC

=(S,Q,Σ,s0,|ψ0,δ,𝕌,𝒫),{\cal M}=(S,Q,\Sigma,s_{0},|\psi_{0}\rangle,\delta,\mathbb{U},{\cal P}), (12)

there is a RBLM {\cal M}^{\prime} with (kn)2(kn)^{2} states, where |S|=k|S|=k and |Q|=n|Q|=n, such that

L(x)=L(x)L_{{\cal M}}(x)=L_{{\cal M}^{\prime}}(x) (13)

for any xΣx\in\Sigma^{*}.

By means of Lemma 1 and Proposition 1, we have the following theorem.

Theorem 1.

Two 1QFAC 1{\cal M}_{1} and 2{\cal M}_{2} are equivalent if and only if they are (k1n1)2+(k2n2)21(k_{1}n_{1})^{2}+(k_{2}n_{2})^{2}-1-equivalent. Furthermore, there exists a polynomial-time algorithm running in time O([(k1n1)2+(k2n2)2]3)O([(k_{1}n_{1})^{2}+(k_{2}n_{2})^{2}]^{3}) that takes as input two 1QFAC 1{\cal M}_{1} and 2{\cal M}_{2} and determines whether 1{\cal M}_{1} and 2{\cal M}_{2} are equivalent, where kik_{i} and nin_{i} are the numbers of classical and quantum basis states of i{\cal M}_{i}, respectively, i=1,2i=1,2.

3 Quantum Discrete Event Systems

In this section, we first recollect the supervisory control of classical DES, then we formalize QDES and corresponding supervisory control of QDES.

3.1 Language and Automaton Models of DES

In this subsection, we briefly review some basic concepts concerning DES [1, 2]. A DES is modeled and represented as a nondeterministic finite automaton GG, described by G=(Q,Σ,δ,q0,Qm)G=(Q,\Sigma,\delta,q_{0},Q_{m}), where QQ is the finite set of states; Σ\Sigma is the finite set of events; δ:Q×Σ𝒫(Q)\delta:Q\times\Sigma\rightarrow{\cal P}(Q) is the transition function (In what follows, 𝒫(X){\cal P}(X) represents the power set of set XX.); q0Qq_{0}\in Q is the initial state; and QmQQ_{m}\subseteq Q is called the set of marked states. Indeed, transition function δ\delta can be naturally extended to Q×ΣQ\times\Sigma^{*} in the following manner: For any qQq\in Q, any sΣs\in\Sigma^{*} and σΣ\sigma\in\Sigma, δ(q,ϵ)=ϵ,andδ(q,sσ)=δ(δ(q,s),σ),\delta(q,\epsilon)=\epsilon,\hskip 5.69054pt{\rm and}\hskip 5.69054pt\delta(q,s\sigma)=\delta(\delta(q,s),\sigma), where we define δ(A,σ)=qAδ(q,σ)\delta(A,\sigma)=\bigcup_{q\in A}\delta(q,\sigma) for any A𝒫(Q)A\in{\cal P}(Q).

In particular, when δ\delta is a map from Q×ΣQ\times\Sigma to QQ, then it is a DFA, as we depict it in Fig. 1.

In fact, in GG we can represent qiq_{i} by vector si=(010)Ts_{i}=(0\hskip 5.69054pt\cdots\hskip 5.69054pt1\hskip 5.69054pt\cdots\hskip 5.69054pt0)^{T} where 1 is in the iith place and the dimension equals nn (TT denotes transpose); for σΣ\sigma\in\Sigma, σ\sigma is represented as a 0-1 matrix (aij)n×n(a_{ij})_{n\times n} where aij{0,1}a_{ij}\in\{0,1\}, and aij=1a_{ij}=1 if and only if qjδ(qi,σ)q_{j}\in\delta(q_{i},\sigma). Analogously, vector (01010)T(0\hskip 5.69054pt\cdots\hskip 5.69054pt1\hskip 5.69054pt0\hskip 5.69054pt\cdots\hskip 5.69054pt1\hskip 5.69054pt\cdots\hskip 5.69054pt0)^{T} in which 1 is in the iith and jjth places, respectively, means that the current state may be qiq_{i} or qjq_{j}.

For a DES modeled by finite automaton G=(Q,Σ,δ,q0,Qm)G=(Q,\Sigma,\delta,q_{0},Q_{m}), L(G)={xΣ:δ(q0,x)Q}L(G)=\{x\in\Sigma^{*}:\delta(q_{0},x)\in Q\} represents all feasible input strings in DES GG, and Lm(G)={xΣ:δ(q0,x)Qm}L_{m}(G)=\{x\in\Sigma^{*}:\delta(q_{0},x)\in Q_{m}\} is called the language marked by GG.

In order to define and better understand parallel composition of quantum finite automata, we reformulate the parallel composition of crisp finite automata [1, 2]. For finite automata Gi=(Qi,Σi,δi,q0i,Qmi)G_{i}=(Q_{i},\Sigma_{i},\delta_{i},q_{0i},Q_{mi}), i=1,2i=1,2, we reformulate the parallel composition in terms of the following fashion:

G1G2=(Q1Q2,Σ1Σ2,δ1δ2,q10q20,Qm1Qm2).\displaystyle G_{1}\|G_{2}=(Q_{1}\otimes Q_{2},\Sigma_{1}\cup\Sigma_{2},\delta_{1}\|\delta_{2},q_{10}\otimes q_{20},Q_{m1}\otimes Q_{m2}). (14)

Here, Q1Q2={q1q2:q1Q1,q2Q2}Q_{1}\otimes Q_{2}=\{q_{1}\otimes q_{2}:q_{1}\in Q_{1},q_{2}\in Q_{2}\}, and symbol ``"``\otimes" denotes tensor product. For event σΣ1Σ2\sigma\in\Sigma_{1}\cup\Sigma_{2}, we define the corresponding matrix of σ\sigma in G1G2G_{1}\|G_{2} as follows:

(i) If event σΣ1Σ2\sigma\in\Sigma_{1}\cap\Sigma_{2}, then σ=σ1σ2\sigma=\sigma_{1}\otimes\sigma_{2} where σ1\sigma_{1} and σ2\sigma_{2} are the matrices of σ\sigma in G1G_{1} and G2G_{2}, respectively.

(ii) If event σΣ1\Σ2\sigma\in\Sigma_{1}\backslash\Sigma_{2}, then σ=σ1I2\sigma=\sigma_{1}\otimes I_{2} where σ1\sigma_{1} is the matrix of σ\sigma in G1G_{1}, and I2I_{2} is unit matrix of order |Q2||Q_{2}|.

(iii) If event σΣ2\Σ1\sigma\in\Sigma_{2}\backslash\Sigma_{1}, then σ=I1σ2\sigma=I_{1}\otimes\sigma_{2} where σ2\sigma_{2} is the matrix of σ\sigma in G2G_{2}, and I1I_{1} is unit matrix of order |Q1||Q_{1}|.

In terms of the above (i-iii) regarding the event σΣ1Σ2\sigma\in\Sigma_{1}\cup\Sigma_{2}, we can define δ1δ2\delta_{1}\|\delta_{2} as: For q1q2Q1Q2q_{1}\otimes q_{2}\in Q_{1}\otimes Q_{2}, σΣ1Σ2\sigma\in\Sigma_{1}\cup\Sigma_{2},

(δ1δ2)(q1q2,σ)\displaystyle(\delta_{1}\|\delta_{2})(q_{1}\otimes q_{2},\sigma) (15)
=\displaystyle= {(σ1σ2)×(q1q2),ifσΣ1Σ2,(σ1I2)×(q1q2),ifσΣ1\Σ2,(I1σ2)×(q1q2),ifσΣ2\Σ1,\displaystyle\left\{\begin{array}[]{lll}(\sigma_{1}\otimes\sigma_{2})\times(q_{1}\otimes q_{2}),&{\rm if}&\sigma\in\Sigma_{1}\cap\Sigma_{2},\\ (\sigma_{1}\otimes I_{2})\times(q_{1}\otimes q_{2}),&{\rm if}&\sigma\in\Sigma_{1}\backslash\Sigma_{2},\\ (I_{1}\otimes\sigma_{2})\times(q_{1}\otimes q_{2}),&{\rm if}&\sigma\in\Sigma_{2}\backslash\Sigma_{1},\end{array}\right. (19)

where ×\times is the usual product between matrices, and, as indicated above, symbol \otimes denotes tensor product of matrices.

3.2 Probabilistic DES (PDES)

In the interest of completeness, in this subsection, we would recall the automata model for probabilistic discrete event systems (PDES). Formally, there are two definitions concerning PDES, and we name them PDES-I and PDES-II respectively.

Definition 3.

[6, 7, 10] A PDES-I could be modeled as the following probabilistic automaton:

G={X,x0,Σ,δ,ρ},G=\{X,x_{0},\Sigma,\delta,\rho\}, (20)

where XX is the nonempty finite set of states; x0Xx_{0}\in X is the initial state; Σ\Sigma is the nonempty finite set of events; Σ=ΣcΣuc\Sigma=\Sigma_{c}\cup\Sigma_{uc} with Σc\Sigma_{c} and Σuc\Sigma_{uc} being the disjoint controllable and uncontrollable event sets, respectively; δ:X×ΣX\delta:X\times\Sigma\rightarrow X is the (partial) transition function, and the function δ\delta can be extended to X×ΣX\times\Sigma^{*} by the natural manner; ρ:X×Σ[0,1]\rho:X\times\Sigma\rightarrow[0,1] is the transition-probability function, where ρ(x,σ)\rho(x,\sigma) is the probability of transition δ(x,σ)\delta(x,\sigma) and satisfies σΣρ(x,σ)1\sum_{\sigma\in\Sigma}\rho(x,\sigma)\leq 1, xX\forall x\in X. In particular, if σΣρ(x,σ)=1\sum_{\sigma\in\Sigma}\rho(x,\sigma)=1, xX\forall x\in X, then the system GG is called as a non-terminating PDES-I.

PDES-II are defined in light of PFA in [39] that are from [44, 45, 46] as follows.

Definition 4.

A PDES-II is a type of systems with a quadruple

G=(Q,Σ,η,q0),G=(Q,\Sigma,\eta,q_{0}), (21)

where QQ is a finite state space; q0Qq_{0}\in Q is the initial state; Σ\Sigma is a finite set of events; η:Q×Σ×Q[0,1]\eta:Q\times\Sigma\times Q\rightarrow[0,1] is a state transition function: for q,qQq,q^{\prime}\in Q and σΣ\sigma\in\Sigma, η(q,σ,q)\eta(q,\sigma,q^{\prime}) represents the probability that event σ\sigma will occur, together with transferring the state of the machine from qq to qq^{\prime}. If we require that qQη(q,σ,q)=1\sum_{q^{\prime}\in Q}\eta(q,\sigma,q^{\prime})=1 for any qQq\in Q and any σΣ\sigma\in\Sigma, then it is exactly the PFA in [39] as defined in subsection 2.2.1.

However, the problems of state complexity in PDES-I and PDES-II still need to be studied.

3.3 Quantum DES (QDES)

As in [31], a quantum language over finite input alphabet Σ\Sigma is defined as a function mapping words to probabilities, i.e., a function from Σ\Sigma^{\ast} to [0,1][0,1].

For any 1QFA {\cal M} (MO-1QFA,1QFAC) with finite input alphabet Σ\Sigma, the accepting probability L(x1xn)L_{\cal M}(x_{1}\dots x_{n}) for any x1xnΣx_{1}\dots x_{n}\in\Sigma^{*} is defined as before. Therefore {\cal M} generates a quantum language LL_{{\cal M}} over finite input alphabet Σ\Sigma.

For any two quantum languages f1f_{1} and f2f_{2} over finite input alphabet Σ\Sigma, denote f2f2f_{2}\subseteq f_{2} if and only if f2(w)f2(w)f_{2}(w)\leq f_{2}(w) for any wΣw\in\Sigma^{*}.

