Model checking quantum Markov chains

Although the security of quantum cryptography is provable based on the principles of quantum mechanics, it can be compromised by the flaws in the design of quantum protocols and the noise in their physical implementations. So, it is indispensable to develop techniques of verifying and debugging quantum cryptographic systems. Model-checking has proved to be effective in the verification of classical cryptographic protocols, but an essential difficulty arises when it is applied to quantum systems: the state space of a quantum system is always a continuum even when its dimension is finite. To overcome this difficulty, we introduce a novel notion of quantum Markov chain, specially suited to model quantum cryptographic protocols, in which quantum effects are entirely encoded into super-operators labelling transitions, leaving the location information (nodes) being classical. Then we define a quantum extension of probabilistic computation tree logic (PCTL) and develop a model-checking algorithm for quantum Markov chains.Comment: Journal versio

Model checking ω-regular properties for quantum Markov chains

© Yuan Feng, Ernst Moritz Hahn, Andrea Turrini, and Shenggang Ying. Quantum Markov chains are an extension of classical Markov chains which are labelled with super-operators rather than probabilities. They allow to faithfully represent quantum programs and quantum protocols. In this paper, we investigate model checking !-regular properties, a very general class of properties (including, e.g., LTL properties) of interest, against this model. For classical Markov chains, such properties are usually checked by building the product of the model with a language automaton. Subsequent analysis is then performed on this product. When doing so, one takes into account its graph structure, and for instance performs different analyses per bottom strongly connected component (BSCC). Unfortunately, for quantum Markov chains such an approach does not work directly, because super-operators behave differently from probabilities. To overcome this problem, we transform the product quantum Markov chain into a single super-operator, which induces a decomposition of the state space (the tensor product of classical state space and the quantum one) into a family of BSCC subspaces. Interestingly, we show that this BSCC decomposition provides a solution to the issue of model checking ω-regular properties for quantum Markov chains

Decomposition of quantum Markov chains and its applications

© 2018 Elsevier Inc. Markov chains have been widely employed as a fundamental model in the studies of probabilistic and stochastic communicating and concurrent systems. It is well-understood that decomposition techniques play a key role in reachability analysis and model-checking of Markov chains. (Discrete-time) quantum Markov chains have been introduced as a model of quantum communicating systems [1] and also a semantic model of quantum programs [2]. The BSCC (Bottom Strongly Connected Component) and stationary coherence decompositions of quantum Markov chains were introduced in [3–5]. This paper presents a new decomposition technique, namely periodic decomposition, for quantum Markov chains. We further establish a limit theorem for them. As an application, an algorithm to find a maximum dimensional noiseless subsystem of a quantum communicating system is given using decomposition techniques of quantum Markov chains

Quantum Markov chains: Description of hybrid systems, decidability of equivalence, and model checking linear-time properties

© 2015 Elsevier Inc. In this paper, we study a model of quantum Markov chains that is a quantum analogue of Markov chains and is obtained by replacing probabilities in transition matrices with quantum operations. We show that this model is very suited to describe hybrid systems that consist of a quantum component and a classical one. Indeed, hybrid systems are often encountered in quantum information processing. Thus, we further propose a model called hybrid quantum automata (HQA) that can be used to describe the hybrid systems receiving inputs (actions) from the outer world. We show the language equivalence problem of HQA is decidable in polynomial time. Furthermore, we apply this result to the trace equivalence problem of quantum Markov chains, and thus it is also decidable in polynomial time. Finally, we discuss model checking linear-time properties of quantum Markov chains, and show the quantitative analysis of regular safety properties can be addressed successfully

Model Checking Quantum Continuous-Time Markov Chains

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture; further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method

Model-Checking Branching-Time Properties of Stateless Probabilistic Pushdown Systems and Its Quantum Extension

In this work, we first resolve a question in the probabilistic verification of infinite-state systems (specifically, the probabilistic pushdown systems). We show that model checking stateless probabilistic pushdown systems (pBPA) against probabilistic computational tree logic (PCTL) is generally undecidable. We define the quantum analogues of the probabilistic pushdown systems and Markov chains and investigate whether it is necessary to define a quantum analogue of probabilistic computational tree logic to describe the branching-time properties of the quantum Markov chain. We also study its model-checking problem and show that the model-checking of stateless quantum pushdown systems (qBPA) against probabilistic computational tree logic (PCTL) is generally undecidable, too. The immediate corollaries of the above results are summarized in the work.Comment: Obvious typos corrected in new version; this work is a quantum extension of arXiv:1405.4806, [v13]; 30 pages; comments are welcom

An Algebraic Method to Fidelity-based Model Checking over Quantum Markov Chains

Fidelity is one of the most widely used quantities in quantum information that measure the distance of quantum states through a noisy channel. In this paper, we introduce a quantum analogy of computation tree logic (CTL) called QCTL, which concerns fidelity instead of probability in probabilistic CTL, over quantum Markov chains (QMCs). Noisy channels are modelled by super-operators, which are specified by QCTL formulas; the initial quantum states are modelled by density operators, which are left parametric in the given QMC. The problem is to compute the minimumfidelity over all initial states for conservation. We achieve it by a reduction to quantifier elimination in the existential theory of the reals. The method is absolutely exact, so that QCTL formulas are proven to be decidable in exponential time. Finally, we implement the proposed method and demonstrate its effectiveness via a quantum IPv4 protocol

Quantum speedup for active learning agents

Can quantum mechanics help us in building intelligent robots and agents? One of the defining characteristics of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in any real-life situation is the size and complexity of the corresponding task environment. Owing to, e.g., a large space of possible strategies, learning is typically slow. Even for a moderate task environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here we show that quantum physics can help and provide a significant speed-up for active learning as a genuine problem of artificial intelligence. We introduce a large class of quantum learning agents for which we show a quadratic boost in their active learning efficiency over their classical analogues. This result will be particularly relevant for applications involving complex task environments.Comment: Minor updates, 14 pages, 3 figure

Search via Quantum Walk

We propose a new method for designing quantum search algorithms for finding a "marked" element in the state space of a classical Markov chain. The algorithm is based on a quantum walk \'a la Szegedy (2004) that is defined in terms of the Markov chain. The main new idea is to apply quantum phase estimation to the quantum walk in order to implement an approximate reflection operator. This operator is then used in an amplitude amplification scheme. As a result we considerably expand the scope of the previous approaches of Ambainis (2004) and Szegedy (2004). Our algorithm combines the benefits of these approaches in terms of being able to find marked elements, incurring the smaller cost of the two, and being applicable to a larger class of Markov chains. In addition, it is conceptually simple and avoids some technical difficulties in the previous analyses of several algorithms based on quantum walk.Comment: 21 pages. Various modifications and improvements, especially in Section