Denote

Lλ={xΣ:f(x)>λ},L_{{\cal M}}^{\lambda}=\{x\in\Sigma^{\ast}:f_{\cal M}(x)>\lambda\}, (22)

where 0λ<10\leq\lambda<1. Then LλL_{{\cal M}}^{\lambda} is called the language recognized by {\cal M} with cut-point λ\lambda.

A language, denoted by Lλ,ρΣL_{{\cal M}}^{\lambda,\rho}\subseteq\Sigma^{\ast}, is recognized by {\cal M} with some cut-point λ\lambda isolated by some ρ>0\rho>0, if for any xLλ,ρx\in L_{{\cal M}}^{\lambda,\rho}, f(x)λ+ρf_{{\cal M}}(x)\geq\lambda+\rho and for any xLλ,ρx\notin L_{{\cal M}}^{\lambda,\rho}, f(x)λρf_{{\cal M}}(x)\leq\lambda-\rho. In fact, if a language LL is recognized by a 1QFA {\cal M} with some cut-point λ\lambda isolated by some ρ>0\rho>0, then LL is also called to be recognized by a 1QFA {\cal M} with bounded error.

In DES, the event set (input alphabet) Σ\Sigma is partitioned into two disjoint subsets Σc\Sigma_{c} (controllable events) and Σuc\Sigma_{uc} (uncontrollable events), and a specification language KΣK\subset\Sigma^{\ast} is given. It is assumed that controllable events can be disabled by a supervisor. To solve the supervisory control problem we need to find a supervisor for performing a feedback control with the plant that is described by an automaton. Formally, supervisor SS is defined as a function:

S:L(G)𝒫(Σ).S:L(G)\rightarrow{\cal P}(\Sigma).

It is interpreted that for each sL(G)s\in L(G), S(s){σ:sσL(G)}S(s)\cap\{\sigma:s\sigma\in L(G)\} represents the set of enabled events after the occurrence of ss. Furthermore, it is required that for any sL(G)s\in L(G),

Σuc{σΣ:sσL(G)}S(s),\Sigma_{uc}\cap\{\sigma\in\Sigma:s\sigma\in L(G)\}\subseteq S(s), (23)

which means that after the occurrence of any physically possible string of events, the physically possible uncontrollable events are not allowed to be disabled by SS. The condition described by Eq. (23) is called admissible for SS [1, 2].

A QDES is a quantum system (called a quantum plant) described by a 1QFA {\cal M} together with the event set Σ=ΣcΣuc\Sigma=\Sigma_{c}\cup\Sigma_{uc} and simulated as the quantum language generated by this 1QFA {\cal M} (sometimes simulated as the quantum language recognized by this 1QFA {\cal M} with some cut-point λ\lambda or with some cut-point λ\lambda isolated by some ρ>0\rho>0).

A quantum supervisor 𝒮{\cal S} for controlling {\cal M} is defined formally as a function 𝒮:Σ[0,1]Σ{\cal S}:\Sigma^{\ast}\rightarrow[0,1]^{\Sigma}, where for any sΣs\in\Sigma^{\ast}, 𝒮(s){\cal S}(s) is a quantum language over Σ\Sigma. Intuitively, after inputting ss in QDES {\cal M}, for any σΣ\sigma\in\Sigma, 𝒮(s)(σ){\cal S}(s)(\sigma) denotes the degree to which σ\sigma is enabled.

Therefore, we require that quantum supervisor 𝒮{\cal S} logically satisfies: σΣuc\forall\sigma\in\Sigma_{uc}, sΣ\forall s\in\Sigma^{\ast},

L(sσ)𝒮(s)(σ),L_{{\cal M}}(s\sigma)\leq{\cal S}(s)(\sigma), (24)

which denotes that both ss and sσs\sigma being feasible in the quantum plant for uncontrollable event σ\sigma results in σ\sigma being enabled after quantum supervisor 𝒮{\cal S} controlling ss. Equation (24) is called quantum admissible condition.

The feedback loop of supervisory control of QDES {\cal M} controlled by quantum supervisor 𝒮{\cal S} can be depicted as Fig. 4.

In classical DES, given a DES modeled by finite automaton GG and admissible supervisor SS, the resulting controlled system denoted by S/GS/G that means SS controlling GG, is also a DES modeled by a language L(S/G)L(S/G) defined recursively as follows [1, 2]: ϵL(S/G)\epsilon\in L(S/G) and sσL(S/G)sL(S/G)s\sigma\in L(S/G)\Longleftrightarrow s\in L(S/G) and sσL(G)s\sigma\in L(G) and σS(s)\sigma\in S(s).

Naturally, we denote by 𝒮/{\cal S}/{\cal M} the controlled system by 𝒮{\cal S}, and L𝒮/L_{{\cal S}/{\cal M}} is defined as a function from Σ\Sigma^{\ast} to [0,1][0,1] as follows:

First, it is required that L𝒮/(ϵ)=1L_{{\cal S}/{\cal M}}(\epsilon)=1 (i.e., the starting state is an accepting state) and then recursively, sΣ\forall s\in\Sigma^{\ast}, σΣ\forall\sigma\in\Sigma, the following equation holds

L𝒮/(sσ)=min{L𝒮/(s),L(sσ),𝒮(s)(σ)}.L_{{\cal S}/{\cal M}}(s\sigma)=\min\{L_{{\cal S}/{\cal M}}(s),L_{{\cal M}}(s\sigma),{\cal S}(s)(\sigma)\}. (25)

By intuitive the above equation logically implies that sσs\sigma can be performed by the controlled system 𝒮/{\cal S}/{\cal M} if and only if ss can be performed by the controlled system 𝒮/{\cal S}/{\cal M} and sσs\sigma is feasible in the quantum plant as well as σ\sigma is enabled by the quantum supervisor 𝒮{\cal S} after the event string ss occurs.

In classical DES and automata theory, the prefix-closure pr(L)pr(L) of a language LL over alphabet Σ\Sigma is defined as: for any sΣs\in\Sigma^{\ast}, spr(L)s\in pr(L) if and only if stLst\in L for some tΣt\in\Sigma^{\ast}. Naturaly, if 𝒦{\cal K} is a quantum language over alphabet Σ\Sigma, then logically we define the quantum language of its prefix-closure pr(𝒦)pr({\cal K}) as follows: sΣ\forall s\in\Sigma^{\ast},

pr(𝒦)(s)=suptΣ𝒦(st).\displaystyle pr({\cal K})(s)=\sup_{t\in\Sigma^{\ast}}{\cal K}(st). (26)
Refer to caption
Figure 4: The supervisory control of QDES, where GG represents the uncontrolled system and SS represents the quantum supervisor.

3.4 Parallel composition of QDES

Let two QDES with the same finite event set (input alphabet) Σ\Sigma be described by two 1QFAC i=(Si,Qi,Σ,s0(i),|ψ0(i),δi,𝕌i,𝒫i){{\cal M}_{i}}=(S_{i},Q_{i},\Sigma,s_{0}^{(i)},|\psi_{0}^{(i)}\rangle,\delta_{i},\mathbb{U}_{i},{\cal P}_{i}), i=1,2.i=1,2. Then the parallel composition of QDES 1{{\cal M}_{1}} and 2{{\cal M}_{2}} is their tensor operation, that is the 1QFAC 12{{\cal M}_{1}}\otimes{{\cal M}_{2}} as follows.

12=(S1S2,Q1Q2,Σ,(s0(1),s0(2)),\displaystyle{{\cal M}_{1}}\otimes{{\cal M}_{2}}=(S_{1}\otimes S_{2},Q_{1}\otimes Q_{2},\Sigma,(s_{0}^{(1)},s_{0}^{(2)}),
|ψ0(1)|ψ0(2),δ1δ2,𝕌1𝕌2,𝒫1𝒫2),\displaystyle|\psi_{0}^{(1)}\rangle\otimes|\psi_{0}^{(2)}\rangle,\delta_{1}\otimes\delta_{2},\mathbb{U}_{1}\otimes\mathbb{U}_{2},{\cal P}_{1}\otimes{\cal P}_{2}), (27)

where

  • S1S2={(s1,s2):siSi,i=1,2}S_{1}\otimes S_{2}=\{(s_{1},s_{2}):s_{i}\in S_{i},i=1,2\};

  • Q1Q2Q_{1}\otimes Q_{2} means the set {|q1,i|q2,j:q1,iQ1,q2,jQ2}\{|q_{1,i}\rangle\otimes|q_{2,j}\rangle:q_{1,i}\in Q_{1},q_{2,j}\in Q_{2}\};

  • 𝕌1𝕌2={UsiσUsjσ:(si,sj)S1S2,Usiσ𝕌1,Usjσ𝕌2,σΣ}\mathbb{U}_{1}\otimes\mathbb{U}_{2}=\{U_{s_{i}\sigma}\otimes U_{s_{j}\sigma}:(s_{i},s_{j})\in S_{1}\otimes S_{2},U_{s_{i}\sigma}\in\mathbb{U}_{1},U_{s_{j}\sigma}\in\mathbb{U}_{2},\sigma\in\Sigma\};

  • 𝒫1𝒫2={𝒫(si,sj):(si,sj)S1S2}{\cal P}_{1}\otimes{\cal P}_{2}=\{{\cal P}_{(s_{i},s_{j})}:(s_{i},s_{j})\in S_{1}\otimes S_{2}\} and 𝒫(si,sj)={P(si,sj),a,P(si,sj),r}{\cal P}_{(s_{i},s_{j})}=\{P_{(s_{i},s_{j}),a},P_{(s_{i},s_{j}),r}\};

  • δ1δ2((s1,s2),σ)=(δ1(s1,σ),δ2(s2,σ))\delta_{1}\otimes\delta_{2}((s_{1},s_{2}),\sigma)=(\delta_{1}(s_{1},\sigma),\delta_{2}(s_{2},\sigma)).

It is easy to check that for any s=x1x2xnΣs=x_{1}x_{2}\ldots x_{n}\in\Sigma^{\ast},

L12(s)\displaystyle L_{{{\cal M}_{1}}\otimes{{\cal M}_{2}}}(s)
=\displaystyle= P(μ1(s),μ2(s)),av1(s)v2(s)|ψ0(1)|ψ0(2)2\displaystyle\|P_{(\mu_{1}(s),\mu_{2}(s)),a}v_{1}(s)\otimes v_{2}(s)|\psi_{0}^{(1)}\rangle\otimes|\psi_{0}^{(2)}\rangle\|^{2} (28)
=\displaystyle= Pμ1(s),av1(s)|ψ0(1)2Pμ2(s),av2(s)|ψ0(2)2\displaystyle\|P_{\mu_{1}(s),a}v_{1}(s)|\psi_{0}^{(1)}\rangle\|^{2}\|P_{\mu_{2}(s),a}v_{2}(s)|\psi_{0}^{(2)}\rangle\|^{2} (29)
=\displaystyle= L1(s)L2(s),\displaystyle L_{{\cal M}_{1}}(s)L_{{\cal M}_{2}}(s), (30)

where

vi(s)=\displaystyle v_{i}(s)= Uμi(x1xn2xn1)xnUμi(x1xn3xn2)xn1\displaystyle U_{\mu_{i}(x_{1}\cdots x_{n-2}x_{n-1})x_{n}}U_{\mu_{i}(x_{1}\cdots x_{n-3}x_{n-2})x_{n-1}}\cdots
Uμi(x1)x2Us0(i)x1,\displaystyle U_{\mu_{i}(x_{1})x_{2}}U_{s_{0}^{(i)}x_{1}}, (31)

and μi(s)=δi(s0(i),s)\mu_{i}(s)=\delta_{i}^{*}(s_{0}^{(i)},s), i=1,2.i=1,2.

4 Supervisory Control of QDES

In this section, we first present some properties and new examples concerning 1QFA, and these results are new and useful for the study of supervisory control of QDES. In DES, the occurrence of an event string s=x1x2xns=x_{1}x_{2}\ldots x_{n} being feasible entails usually that any prefix of ss is feasible as well [1, 2]. So, 1QFA used for simulating QDES need to recognize with cut-point or bounded error prefix-closured languages. However, we will prove that any MO-1QFA is short of this ability. MM-1QFA can recognize some prefix-closured languages, but cannot recognize all regular languages with bounded error. Therefore, 1QFAC are better for simulating QDES. QDES simulated by 1QFAC can be thought of as hybrid systems of quantum and classical control since 1QFAC are an integration of MO-1QFA and DFA.

4.1 Some properties and new examples concerning QFA

First we present a result from [33].

Fact 1. [33] For any unitary matrix UU and any ϵ>0\epsilon>0 there exists an integer n>0n>0 such that IUn2<ϵ\|I-U^{n}\|_{2}<\epsilon.

We call a language LL is prefix-closured if for any sLs\in L, any prefix of ss also belongs to LL. From the above fact we can prove that no MO-1QFA can recognize prefix-closured languages. That is the following Fact.

Fact 2. Let Σ\Sigma be a finite alphabet, and let LΣL\subsetneq\Sigma^{*} be any regular language with prefix closure. Then no MO-1QFA can recognize LL with cut-point or bounded error.

Proof: First we note that empty string ϵL\epsilon\in L. If any sLs\in L and any σΣ\sigma\in\Sigma imply sσLs\sigma\in L, then it is easy to see L=ΣL=\Sigma^{*}. So, there exist sLs\in L and σΣ\sigma\in\Sigma such that sσLs\sigma\notin L, and therefore sσkLs\sigma^{k}\notin L for any k1k\geq 1. If there exist an MO-1QFA =(Q,|ψ0,{U(σ)}σΣ,Qa,Qr){\cal M}=(Q,|\psi_{0}\rangle,\{U(\sigma)\}_{\sigma\in\Sigma},Q_{a},Q_{r}) and a cut-point 0λ<10\leq\lambda<1 such that {\cal M} recognizes LL with cut-point λ\lambda, then P(a)Us|ψ02>λ\|P(a)U_{s}|\psi_{0}\rangle\|^{2}>\lambda due to sLs\in L. By virtue of Fact 1, there is k1k\geq 1 such that UσkI2<P(a)Us|q1λ\|U_{\sigma}^{k}-I\|_{2}<\|P(a)U_{s}|q_{1}\rangle\|-\sqrt{\lambda}, and therefore we have

P(a)Usσk|q1P(a)Us|q1\displaystyle\|P(a)U_{s{\sigma}^{k}}|q_{1}\rangle-P(a)U_{s}|q_{1}\rangle\|\leq Usσk|q1Us|q1\displaystyle\|U_{s{\sigma}^{k}}|q_{1}\rangle-U_{s}|q_{1}\rangle\| (32)
\displaystyle\leq UσkI2\displaystyle\|U_{\sigma}^{k}-I\|_{2} (33)
<\displaystyle< P(a)Us|q1λ,\displaystyle\|P(a)U_{s}|q_{1}\rangle\|-\sqrt{\lambda}, (34)

which results in P(a)Usσk|q1>λ\|P(a)U_{s{\sigma}^{k}}|q_{1}\rangle\|>\sqrt{\lambda}, implying sσkLs{\sigma}^{k}\in L, a contradiction. So, we have no MO-1QFA recognizing LL with cut-point (or bounded error).       \blacksquare

Now we present the first prefix-closured regular language that shows the state complexity advantage of 1QFAC over PFA.

Example 1.

For any m+m\in\mathbb{Z}^{+}, let

L(m)={w{0,1}:0km1 is not a prefix of w,k}L(m)=\{w\in\{0,1\}^{*}:0^{km}1\text{ is not a prefix of }w,\forall k\in\mathbb{N}\} (35)

where \mathbb{N} is the set of all natural numbers, and +\mathbb{Z}^{+} is the set of all positive integers.

We have the following theorem.

Theorem 2.

There exists an 1QFAC recognizing language L(m)L(m) (Eq. (35)) with cut-point 0 and with 22 classical states and 22 quantum states, but any PFA recognizing L(m)L(m) with cut-point 0 has at least log2m\lceil\log_{2}m\rceil states.

Next we prove the theorem in detail. First we need a lemma.

Lemma 2.

Let LL be a language over Σ={0}\Sigma=\{0\}. Suppose the minimal DFA recognizing LL has mm states. Then any PFA recognizing LL with cut-point 0 has at least log2m\lceil\log_{2}m\rceil states.

Proof.

Let =(S,Σ,π,{M(σ)}σΣ,η)\mathcal{M}=(S,\Sigma,\pi,\{M(\sigma)\}_{\sigma\in\Sigma},\eta) be a PFA that recognizes LL with cut-point 0, where S={s1,s2,,s|S|}S=\{s_{1},s_{2},\cdots,s_{|S|}\}. Denote by pijp_{ij} the (i,j)(i,j)-th entry of M(0)M(0). For any siSs_{i}\in S, define δ\delta a transfer relation of states as follows:

δ({si},0)={sj:pij>0};\delta(\{s_{i}\},0)=\{s_{j}:p_{ij}>0\};

for any S1SS_{1}\subseteq S, define

δ(S1,0)=sS1δ({s},0);\delta(S_{1},0)=\mathop{\bigcup}\limits_{s\in S_{1}}\delta(\{s\},0);

for any S1SS_{1}\subseteq S, xΣx\in\Sigma^{*}, recursively define

δ(S1,0x)=δ(δ(S1,0),x).\delta(S_{1},0x)=\delta(\delta(S_{1},0),x).

Let S0={sS:the initial probability of s is greater than 0}S_{0}=\{s\in S:\text{the initial probability of $s$ is greater than $0$}\} and let FF be the set of accepting states of \mathcal{M}. It is easy to check that L0={xΣ:δ(S0,x)F}L_{\mathcal{M}}^{0}=\{x\in\Sigma^{*}:\delta(S_{0},x)\cap F\neq\varnothing\}.

Based on the above facts, we can construct a DFA recognizing LL with 2|S|2^{|S|} states. Let 𝒜=(𝒫(S),Σ,δ,S0,Qa)\mathcal{A}=(\mathcal{P}(S),\Sigma,\delta,S_{0},Q_{a}) be a DFA, where Qa={S1S:S1F}Q_{a}=\{S_{1}\subseteq S:S_{1}\cap F\neq\varnothing\}. We can see that xΣx\in\Sigma^{*} is accepted by 𝒜\mathcal{A}, iff δ(S0,x)Qa\delta(S_{0},x)\in Q_{a}, iff δ(S0,x)F\delta(S_{0},x)\cap F\neq\varnothing, and iff xL0x\in L_{\mathcal{M}}^{0}. Thus 𝒜\mathcal{A} recognizes L0L_{\mathcal{M}}^{0}.

Since the minimal DFA of LL has mm states and 𝒜\mathcal{A} has 2|S|2^{|S|} states, we get that 2|S|m2^{|S|}\geq m. Therefore |S|log2m|S|\geq\lceil\log_{2}m\rceil.  \blacksquare

From the above theorem we have a corollary.

Corollary 1.

Let L={0t:tmodm0}L=\{0^{t}:t\bmod m\neq 0\} be a language over Σ={0}\Sigma=\{0\}. Then any PFA recognizing LL with cut-point 0 has at least log2m\lceil\log_{2}m\rceil states, where m+m\in\mathbb{Z}^{+}.

Proof.

Immediate from Lemma 2.  \blacksquare

Due to Corollary 1 we can obtain the following proposition.

Proposition 3.

Any PFA recognizing language L(m)L(m) with cut-point 0 has at least log2m\lceil\log_{2}m\rceil states, where m+m\in\mathbb{Z}^{+}.

Proof.

We prove by contradiction. Let =(S,Σ,π,{M(σ)}σΣ,η)\mathcal{M}=(S,\Sigma,\pi,\{M(\sigma)\}_{\sigma\in\Sigma},\eta) be a PFA that recognizes L(m)L(m) with cut-point 0 and with less than log2m\lceil\log_{2}m\rceil states, where Σ={0,1}\Sigma=\{0,1\}. Define χ:|S||S|\chi:\mathbb{R}^{|S|}\rightarrow\mathbb{R}^{|S|} as: for any η1|S|\eta_{1}\in{\mathbb{R}}^{|S|}, denote by ai,bia_{i},b_{i} the ii-th entries of η1\eta_{1} and χ(η1)\chi(\eta_{1}), respectively, where

bi={0,if ai=0,1,if ai0.b_{i}=\begin{cases}0,&\text{if $a_{i}=0$},\\ 1,&\text{if $a_{i}\neq 0$}.\end{cases} (36)

Since \mathcal{M} recognizes L(m)L(m) with cut-point 0, we have πM(0)tM(1)η>0\pi M(0)^{t}M(1)\eta>0 iff tmodm0t\bmod m\neq 0. Since πM(0)tM(1)η>0\pi M(0)^{t}M(1)\eta>0 iff πM(0)tχ(M(1)η)>0\pi M(0)^{t}\chi(M(1)\eta)>0, we have πM(0)tχ(M(1)η)>0\pi M(0)^{t}\chi(M(1)\eta)>0 iff tmodm0t\bmod m\neq 0. Thus, PFA =(S,{0},π,{M(σ)}σ{0},χ(M(1)η))\mathcal{M}^{\prime}=(S,\{0\},\pi,\{M(\sigma)\}_{\sigma\in\{0\}},\chi(M(1)\eta)) can recognize unary language L={0t:tmodm0}L=\{0^{t}:t\bmod m\neq 0\} with cut-point 0. It contradicts Corollary 1. Therefore, the proposition holds.  \blacksquare

Now we prove the state complexity advantage of 1QFAC over PFA for recognizing L(m)L(m).

Proposition 4.

There exists an 1QFAC recognizing language L(m)L(m) with cut-point 0 and with 22 classical states and 22 quantum states, where m+m\in\mathbb{Z}^{+}.

Proof.

The idea for constructing a 1QFAC is as follows. For any input string wΣw\in\Sigma^{*}, we use classical states to determine whether ww starts with 0n10^{n}1, where nn\in\mathbb{N}. If not, we accept it. Otherwise we measure quantum states to determine whether nmodm=0n\bmod m=0. So, 1QFAC =(S,Q,Σ,s0,|q0,δ,𝕌,𝒫)\mathcal{M}=(S,Q,\Sigma,s_{0},|q_{0}\rangle,\delta,\mathbb{U},\mathcal{P}) is constructed as follows, where

  • S={s0,sf}S=\{s_{0},s_{f}\};

  • Q={q0,q1}Q=\{q_{0},q_{1}\};

  • Σ={0,1}\Sigma=\{0,1\};

  • δ(s0,0)=s0,δ(s0,1)=sf,δ(sf,0)=δ(sf,1)=sf\delta(s_{0},0)=s_{0},\delta(s_{0},1)=s_{f},\delta(s_{f},0)=\delta(s_{f},1)=s_{f};

  • Us0,0=[cosπmsinπmsinπmcosπm]U_{s_{0},0}=\begin{bmatrix}\cos{\frac{\pi}{m}}&\sin{\frac{\pi}{m}}\\ -\sin{\frac{\pi}{m}}&\cos{\frac{\pi}{m}}\end{bmatrix} and other operators in 𝕌\mathbb{U} are II;

  • Ps0,acc=I,Ps0,rej=OP_{s_{0},acc}=I,P_{s_{0},rej}=O; Psf,acc=|q1q1|,Psf,rej=|q0q0|P_{s_{f},acc}=|q_{1}\rangle\langle q_{1}|,P_{s_{f},rej}=|q_{0}\rangle\langle q_{0}|.

It works as follow. For any input string wΣw\in\Sigma^{*}, if ww does not start with 0n10^{n}1, where nn\in\mathbb{N}, that is w{0}w\in\{0\}^{*}, then the final classical state of \mathcal{M} is s0s_{0} and ww will be accepted with probability 11. If ww starts with 0n10^{n}1, then the final classical state of \mathcal{M} is sfs_{f}. The final quantum state is Us0,0n|q0U_{s_{0},0}^{n}|q_{0}\rangle. Note that

Us0,0n=[cosπnmsinπnmsinπnmcosπnm].U_{s_{0},0}^{n}=\begin{bmatrix}\cos{\frac{\pi n}{m}}&\sin{\frac{\pi n}{m}}\\ -\sin{\frac{\pi n}{m}}&\cos{\frac{\pi n}{m}}\end{bmatrix}.\ (37)

We obtain that the accepting probability L(w)L_{\cal M}(w) is

L(w)\displaystyle L_{\cal M}(w) =Psf,accUs0,0n|q02\displaystyle=\|P_{s_{f},acc}U_{s_{0},0}^{n}|q_{0}\rangle\|^{2} (38)
=sin2πnm.\displaystyle=\sin^{2}{\frac{\pi n}{m}}. (39)

If nmodm=0n\bmod m=0, then L(w)=0L_{\cal M}(w)=0, otherwise L(w)>0L_{\cal M}(w)>0. Therefore, the proposition holds.  \blacksquare

By combining Propositions 3 and 4, we have proved Theorem 2.

Remark 2.

For any p,q+p,q\in\mathbb{Z}^{+} and p,q2p,q\geq 2, it is clear that L(p)L(pq)L(p)\subset L(pq) holds.


In addition, we give another example L(N)L^{(N)} that shows 1QFAC have the state complexity advantage over DFA. We can construct a PFA that requires more states than 1QFAC to recognize L(N)L^{(N)}, but we still do not know the minimal state number of PFA recognizing L(N)L^{(N)}.

Example 2.

Let Σ={0,1,2}\Sigma=\{0,1,2\}. Given a natural number NN,

L(N)=\displaystyle L^{(N)}= {wΣ:|w0,1|<2N}{wΣ:\displaystyle\{w\in\Sigma^{\ast}:|w_{0,1}|<2N\}\cup\{w\in\Sigma^{\ast}:
|w0,1|=2N,w0,1=x1x2xNy1y2yN},\displaystyle|w_{0,1}|=2N,w_{0,1}=x_{1}x_{2}\cdots x_{N}y_{1}y_{2}\cdots y_{N}\}, (40)

where

i=1Nxi2Ni+i=1Nyi2Ni=2N1\sum_{i=1}^{N}x_{i}2^{N-i}+\sum_{i=1}^{N}y_{i}2^{N-i}=2^{N}-1 (41)

and w0,1w_{0,1} denotes the substring of ww by removing all 22 in ww.

Remark 3.

By means of Myhill-Nerode theorem [38], we can know that DFA require Ω(2N)\Omega(2^{N}) states to recognize the language L(N)L^{(N)}. In fact, as usual, define the equivalence relation L(N)\equiv_{L^{(N)}} over Σ\Sigma^{*}: for any x,yΣx,y\in\Sigma^{*}, xL(N)yx\equiv_{L^{(N)}}y if and only if for any zΣz\in\Sigma^{*}, xzL(N)yzL(N)xz\in L^{(N)}\Leftrightarrow yz\in L^{(N)}. For any x,y{0,1}x,y\in\{0,1\}^{*}, with |x|=|y|=N|x|=|y|=N and xyx\neq y, then there is z{0,1}z\in\{0,1\}^{*} with |z|=N|z|=N such that x+z=2N1x+z=2^{N}-1 (i.e., xzL(N)xz\in L^{(N)}). However, yzL(N)yz\notin L^{(N)} since x+zy+zx+z\neq y+z due to xyx\neq y. So, xL(N)yx\nequiv_{L^{(N)}}y and the number of equivalence classes is at least |{0,1}N|=2N|\{0,1\}^{N}|=2^{N}. As a result, the number of states of any DFA recognizing L(N)L^{(N)} is at least 2N2^{N} as well.

For 1QFAC to recognize L(N)L^{(N)}, we have the following result.

Theorem 3.

For any 0<ϵ<10<\epsilon<1, there exists a 1QFAC {\cal M} having 2N+22N+2 classical states and Θ(N)\Theta(N) quantum basis states to recognize L(N)L^{(N)}, satisfying L(w)=1L_{{\cal M}}(w)=1 for every wL(N)w\in L^{(N)}, and L(w)<ϵL_{{\cal M}}(w)<\epsilon for every wΣL(N)w\in\Sigma^{*}\setminus L^{(N)}.

Proof.

{\cal M} can be constructed as follows. First we need to employ an important result by Ambainis and Freivalds [21]: For language {0kp:k}\{0^{kp}:k\in\mathbb{N}\}, where pp is a prime number and 2N+1<p<2N+22^{N+1}<p<2^{N+2}, then there is an MO-QFA 0{\cal M}_{0} recognizing {0kp:k}\{0^{kp}:k\in\mathbb{N}\}, say 0=(Q,|ψ0,{U(0)},Qa,Qr){\cal M}_{0}=(Q,|\psi_{0}\rangle,\{U(0)\},Q_{a},Q_{r}), where U(0)p=IU(0)^{p}=I and |Q|=Θ(logp)|Q|=\Theta(\log p). Then P(a)U(0)t|ψ02=1\|P(a)U(0)^{t}|\psi_{0}\rangle\|^{2}=1 for t=kpt=kp with some kk\in\mathbb{N}, and P(a)U(0)t|ψ02<ϵ\|P(a)U(0)^{t}|\psi_{0}\rangle\|^{2}<\epsilon for tkpt\neq kp with any kk\in\mathbb{N}.

1QFAC =(S,Q,Σ,s0,|φ0,δ,𝕌,𝒫){\cal M}=(S,Q,\Sigma,s_{0},|\varphi_{0}\rangle,\delta,\mathbb{U},{\cal P}) can be constructed as:

  • S={si:i=0,1,,2N+1}S=\{s_{i}:i=0,1,\ldots,2N+1\};

  • For σ{0,1}\sigma\in\{0,1\}, δ(si,σ)=si+1\delta(s_{i},\sigma)=s_{i+1} for i2Ni\leq 2N; δ(s2N+1,σ)=s2N+1\delta(s_{2N+1},\sigma)=s_{2N+1}, and δ(si,2)=si\delta(s_{i},2)=s_{i} for any siSs_{i}\in S;

  • 𝒫={𝒫si:siS}{\cal P}=\{{\cal P}_{s_{i}}:s_{i}\in S\} where 𝒫si={Psi,a,Psi,r}{\cal P}_{s_{i}}=\{P_{s_{i},a},P_{s_{i},r}\} and Psi,a=IP_{s_{i},a}=I for i<2Ni<2N, Ps2N,a=P(a)P_{s_{2N},a}=P(a), Ps2N+1,r=IP_{s_{2N+1},r}=I;

  • |φ0=U(0)p2N+1|ψ0|\varphi_{0}\rangle=U(0)^{p-2^{N}+1}|\psi_{0}\rangle;

  • 𝕌={Usσ:sS,σΣ}\mathbb{U}=\{U_{s\sigma}:s\in S,\sigma\in\Sigma\} where Usσ=U(0)σ2N1iU_{s\sigma}=U(0)^{\sigma 2^{N-1-i}} for σ{0,1}\sigma\in\{0,1\}, s=sis=s_{i} or s=sN+is=s_{N+i}, with iN1i\leq N-1; Us2NσU_{s_{2N}\sigma} and Us2N+1σU_{s_{2N+1}\sigma} can be any unitary operator for σ{0,1}\sigma\in\{0,1\}, Us2=IU_{s2}=I for any sSs\in S.

In the light of the above constructions, for wΣw\in\Sigma^{*}, if |w0,1|<2N|w_{0,1}|<2N then ww is accepted exactly; if |w0,1|>2N|w_{0,1}|>2N then ww is rejected exactly; if |w0,1|=2N|w_{0,1}|=2N, denote w0,1=x1x2xNy1y2yNw_{0,1}=x_{1}x_{2}\cdots x_{N}y_{1}y_{2}\cdots y_{N}, then the classical state is s2Ns_{2N}, and the quantum state is

Us2N1yNUsNy1UsN1xNUs1x2Us0x1|φ0\displaystyle U_{s_{2N-1}y_{N}}\cdots U_{s_{N}y_{1}}U_{s_{N-1}x_{N}}\cdots U_{s_{1}x_{2}}U_{s_{0}x_{1}}|\varphi_{0}\rangle (42)
=\displaystyle= U(0)p2N+1+i=1Nxi2Ni+i=1Nyi2Ni|ψ0,\displaystyle U(0)^{p-2^{N}+1+\sum_{i=1}^{N}x_{i}2^{N-i}+\sum_{i=1}^{N}y_{i}2^{N-i}}|\psi_{0}\rangle, (43)
=\displaystyle= |ψ(w),\displaystyle|\psi(w)\rangle, (44)

and the accepting probability is

P(a)|ψ(w)2.\|P(a)|\psi(w)\rangle\|^{2}. (45)

So, L(w)=1L_{{\cal M}}(w)=1 for wL(N)w\in L^{(N)} and L(w)ϵL_{{\cal M}}(w)\leq\epsilon for wL(N)w\notin L^{(N)}.

In fact, after reading input symbol 2, neither the classical nor quantum states have been changed, so without loss of generalization, we consider the dynamics of {\cal M} for computing string σ0σ1σ2N1{0,1}\sigma_{0}\sigma_{1}\ldots\sigma_{2N-1}\in\{0,1\}^{*}, and it is depicted by Fig. 5.  \blacksquare

Refer to caption
Figure 5: 1QFAC dynamics {\cal M} for input string σ0σ1σN1{0,1}\sigma_{0}\sigma_{1}\ldots\sigma_{N-1}\in\{0,1\}^{*}.

For PFA to recognize L(N)L^{(N)}, we have:

Theorem 4.

For any 0<ϵ<10<\epsilon<1, there exists a PFA {\cal M^{\prime}} with O(N3)O(N^{3}) states recognizing L(N)L^{(N)}, satisfying that the accepting probability P(w)>1ϵP_{{\cal M^{\prime}}}(w)>1-\epsilon for wL(N)w\in L^{(N)} and P(w)<ϵP_{{\cal M^{\prime}}}(w)<\epsilon for wL(N)w\notin L^{(N)}.

Proof.

Due to Theorem 10 by Ambainis and Freivalds in [21], there exists a PFA =(S,{0},π,{M(σ)}σ{0},η)\mathcal{M}=(S,\{0\},\pi,\{M(\sigma)\}_{\sigma\in\{0\}},\eta) with O(N2)O(N^{2}) states recognizing L={02N1}L=\{0^{2^{N}-1}\} with probability 1ϵ1-\epsilon, that is, πM(0)2N1η>1ϵ\pi M(0)^{2^{N}-1}\eta>1-\epsilon and πM(0)tη<ϵ\pi M(0)^{t}\eta<\epsilon for t2N1t\neq 2^{N}-1. We construct a PFA =(S,{0,1,2},π,{M(σ)}σ{0,1,2},η)\mathcal{M}^{\prime}=(S^{\prime},\{0,1,2\},\pi^{\prime},\{M^{\prime}(\sigma)\}_{\sigma\in\{0,1,2\}},\eta^{\prime}) recognizing L(N)L^{(N)} with probability 1ϵ1-\epsilon, where M(2)M^{\prime}(2) is an identity operator and the others are defined as follows:

  • S={(s,q):s{s0,,s2N+1},qS}S^{\prime}=\{(s,q):s\in\{s_{0},\cdots,s_{2N+1}\},q\in S\}, and for convenience, we denote the states in SS^{\prime} as s|q|\langle s|\langle q|;

  • π=s0|π\pi^{\prime}=\langle s_{0}|\otimes\pi;

  • si|q|M(σ)=si+1|(q|M(0)σ2N1i),\langle s_{i}|\langle q|M^{\prime}(\sigma)=\langle s_{i+1}|\left(\langle q|M(0)^{\sigma 2^{N-1-i}}\right),
    sN+i|q|M(σ)=sN+i+1|(q|M(0)σ2N1i),\langle s_{N+i}|\langle q|M^{\prime}(\sigma)=\langle s_{N+i+1}|\left(\langle q|M(0)^{\sigma 2^{N-1-i}}\right),
    s2N|q|M(σ)=s2N+1|q|M(σ)=s2N+1|q|,\langle s_{2N}|\langle q|M^{\prime}(\sigma)=\langle s_{2N+1}|\langle q|M^{\prime}(\sigma)=\langle s_{2N+1}|\langle q|,

    i=0,,N1i=0,\cdots,N-1, σ{0,1}\sigma\in\{0,1\}, qSq\in S;

  • η=(i=02N1|si)|e+|s2Nη\eta^{\prime}=(\sum\limits_{i=0}^{2N-1}|s_{i}\rangle)\otimes|e\rangle+|s_{2N}\rangle\otimes\eta, where |e|e\rangle represents a vector in |S|\mathbb{R}^{|S|} that all of its elements are 1.

For any input string wΣw\in\Sigma^{*}, \mathcal{M}^{\prime} works as follows. According to the definition of η\eta^{\prime}, if |w0,1|<2N|w_{0,1}|<2N, then the resulting state of \mathcal{M}^{\prime} computing ww is si|q|\langle s_{i}|\langle q|, where i<2Ni<2N and q|\langle q| is a superposition state. Hence ww will be accepted with probability

si|q|η=1.\langle s_{i}|\langle q|\eta^{\prime}=1.

If |w0,1|>2N|w_{0,1}|>2N, then the state of \mathcal{M}^{\prime} is s2N+1|q|\langle s_{2N+1}|\langle q|, where q|\langle q| is a superposition state. Hence the accepting probability of ww is

s2N+1|q|η=0.\langle s_{2N+1}|\langle q|\eta^{\prime}=0.

If |w0,1|=2N|w_{0,1}|=2N, that is w=x1xNy1yNw=x_{1}\cdots x_{N}y_{1}\cdots y_{N}, then we have

πM(x1)M(xN)M(y1)M(yN)η\displaystyle\pi^{\prime}M^{\prime}(x_{1})\cdots M^{\prime}(x_{N})M^{\prime}(y_{1})\cdots M^{\prime}(y_{N})\eta^{\prime} (46)
=\displaystyle= (s0|π)M(x1)M(xN)M(y1)M(yN)η\displaystyle(\langle s_{0}|\otimes\pi)M^{\prime}(x_{1})\cdots M^{\prime}(x_{N})M^{\prime}(y_{1})\cdots M^{\prime}(y_{N})\eta^{\prime} (47)
=\displaystyle= (s2N|πM(0)i=1Nxi2Ni+i=1Nyi2Ni)η\displaystyle\left(\langle s_{2N}|\pi M(0)^{\sum\limits_{i=1}^{N}x_{i}2^{N-i}+\sum\limits_{i=1}^{N}y_{i}2^{N-i}}\right)\eta^{\prime} (48)
=\displaystyle= (s2N|πM(0)i=1Nxi2Ni+i=1Nyi2Ni)|s2Nη\displaystyle\left(\langle s_{2N}|\pi M(0)^{\sum\limits_{i=1}^{N}x_{i}2^{N-i}+\sum\limits_{i=1}^{N}y_{i}2^{N-i}}\right)|s_{2N}\rangle\otimes\eta (49)
=\displaystyle= πM(0)i=1Nxi2Ni+i=1Nyi2Niη.\displaystyle\pi M(0)^{\sum\limits_{i=1}^{N}x_{i}2^{N-i}+\sum\limits_{i=1}^{N}y_{i}2^{N-i}}\eta. (50)

Hence, if i=1Nxi2Ni+i=1Nyi2Ni=2N1\sum\limits_{i=1}^{N}x_{i}2^{N-i}+\sum\limits_{i=1}^{N}y_{i}2^{N-i}=2^{N}-1, ww will be accepted with probability greater than 1ϵ1-\epsilon, otherwise ww will be accepted with probability less than ϵ\epsilon.  \blacksquare

Remark 4.

So, for prefix-closured regular language L(N)L^{(N)}, there exists a 1QFAC {\cal M} having 2N+22N+2 classical states and Θ(N)\Theta(N) quantum basis states to recognize L(N)L^{(N)} with bounded error, and there exists a PFA {\cal M^{\prime}} with O(N3)O(N^{3}) states recognizing L(N)L^{(N)} with bounded error. However, we still do not know the lower bound on the state complexity of PFA for recognizing L(N)L^{(N)}.  \blacksquare

4.2 Supervisory Control of QDES simulated with cut-point languages

Let {\cal M} be a 1QFAC, and let KLλK\subseteq L_{{\cal M}}^{\lambda} (0λ<10\leq\lambda<1) be the set of specifications we hope to achieve, where KK is also a regular language that is recognized by a 1QFAC {\cal H} with cut-point μ\mu (μλ\mu\geq\lambda) isolated by ρ\rho. First, we give a sufficient condition such that there is a quantum supervisor controlling QDES {\cal M} to approximate to KK, and this is the first supervisory control theorem of QDES.

From now on, a QDES associated with a 1QFAC {\cal M} always has L(ϵ)=1L_{\cal M}(\epsilon)=1, that is, the initial state is an accepting state.

Theorem 5.

Let Σ\Sigma be a finite event set and Σ=ΣucΣc\Sigma=\Sigma_{uc}\cup\Sigma_{c}. Suppose a QDES with event set Σ\Sigma is modeled as LλL_{\cal M}^{\lambda} for a 1QFAC {\cal M} with 0λ<10\leq\lambda<1. Let KΣK\subset\Sigma^{*} be recognized by a 1QFAC {\cal H} with cut-point μ\mu (μλ\mu\geq\lambda) isolated by ρ\rho, where pr(K)Lλpr(K)\subseteq L_{\cal M}^{\lambda} and LLL_{\cal H}\leq L_{\cal M} (but L(x)=L(x)L_{\cal H}(x)=L_{\cal M}(x) for xpr(K)x\in pr(K) ). If sΣ\forall s\in\Sigma^{\ast}, σΣuc\forall\sigma\in\Sigma_{uc},

min{L(s),L(sσ)}L(sσ),\min\{L_{\cal H}(s),L_{\cal M}(s\sigma)\}\leq L_{\cal H}(s\sigma), (51)

then there is a quantum supervisor 𝒮:Lλ[0,1]Σ{\cal S}:L_{\cal M}^{\lambda}\rightarrow[0,1]^{\Sigma} such that

L𝒮/μKL𝒮/λ.L_{{\cal S}/{\cal M}}^{\mu}\subseteq K\subseteq L_{{\cal S}/{\cal M}}^{\lambda}. (52)

Proof.

Let

𝒮(s)(σ)={L(sσ),if σΣuc,L(sσ),if σΣc.\displaystyle{\cal S}(s)(\sigma)=\begin{cases}L_{\cal M}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ L_{\cal H}(s\sigma),&\text{if }\sigma\in\Sigma_{c}.\\ \end{cases} (53)

Recall sΣ\forall s\in\Sigma^{\ast}, σΣ\forall\sigma\in\Sigma,

L𝒮/(sσ)=min{L𝒮/(s),L(sσ),𝒮(s)(σ)}.L_{{\cal S}/{\cal M}}(s\sigma)=\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\}. (54)

Suppose that L(s)=L𝒮/(s)L_{\cal H}(s)=L_{{\cal S}/{\cal M}}(s) for |s|n|s|\leq n. Then σΣ\forall\sigma\in\Sigma,

L𝒮/(sσ)=\displaystyle L_{{\cal S}/{\cal M}}(s\sigma)=
{min{L(s),L(sσ)}L(sσ),if σΣuc,min{L(s),L(sσ),L(sσ)}L(sσ),σΣc.\displaystyle\begin{cases}\min\{L_{\cal H}(s),L_{\cal M}(s\sigma)\}\\ \leq L_{\cal H}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ \min\{L_{\cal H}(s),L_{\cal M}(s\sigma),L_{\cal H}(s\sigma)\}\\ \leq L_{\cal H}(s\sigma),&\sigma\in\Sigma_{c}.\\ \end{cases} (55)

On the other hand,

L(sσ)\displaystyle L_{\cal H}(s\sigma) min{L(sσ),L(s)}\displaystyle\leq\min\{L_{\cal M}(s\sigma),L_{\cal H}(s)\} (56)
={min{L(sσ),S(s)(σ),L𝒮/(s)}=L𝒮/(sσ),if σΣuc,min{L(sσ),L𝒮/(s),L(sσ)}=L𝒮/(sσ),σΣc.\displaystyle=\begin{cases}\min\{L_{\cal M}(s\sigma),S(s)(\sigma),L_{{\cal S}/{\cal M}}(s)\}\\ =L_{{\cal S}/{\cal M}}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ \min\{L_{\cal M}(s\sigma),L_{{\cal S}/{\cal M}}(s),L_{\cal H}(s\sigma)\}\\ =L_{{\cal S}/{\cal M}}(s\sigma),&\sigma\in\Sigma_{c}.\\ \end{cases} (57)

\blacksquare

Remark 5.

Theorem 5 shows that under certain conditions, there is a quantum supervisor to achieve an approximate objective specification. Next we give a sufficient and necessary condition for the existence of quantum supervisor to achieve a precise supervisory control, and this is described by the following theorem.  \blacksquare

Theorem 6.

Let Σ\Sigma be a finite event set and Σ=ΣucΣc\Sigma=\Sigma_{uc}\cup\Sigma_{c}. Suppose a QDES with event set Σ\Sigma is modeled as a quantum language LL_{\cal M} that is generated by an 1QFAC {\cal M}. Quantum language 𝒦{\cal K} generated by some 1QFAC satisfies pr(𝒦)Lpr({\cal K})\subseteq L_{\cal M}. Then there is a quantum supervisor 𝒮:Σ[0,1]Σ{\cal S}:\Sigma^{\ast}\rightarrow[0,1]^{\Sigma} such that L𝒮/=pr(𝒦)L_{{\cal S}/{\cal M}}=pr({\cal K}), if and only if sΣ\forall s\in\Sigma^{\ast}, σΣuc\forall\sigma\in\Sigma_{uc},

min{pr(𝒦)(s),L(sσ)}pr(𝒦)(sσ).\min\{pr({\cal K})(s),L_{\cal M}(s\sigma)\}\leq pr({\cal K})(s\sigma). (58)

Proof.

\Leftarrow). Let

𝒮(s)(σ)={L(sσ),if σΣuc,pr(𝒦)(sσ),if σΣc.\displaystyle{\cal S}(s)(\sigma)=\begin{cases}L_{\cal M}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ pr({\cal K})(s\sigma),&\text{if }\sigma\in\Sigma_{c}.\\ \end{cases} (59)

First L(ϵ)=1L_{\cal M}(\epsilon)=1 holds as we suppose the initial state is an accepting state. Recall sΣ\forall s\in\Sigma^{\ast}, σΣ\forall\sigma\in\Sigma,

L𝒮/(sσ)=min{L𝒮/(s),L(sσ),𝒮(s)(σ)}.L_{{\cal S}/{\cal M}}(s\sigma)=\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\}. (60)

Suppose that pr(𝒦)(s)=L𝒮/(s)pr({\cal K})(s)=L_{{\cal S}/{\cal M}}(s) for |s|n|s|\leq n. Then σΣ\forall\sigma\in\Sigma,

L𝒮/(sσ)=\displaystyle L_{{\cal S}/{\cal M}}(s\sigma)=
{min{pr(𝒦)(s),L(sσ)}pr(𝒦)(sσ),if σΣuc,min{pr(𝒦)(s),L(sσ),pr(𝒦)(sσ)}pr(𝒦)(sσ),if σΣc.\displaystyle\begin{cases}\min\{pr({\cal K})(s),L_{\cal M}(s\sigma)\}\\ \leq pr({\cal K})(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ \min\{pr({\cal K})(s),L_{\cal M}(s\sigma),pr({\cal K})(s\sigma)\}\\ \leq pr({\cal K})(s\sigma),&\text{if }\sigma\in\Sigma_{c}.\\ \end{cases} (61)

On the other hand,

pr(𝒦)(sσ)\displaystyle pr({\cal K})(s\sigma) (62)
min{L(sσ),pr(𝒦)(s)}\displaystyle\leq\min\{L_{\cal M}(s\sigma),pr({\cal K})(s)\} (63)
={min{L(sσ),𝒮(s)(σ),L𝒮/(s)}=L𝒮/(sσ),if σΣuc,min{L(sσ),L𝒮/(s),pr(𝒦)(sσ)}=L𝒮/(sσ),if σΣc.\displaystyle=\begin{cases}\min\{L_{\cal M}(s\sigma),{\cal S}(s)(\sigma),L_{{\cal S}/{\cal M}}(s)\}\\ =L_{{\cal S}/{\cal M}}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ \min\{L_{\cal M}(s\sigma),L_{{\cal S}/{\cal M}}(s),pr({\cal K})(s\sigma)\}\\ =L_{{\cal S}/{\cal M}}(s\sigma),&\text{if }\sigma\in\Sigma_{c}.\\ \end{cases} (64)

\Rightarrow). Let quantum supervisor 𝒮:Σ[0,1]Σ{\cal S}:\Sigma^{\ast}\rightarrow[0,1]^{\Sigma} satisfy that 𝒮(s)(σ)L(sσ){\cal S}(s)(\sigma)\geq L_{\cal M}(s\sigma) for any sΣs\in\Sigma^{\ast}, any σΣuc\sigma\in\Sigma_{uc}.

min{pr(𝒦)(s),L(sσ)}\displaystyle\min\{pr({\cal K})(s),L_{\cal M}(s\sigma)\}
=\displaystyle= min{L𝒮/(s),L(sσ)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma)\} (65)
=\displaystyle= min{L𝒮/(s),L(sσ),𝒮(s)(σ)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\} (66)
=\displaystyle= L𝒮/(sσ)\displaystyle L_{{\cal S}/{\cal M}}(s\sigma) (67)
=\displaystyle= pr(𝒦)(sσ).\displaystyle pr({\cal K})(s\sigma). (68)

So, we complete the proof of theorem.  \blacksquare


From Theorem 6 we can obtain a corollary, and this is a modelling fashion of QDES with cut-point.

Corollary 2.

Suppose a QDES is modeled as a cut-point language LλL_{\cal M}^{\lambda} recognized by a 1QFAC {\cal M} with 0λ<10\leq\lambda<1. Quantum language 𝒦{\cal K} generated by some 1QFAC satisfies pr(𝒦)Lpr({\cal K})\subseteq L_{\cal M}. Then there is a quantum supervisor 𝒮:Σ[0,1]Σ{\cal S}:\Sigma^{\ast}\rightarrow[0,1]^{\Sigma} such that L𝒮/λ=pr(𝒦)λL_{{\cal S}/{\cal M}}^{\lambda}=pr({\cal K})^{\lambda}, if and only if spr(𝒦)λ\forall s\in pr({\cal K})^{\lambda}, σΣuc\forall\sigma\in\Sigma_{uc}, if sσLλs\sigma\in L_{\cal M}^{\lambda}, then sσpr(𝒦)λs\sigma\in pr({\cal K})^{\lambda}.

Proof.

\Leftarrow). Let

𝒮(s)(σ)={L(sσ),if σΣuc,pr(𝒦)(sσ),if σΣc.\displaystyle{\cal S}(s)(\sigma)=\begin{cases}L_{\cal M}(s\sigma),&\text{if }\sigma\in\Sigma_{uc},\\ pr({\cal K})(s\sigma),&\text{if }\sigma\in\Sigma_{c}.\\ \end{cases} (69)

By means of Inequalities Proof and 62 we can obtain that spr(𝒦)λs\in pr({\cal K})^{\lambda} if and only if sL𝒮/λs\in L_{{\cal S}/{\cal M}}^{\lambda}, for any sΣs\in\Sigma^{\ast}.

\Rightarrow). Recall the supervisor 𝒮{\cal S} satisfies the quantum admissible condition (Eq. (24)): for any sΣs\in\Sigma^{\ast}, any σΣuc\sigma\in\Sigma_{uc}, 𝒮(s)(σ)L(sσ){\cal S}(s)(\sigma)\geq L_{\cal M}(s\sigma). Therefore, with the definition L𝒮/λL_{{\cal S}/{\cal M}}^{\lambda}, we have sσpr(𝒦)λs\sigma\in pr({\cal K})^{\lambda}.  \blacksquare

4.3 An example to illustrate supervisory control theorems of QDES

Theorem 6 and its Corollary 2 are the main supervisory control results of QDES. As an application of Theorem 6 (or Corollary 2), we present an example to show the advantage of QDES over classical DES in state complexity.

Example 3.

We employ the language L(m)L(m) in Example 1. Let Σ={0,1}\Sigma=\{0,1\}, Σuc={0}\Sigma_{uc}=\{0\}, Σc={1}\Sigma_{c}=\{1\}. Given an natural number m>2m>2, suppose a QDES modeled as the cut-point language

L0=L(m)={w{0,1}:0km1 is not a prefix of w,k}L_{\cal M}^{0}=L(m)=\{w\in\{0,1\}^{*}:0^{km}1\text{ is not a prefix of }w,\forall k\in\mathbb{N}\} (70)

recognized by a 1QFAC {\cal M} with cut-point 0, and quantum language 𝒦{\cal K} is generated by another 1QFAC 𝒦{\cal M}_{{\cal K}} that will be defined in the following.

{\cal M} is defined as Example 1, and L(w)>0L_{{\cal M}}(w)>0 for any wΣw\in\Sigma^{\ast} iff wL(m)w\in L(m).

For any given natural number q>2q>2, we consider the language L(mq)L(mq) that is recognized by another 1QFAC 𝒦{\cal M}_{\cal K} with cut-point 0. 𝒦{\cal M}_{\cal K} can be defined by means of {\cal M}, and 𝒦(w)=L𝒦(w)>0{\cal K}(w)=L_{{\cal M}_{\cal K}}(w)>0 for any wΣw\in\Sigma^{\ast} iff wL(mq)w\in L(mq).

Therefore, we have L0=L(m)L_{\cal M}^{0}=L(m) and pr(𝒦)0=𝒦0=L(mq)pr({\cal K})^{0}={\cal K}^{0}=L(mq).

It is immediate to check that the condition “wpr(𝒦)0\forall w\in pr({\cal K})^{0}, σΣuc\forall\sigma\in\Sigma_{uc}, if wσL0w\sigma\in L_{\cal M}^{0}, then wσpr(𝒦)0w\sigma\in pr({\cal K})^{0}” in Corollary 2 holds, so there is a quantum supervisor S:Σ[0,1]ΣS:\Sigma^{\ast}\rightarrow[0,1]^{\Sigma} such that L𝒮/0=pr(𝒦)0L_{{\cal S}/{\cal M}}^{0}=pr({\cal K})^{0}.

Due to Theorem 2, we know that PFA (i.e., PDES) require log2m\lceil\log_{2}m\rceil and log2mq\lceil\log_{2}mq\rceil states to recognize the languages L0L_{\cal M}^{0} and pr(𝒦)0pr({\cal K})^{0} with cut-point 0, respectively. So, QDES show essential advantage over classical DES in state complexity for simulation of systems.

\blacksquare


4.4 Supervisory Control of QDES simulated with isolated cut-point languages

In this section we study nonblocking problem in QDES. In this case, it is more suitsble to consider QDES simulated with the languages (say LL) of cut-point λ\lambda isolated by an ρ\rho, and the controlled language by quantum supervisor 𝒮{\cal S} is required to belong to this language LL. Our main purpose is to establish nonblocking quantum supervisor theorem in QDES.

First, we would recall nonblocking problem in classical DES [1, 2]. Let a DES modeled by finite automaton G=(Q,Σ,δ,q0,Qm)G=(Q,\Sigma,\delta,q_{0},Q_{m}). Then GG is called nonblocking if pr(Lm(G))=L(G)pr(L_{m}(G))=L(G). That is, for any feasible input string ss, input string stst will reach a marked state for some string tt. Let SS be a supervisor. Then the language marked by S/GS/G is defined as:

Lm(S/G)=L(S/G)Lm(G).L_{m}(S/G)=L(S/G)\cap L_{m}(G). (71)

The DES modeled by L(S/G)L(S/G) is nonblocking if L(S/G)=pr(Lm(S/G)).L(S/G)=pr(L_{m}(S/G)). In practice, L(S/G)L(S/G) being nonblocking is important since it leads to each string in L(S/G)L(S/G) together with some more input string being able to reach a marked state.

Suppose that 1QFAC {\cal M} recognizes a language (denoted by Lλ,ρL_{\cal M}^{\lambda,\rho}) over alphabet Σ\Sigma with cut-point λ\lambda isolated by ρ\rho. For any sΣs\in\Sigma^{\ast}, denote

L,a(s)={L(s),if sLλ,ρ,0,otherwise,\displaystyle L_{{\cal M},a}(s)=\begin{cases}L_{\cal M}(s),&\text{if }s\in L_{\cal M}^{\lambda,\rho},\\ 0,&\text{otherwise},\\ \end{cases} (72)

and

L𝒮/,a(s)={min{L(s),L𝒮/(s)},if sLλ,ρ,0,otherwise.\displaystyle L_{{\cal S}/{\cal M},a}(s)=\begin{cases}\min\{L_{\cal M}(s),L_{{\cal S}/{\cal M}}(s)\},&\text{if }s\in L_{\cal M}^{\lambda,\rho},\\ 0,&\text{otherwise}.\\ \end{cases} (73)

For any quantum language LL over Σ\Sigma, denote by supp(L)\text{supp}(L) the support set of LL, i.e., supp(L)={sΣ:L(s)>0}\text{supp}(L)=\{s\in\Sigma^{\ast}:L(s)>0\}. A quantum supervisor 𝒮{\cal S} is called as nonblocking if it satisfies L𝒮/=pr(L𝒮/,a)L_{{\cal S}/{\cal M}}=pr(L_{{\cal S}/{\cal M},a}).

Theorem 7.

Suppose a QDES is modeled as a language L,aL_{{\cal M},a} recognized by a 1QFAC {\cal M} with cut-point λ\lambda isolated by ρ\rho. 𝒦{\cal K} is a quantum language over Σ\Sigma. Let pr(𝒦)L,apr({\cal K})\leq L_{{\cal M},a}. Then there is a quantum supervisor 𝒮{\cal S} satisfying nonblocking (i.e., L𝒮/=pr(L𝒮/,a)L_{{\cal S}/{\cal M}}=pr(L_{{\cal S}/{\cal M},a})) such that 𝒦=L𝒮/,a{\cal K}=L_{{\cal S}/{\cal M},a} and pr(𝒦)=L𝒮/pr({\cal K})=L_{{\cal S}/{\cal M}} if and only if

  1. 1.

    sΣ\forall s\in\Sigma^{\ast}, σΣuc\forall\sigma\in\Sigma_{uc},

    min{pr(𝒦)(s),L(sσ)}pr(𝒦)(sσ);\displaystyle\min\{pr({\cal K})(s),L_{\cal M}(s\sigma)\}\leq pr({\cal K})(s\sigma); (74)
  2. 2.

    sΣ\forall s\in\Sigma^{\ast},

    𝒦(s)=min{pr(𝒦)(s),L,a(s)}.\displaystyle{\cal K}(s)=\min\{pr({\cal K})(s),L_{{\cal M},a}(s)\}. (75)

Proof.

\Leftarrow). sΣ\forall s\in\Sigma^{\ast}, let

𝒮(s)(σ)={L(sσ),σΣuc,pr(𝒦)(sσ),σΣc.\displaystyle{\cal S}(s)(\sigma)=\begin{cases}L_{\cal M}(s\sigma),&\sigma\in\Sigma_{uc},\\ pr({\cal K})(s\sigma),&\sigma\in\Sigma_{c}.\\ \end{cases} (76)

First, L𝒮/(ϵ)=1=pr(𝒦)(ϵ)=suptΣ𝒦(t)=1L_{{\cal S}/{\cal M}}(\epsilon)=1=pr({\cal K})(\epsilon)=\sup_{t\in\Sigma^{\ast}}{\cal K}(t)=1 (𝒦(ϵ)=1{\cal K}(\epsilon)=1). Suppose sΣs\in\Sigma^{\ast} and |s|n|s|\leq n, L𝒮/(s)=pr(𝒦)(s)L_{{\cal S}/{\cal M}}(s)=pr({\cal K})(s). Then σΣ\forall\sigma\in\Sigma,

(I) if σΣuc\sigma\in\Sigma_{uc}, then

L𝒮/(sσ)=\displaystyle L_{{\cal S}/{\cal M}}(s\sigma)= min{L𝒮/(s),L(sσ),𝒮(s)(σ)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\} (77)
=\displaystyle= min{L𝒮/(s),L(sσ)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{{\cal M}}(s\sigma)\} (78)
=\displaystyle= min{pr(𝒦)(s),L(sσ)}\displaystyle\min\{pr({\cal K})(s),L_{{\cal M}}(s\sigma)\} (79)
\displaystyle\leq pr(𝒦)(sσ);\displaystyle pr({\cal K})(s\sigma); (80)

(II) if σΣc\sigma\in\Sigma_{c}, then it holds as well, since 𝒮(s)(σ)=pr(𝒦)(sσ){\cal S}(s)(\sigma)=pr({\cal K})(s\sigma). On the other hand,

pr(𝒦)(sσ)\displaystyle pr({\cal K})(s\sigma)\leq min{pr(𝒦)(s),L(sσ)}\displaystyle\min\{pr({\cal K})(s),L_{\cal M}(s\sigma)\} (81)
=\displaystyle= min{pr(𝒦)(s),L(sσ),𝒮(s)(σ)}\displaystyle\min\{pr({\cal K})(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\} (82)
=\displaystyle= L𝒮/(sσ).\displaystyle L_{{\cal S}/{\cal M}}(s\sigma). (83)

So, pr(𝒦)=L𝒮/pr({\cal K})=L_{{\cal S}/{\cal M}}.

For any sΣs\in\Sigma^{\ast}, if L(s)<λ+ρL_{\cal M}(s)<\lambda+\rho, then L𝒮/,a(s)=𝒦(s)=0L_{{\cal S}/{\cal M},a}(s)={\cal K}(s)=0; if L(s)λ+ρL_{\cal M}(s)\geq\lambda+\rho, then with condition 2) above, we have

𝒦(s)=\displaystyle{\cal K}(s)= min{pr(𝒦)(s),L,a(s)}\displaystyle\min\{pr({\cal K})(s),L_{{\cal M},a}(s)\} (84)
=\displaystyle= min{pr(𝒦)(s),L(s)}\displaystyle\min\{pr({\cal K})(s),L_{\cal M}(s)\} (85)
=\displaystyle= min{L𝒮/(s),L(s)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{{\cal M}}(s)\} (86)
=\displaystyle= L𝒮/,a(s).\displaystyle L_{{\cal S}/{\cal M},a}(s). (87)

\Rightarrow). 1) sΣ\forall s\in\Sigma^{\ast}, σΣuc\forall\sigma\in\Sigma_{uc}, since quantum supervisor 𝒮{\cal S} always satisfies that L(sσ)𝒮(s)(σ)L_{\cal M}(s\sigma)\leq{\cal S}(s)(\sigma), we have

min{pr(𝒦)(s),L(sσ)}\displaystyle\min\{pr({\cal K})(s),L_{{\cal M}}(s\sigma)\} (88)
\displaystyle\leq min{L𝒮/(s),L(sσ),𝒮(s)(σ)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s\sigma),{\cal S}(s)(\sigma)\} (89)
=\displaystyle= L𝒮/(sσ).\displaystyle L_{{\cal S}/{\cal M}}(s\sigma). (90)

2) sΣ\forall s\in\Sigma^{\ast}, if L(s)<λ+ρL_{\cal M}(s)<\lambda+\rho, then 𝒦(s)=0{\cal K}(s)=0 and pr(𝒦)(s)=L,a(s)=0pr({\cal K})(s)=L_{{\cal M},a}(s)=0; if L(s)λ+ρL_{\cal M}(s)\geq\lambda+\rho, then

𝒦(s)=\displaystyle{\cal K}(s)= L𝒮/,a(s)\displaystyle L_{{\cal S}/{\cal M},a}(s) (91)
=\displaystyle= min{L𝒮/(s),L(s)}\displaystyle\min\{L_{{\cal S}/{\cal M}}(s),L_{\cal M}(s)\} (92)
=\displaystyle= min{pr(𝒦)(s),L,a(s)}.\displaystyle\min\{pr({\cal K})(s),L_{{\cal M},a}(s)\}. (93)

Consequently, the proof is completed.  \blacksquare

As a special case of Theorem 7, the following corollary follows.

Corollary 3.

Suppose a QDES is modeled as a language Lλ,ρL_{{\cal M}}^{\lambda,\rho} recognized by a 1QFAC {\cal M} with cut point λ\lambda isolated by ρ\rho. KΣK\subset\Sigma^{*} is a regular language. Let pr(K)Lλ,ρpr(K)\subseteq L_{{\cal M}}^{\lambda,\rho}. Then there is a quantum supervisor 𝒮{\cal S} satisfying L𝒮/=pr(L𝒮/,a)L_{{\cal S}/{\cal M}}=pr(L_{{\cal S}/{\cal M},a}) such that K=L𝒮/,a0K=L_{{\cal S}/{\cal M},a}^{0} and pr(K)=L𝒮/0pr(K)=L_{{\cal S}/{\cal M}}^{0} if and only if the following two conditions hold:

  1. 1.
    pr(K)ΣucL0pr(K);\displaystyle pr(K)\Sigma_{uc}\cap L_{\cal M}^{0}\subseteq pr(K); (94)
  2. 2.
    K=pr(K)Lλ,ρ,\displaystyle K=pr(K)\cap L_{{\cal M}}^{\lambda,\rho}, (95)

where pr(K)Σuc={sσ:spr(K),σΣuc}pr(K)\Sigma_{uc}=\{s\sigma:s\in pr(K),\sigma\in\Sigma_{uc}\}.

Proof.

In fact, we can do it by taking 𝒦{\cal K} as a classical language in Theorem 7. So, we omit the details here.  \blacksquare

5 Decidability of Controllability Condition

In supervisory control of QDES, the controllability conditions play an important role of the existence of quantum supervisors. So, we present a polynomial-time algorithm to decide the controllability condition Eq. (58). The prefix-closure of quantum language 𝒦{\cal K}, as the target language we hope to achieve under the supervisory control, is in general generated by a 1QFAC {\cal H}, that is, pr(𝒦)=Lpr({\cal K})=L_{{\cal H}}. Then the controllability condition Eq. (58) is equivalently as: sΣ\forall s\in\Sigma^{\ast}, σΣuc\forall\sigma\in\Sigma_{uc},

min{L(s),L(sσ)}L(sσ).\min\{L_{\cal H}(s),L_{\cal M}(s\sigma)\}\leq L_{\cal H}(s\sigma). (96)

First, we need a proposition.

Proposition 5.

Suppose a QDES modeled as a quantum language LL_{\cal M} that is generated by a 1QFAC {\cal M}. Quantum language 𝒦{\cal K} satisfies pr(𝒦)Lpr({\cal K})\subseteq L_{\cal M}, and pr(𝒦)=Lpr({\cal K})=L_{{\cal H}} for some 1QFAC {\cal H}. Then L(s)L(sσ)L_{\cal H}(s)\geq L_{\cal H}(s\sigma) and L(sσ)L(sσ)L_{\cal M}(s\sigma)\geq L_{\cal H}(s\sigma) for any sΣs\in\Sigma^{\ast}, and σΣuc\sigma\in\Sigma_{uc}.

Proof.

By the definition of prefix closure, for any sΣs\in\Sigma^{\ast}, and σΣuc\sigma\in\Sigma_{uc}, we have L(s)=pr(𝒦)(s)=suptΣ𝒦(st)L_{\cal H}(s)=pr({\cal K})(s)=\sup_{t\in\Sigma^{\ast}}{\cal K}(st) and L(sσ)=pr(𝒦)(sσ)=suptΣ𝒦(sσt)L_{\cal H}(s\sigma)=pr({\cal K})(s\sigma)=\sup_{t\in\Sigma^{\ast}}{\cal K}(s\sigma t), which result in L(s)L(sσ)L_{\cal H}(s)\geq L_{\cal H}(s\sigma).

Furthermore, due to pr(𝒦)Lpr({\cal K})\subseteq L_{\cal M} and pr(𝒦)=Lpr({\cal K})=L_{{\cal H}}, we have LLL_{{\cal H}}\subseteq L_{\cal M}, that is, for any sΣs\in\Sigma^{\ast}, and σΣuc\sigma\in\Sigma_{uc}, L(sσ)L(sσ)L_{\cal H}(s\sigma)\leq L_{\cal M}(s\sigma).

In the light of Proposition 5, we have:

min{L(s),L(sσ)}L(sσ)\displaystyle\min\{L_{\cal H}(s),L_{\cal M}(s\sigma)\}\leq L_{{\cal H}}(s\sigma) (97)
\displaystyle\Leftrightarrow min{L(s),L(sσ)}=L(sσ)\displaystyle\min\{L_{\cal H}(s),L_{\cal M}(s\sigma)\}=L_{{\cal H}}(s\sigma) (98)
\displaystyle\Leftrightarrow LH(s)+L(sσ)|L(s)L(sσ)|2=L(sσ)\displaystyle\frac{L_{H}(s)+L_{\cal M}(s\sigma)-|L_{\cal H}(s)-L_{\cal M}(s\sigma)|}{2}=L_{{\cal H}}(s\sigma) (99)
\displaystyle\Leftrightarrow L(s)+L(sσ)2L(sσ)\displaystyle L_{\cal H}(s)+L_{\cal M}(s\sigma)-2L_{\cal H}(s\sigma) (100)
=|L(s)L(sσ)|\displaystyle=|L_{{\cal H}}(s)-L_{\cal M}(s\sigma)| (101)
\displaystyle\Leftrightarrow (L(s)+L(sσ)2L(sσ))2\displaystyle(L_{\cal H}(s)+L_{\cal M}(s\sigma)-2L_{\cal H}(s\sigma))^{2} (102)
=(L(s)L(sσ))2\displaystyle=(L_{{\cal H}}(s)-L_{\cal M}(s\sigma))^{2} (103)
\displaystyle\Leftrightarrow L(s)L(sσ)+L(sσ)2\displaystyle L_{\cal H}(s)L_{\cal M}(s\sigma)+L_{\cal H}(s\sigma)^{2} (104)
=L(sσ)L(s)+L(sσ)L(sσ).\displaystyle=L_{\cal H}(s\sigma)L_{\cal H}(s)+L_{\cal H}(s\sigma)L_{\cal M}(s\sigma). (105)

So, Inequality (96) is equivalent to Eq. (105), and therefore it suffices to check whether Eq. (105) holds for any sΣs\in\Sigma^{\ast}, and for each σΣuc\sigma\in\Sigma_{uc}, in order to check the controllability condition.

In fact, we have the following result, where |Q||Q_{\cal M}| and |Q||Q_{\cal H}| are the numbers of quantum basis states of {\cal M} and {\cal H}, respectively.

Theorem 8.

Suppose a QDES modeled as a quantum language LL_{\cal M} generated by a 1QFAC {\cal M}. For quantum language 𝒦{\cal K}, pr(𝒦)pr({\cal K}) is generated by another 1QFAC {\cal H} and pr(𝒦)Lpr({\cal K})\subseteq L_{\cal M}. Then the controllability condition Eq. (58) holds if and only if for any σΣuc\sigma\in\Sigma_{uc}, for any sΣs\in\Sigma^{\ast} with |s|18|Q|2(|Q|2+|Q|2)1|s|\leq 18|Q_{\cal H}|^{2}(|Q_{\cal H}|^{2}+|Q_{\cal M}|^{2})-1, Eq. (96) holds. Furthermore, there exists a polynomial-time algorithm running in time O(|Σ||Q|6(|Q|2+|Q|2)3)O(|\Sigma||Q_{\cal H}|^{6}(|Q_{\cal H}|^{2}+|Q_{\cal M}|^{2})^{3}) that determines whether the controllability condition Eq. (58) holds.

Proof.

According to Lemma 1, 1QFAC {\cal M} and {\cal H} can be simulated by two RBLM, say MM and HH respectively, such that sΣ\forall s\in\Sigma^{\ast},

L(s)=fM(s),L_{\mathcal{M}}(s)=f_{M}(s), (106)
L(s)=fH(s),L_{\mathcal{H}}(s)=f_{H}(s), (107)

and the numbers of states in MM and HH are |S|2|Q|2|S_{\cal M}|^{2}|Q_{\cal M}|^{2} and |S|2|Q|2|S_{\cal H}|^{2}|Q_{\cal H}|^{2}, respectively, where |S||S_{\cal M}| and |S||S_{\cal H}| represent respectively the numbers of classical states in {\cal M} and {\cal H}, |Q||Q_{\cal M}| and |Q||Q_{\cal H}| represent respectively the numbers of quantum states in {\cal M} and {\cal H}, and functions fMf_{M} and fHf_{H} are associated to MM and HH, respectively.

Similarly, by virtue of Proposition 2 and Lemma 1, for each σΣuc\sigma\in\Sigma_{uc}, there exist two RBLM MσM_{\sigma} and HσH_{\sigma} respectively satisfying that sΣ\forall s\in\Sigma^{\ast},

  1. 1.
    L(sσ)=fMσ(s),L_{\cal M}(s\sigma)=f_{M_{\sigma}}(s), (108)
  2. 2.
    L(sσ)=fHσ(s),L_{\cal H}(s\sigma)=f_{H_{\sigma}}(s), (109)

where the numbers of states in MσM_{\sigma} and HσH_{\sigma} are also |S|2|Q|2|S_{\cal M}|^{2}|Q_{\cal M}|^{2} and |S|2|Q|2|S_{\cal H}|^{2}|Q_{\cal H}|^{2}, respectively. Therefore, sΣ\forall s\in\Sigma^{\ast},

  1. 1.
    L(s)L(sσ)=fH(s)fMσ(s)=fHMσ(s),L_{\cal H}(s)L_{\cal M}(s\sigma)=f_{H}(s)f_{M_{\sigma}}(s)=f_{H\otimes M_{\sigma}}(s), (110)
  2. 2.
    L(sσ)2=fHσ(s)fHσ(s)=fHσHσ(s),L_{\cal H}(s\sigma)^{2}=f_{H_{\sigma}}(s)f_{H_{\sigma}}(s)=f_{H_{\sigma}\otimes H_{\sigma}}(s), (111)
  3. 3.
    L(sσ)L(s)=fHσ(s)fH(s)=fHσH(s),L_{\cal H}(s\sigma)L_{\cal H}(s)=f_{H_{\sigma}}(s)f_{H}(s)=f_{H_{\sigma}\otimes H}(s), (112)
  4. 4.
    L(sσ)L(sσ)=fHσ(s)fMσ(s)=fHσMσ(s),L_{\cal H}(s\sigma)L_{\cal M}(s\sigma)=f_{H_{\sigma}}(s)f_{M_{\sigma}}(s)=f_{H_{\sigma}\otimes M_{\sigma}}(s), (113)

where the second equalities of each equations above are due to Remark 1. Therefore, equation (105) is equivalent to

fHMσ(s)+fHσHσ(s)=fHσMσ(s)+fHσH(s)f_{H\otimes M_{\sigma}}(s)+f_{H_{\sigma}\otimes H_{\sigma}}(s)=f_{H_{\sigma}\otimes M_{\sigma}}(s)+f_{H_{\sigma}\otimes H}(s) (114)

for every sΣs\in\Sigma^{\ast}. Furthermore, by means of Remark 1, we have

f(HMσ)(HσHσ)(s)=f(HσMσ)(HσH)(s)f_{(H\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H_{\sigma})}(s)=f_{(H_{\sigma}\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H)}(s) (115)

for every sΣs\in\Sigma^{\ast}, where the state numbers of (HMσ)(HσHσ)(H\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H_{\sigma}) and (HσMσ)(HσH)(H_{\sigma}\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H) are the same as

|S|2|Q|2|S|2|Q|2+|S|4|Q|4\displaystyle|S_{\cal H}|^{2}|Q_{\cal H}|^{2}|S_{\cal M}|^{2}|Q_{\cal M}|^{2}+|S_{\cal H}|^{4}\ |Q_{\cal H}|^{4} (116)
=\displaystyle= |S|2|Q|2(|S|2|Q|2+|S|2|Q|2).\displaystyle|S_{\cal H}|^{2}|Q_{\cal H}|^{2}(|S_{\cal M}|^{2}|Q_{\cal M}|^{2}+|S_{\cal H}|^{2}|Q_{\cal H}|^{2}). (117)

By virtue of Proposition 1, the above Equation (115) holds for every sΣs\in\Sigma^{\ast} if and only if it holds for all sΣs\in\Sigma^{\ast} with |s|2|S|2|Q|2(|S|2|Q|2+|S|2|Q|2)|s|\leq 2|S_{\cal H}|^{2}|Q_{\cal H}|^{2}(|S_{\cal M}|^{2}|Q_{\cal M}|^{2}+|S_{\cal H}|^{2}|Q_{\cal H}|^{2}), and there exists a polynomial-time algorithm running in time O(|S|6|Q|6(|S|2|Q|2+|S|2|Q|2)3O(|S_{\cal H}|^{6}|Q_{\cal H}|^{6}(|S_{\cal M}|^{2}|Q_{\cal M}|^{2}+|S_{\cal H}|^{2}|Q_{\cal H}|^{2})^{3} to determine whether Equation (115) holds for every sΣs\in\Sigma^{\ast}. Here we present the algorithm in detail, but omit the analyses of correctness and complexity and the details are referred to [35, 36, 40, 41, 42, 43].

In the first step, given 1QFAC {\cal M} and {\cal H}, and for any σΣuc\sigma\in\Sigma_{uc}, we can directly construct two RBLM (HMσ)(HσHσ)(H\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H_{\sigma}) and (HσMσ)(HσH)(H_{\sigma}\otimes M_{\sigma})\oplus(H_{\sigma}\otimes H) as above, and for simplicity, we denote them respectively by

  • 1(σ)=(S1,π1,{M1(t)}tΣ,η1){\cal M}_{1}(\sigma)=(S_{1},\pi_{1},\{M_{1}(t)\}_{t\in\Sigma},\eta_{1}),

  • 2(σ)=(S2,π2,{M2(t)}tΣ,η2){\cal M}_{2}(\sigma)=(S_{2},\pi_{2},\{M_{2}(t)\}_{t\in\Sigma},\eta_{2}).

Recalling Definition 1 and Remark 1, we have

fi(σ)(w)=ηiMi(wm)Mi(wm1)Mi(w1)πi,f_{{\cal M}_{i}(\sigma)}(w)=\eta_{i}M_{i}(w_{m})M_{i}(w_{m-1})\ldots M_{i}(w_{1})\pi_{i}, (118)

i=1,2,i=1,2, their direct sum is

1(σ)2(σ)\displaystyle{\cal M}_{1}(\sigma)\oplus{\cal M}_{2}(\sigma) (119)
=\displaystyle= (S1S2,π1π2,{M1(t)M2(t)}tΣ,η1η2),\displaystyle(S_{1}\oplus S_{2},\pi_{1}\oplus\pi_{2},\{M_{1}(t)\oplus M_{2}(t)\}_{t\in\Sigma},\eta_{1}\oplus\eta_{2}), (120)

and then

f12(w)=f1(w)+f2(w)f_{\mathcal{M}_{1}\oplus\mathcal{M}_{2}}(w)=f_{\mathcal{M}_{1}}(w)+f_{\mathcal{M}_{2}}(w) (121)

for any wΣw\in\Sigma^{*}. For any w=w1w2wmΣw=w_{1}w_{2}\ldots w_{m}\in\Sigma^{\ast}, denote

Pi(σ)(w)=Mi(wm)Mi(wm1)Mi(w1)πi,\displaystyle P_{{\cal M}_{i}(\sigma)}(w)=M_{i}(w_{m})M_{i}(w_{m-1})\ldots M_{i}(w_{1})\pi_{i}, (122)

for i=1,2.i=1,2.

Now we present Algorithm 1 to check whether or not M1(σ)M_{1}(\sigma) and M2(σ)M_{2}(\sigma) are equivalent.

So, if for any σΣuc\sigma\in\Sigma_{uc}, Algorithm 1 returns M1(σ)M_{1}(\sigma) and M2(σ)M_{2}(\sigma) are equivalent, then the controllability condition holds; otherwise the controllability condition does not hold.

Algorithm 1 Algorithm for checking the equivalence between 1(σ){\cal M}_{1}(\sigma) and 2(σ){\cal M}_{2}(\sigma)
0:  RBLM 1(σ)=(S1,π1,{M1(t)}tΣ,η1){\cal M}_{1}(\sigma)=(S_{1},\pi_{1},\{M_{1}(t)\}_{t\in\Sigma},\eta_{1}) and 2(σ)=(S2,π2,{M2(t)}tΣ,η2){\cal M}_{2}(\sigma)=(S_{2},\pi_{2},\{M_{2}(t)\}_{t\in\Sigma},\eta_{2});
1:  Set V and N to be the empty set;
2:  queue \leftarrow node(ε\varepsilon); ε\varepsilon denotes empty string;
3:  while queue is not empty do
4:     begin take an element node(xx) from queue; xΣx\in\Sigma^{*};
5:     if P1(σ)2(σ)(x)span(V)P_{{\cal M}_{1}(\sigma)\oplus{\cal M}_{2}(\sigma)}(x)\notin span(\textbf{V}) then
6:        begin add all node(xtxt) for tΣt\in\Sigma to queue;
7:           add vector P1(σ)2(σ)(x)P_{{\cal M}_{1}(\sigma)\oplus{\cal M}_{2}(\sigma)}(x) to V;
8:           add node(xx) to N;
9:        end;
10:     end if;
11:     end;
12:  end while;
13:  if vV\forall\textbf{v}\in\textbf{V}, (η1η2)v=0(\eta_{1}\oplus-\eta_{2})\textbf{v}=0 then
14:     return(yes)
15:  else
16:     return an x0{x:node(x)N,(η1η2)P1(σ)2(σ)(x)0}x_{0}\in\{x:node(x)\in\textbf{N},(\eta_{1}\oplus-\eta_{2})P_{{\cal M}_{1}(\sigma)\oplus{\cal M}_{2}(\sigma)}(x)\neq 0\};
17:  end if;

6 Concluding Remarks

As a kind of important control systems, DES have been developed deeply [1]-[9] due to the potential of practical application, but state complexity is still a key problem in DES to be solved appropriately. In recent thirty years, quantum computing has been studied rapidly [11], and quantum control has attracted great interest [17]. So, initiating the study of QDES likely becomes a new subject of DES, and it is also motivated by two aspects: one is the simulation of DES in quantum systems by virtue of the principle of quantum computing; another is that QDES have advantages over classical DES for processing some problems in state complexity. This paper has been the first study for establishing QDES in light of QFA.

In this paper, we have established a basic framework of QDES, and the supervisory control of QDES has been studied. The main contributions are: (1) We have used 1QFAC to simulate QDES, and proved that MO-1QFA are not suitable for modeling QDES since we found MO-1QFA cannot recognize any prefix-closured language even with cut-point. (2) We have established a number of supervisory control theorems of QDES and proved the sufficient and necessary conditions of the existence of quantum supervisors. (3) We have constructed a number of examples to illustrate the supervisory control theorems and these examples have also showed the advantages of QDES over classical DES concerning state complexity. (4) We have given a polynomial-time algorithm to determine whether or not the controllability condition holds.

In subsequent study, we would like to consider controllability and observability problem of QDES under partial observation of events, and decentralized control of QDES with multi-supervisors under partial observation of events, as well as diagnosability of QDES. In particular, we would like to discover more practical examples to illustrate the advantages of QDES over classical DES in state complexity.

Acknowledgements

I thank Ligang Xiao for useful discussion and helpful construction concerning the examples of QFA recognizing prefix-closured languages. This work is supported in part by the National Natural Science Foundation of China (No. 61876195).

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