Ondřej Lengál

Yu-Fang Chen, Kai-Min Chung, Min-Hsiu Hsieh et al.

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a~support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the \emph{repeat-until-success} (RUS) algorithm and the weak-measurement-based version of Grover's search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover's search, we were able to handle the case of 100 qubits in $\sim$20 minutes.

Vojtěch Havlena, Lukáš Holík, Ondřej Lengál et al.

Matching regexes (regular expressions) is a common problem in many areas of computer science, with requirements on high speed and robust performance. Regexes with backreferences allow one to express certain patterns (even beyond regular) concisely, however, since the matching is usually done by backtracking, the matching speed can degrade to a degree that constitutes a service failure or a security threat. To facilitate high-speed matching of such regexes, we propose register set automata (RSAs), an extension of register automata where registers can contain sets of symbols (from a potentially infinite alphabet) and the following operations are supported: adding input values to registers, merging or clearing registers, and testing whether a register contains a value. We show that a large class of register automata can be transformed into deterministic RSAs, which can serve as a basis for fast matching of a family of regexes with single-letter capture groups and backreferences. We also give a derivative-based algorithm for transforming a large class of regexes with backreferences to register automata and show that the time complexity of matching is linear and quadratic to the length of the input for finite and infinite alphabets respectively. Our prototype implementation of a regex matcher shows that our approach can significantly improve the robustness of state-of-the-art regex matchers on regexes with backreferences. We also study the theoretical properties of the model and show that the emptiness problem for RSAs is decidable and complete for the $\mathbf{F}_ω$ class and that RSAs are incomparable in expressive power to other popular automata models over data words.

Wei-Lun Tsai, Yu-Fang Chen, Ondřej Lengál

Hoare-style verification provides a principled foundation for reasoning about the correctness of quantum programs, but existing approaches do not allow fully automatic verification. While automata-based verification scales well when specifications are given directly as automata, prior frameworks incur exponential blow-up when translating high-level set-based assertions into automata, which severely limits practicality. We introduce an extended set-based specification language and a specification-to-automata translation algorithm whose complexity is linear in the number of qubits, enabled by controlled automaton construction and qubit reordering. The resulting compact automata enable fully automatic Hoare-style verification of fixed-qubit quantum programs at previously infeasible scales, while substantially improving expressiveness without compromising efficiency.

David Chocholatý, Vojtěch Havlena, Lukáš Holík et al.

We generalize an efficient automata-based approach to string constraint solving, the stabilization-based method behind the solver Z3-Noodler, to support relational constraints represented by finite-state transducers (useful, for example, for modeling replaceAll constraints). We focus on an efficient treatment of length constraints by reducing the need for expensive concatenation elimination, which is a major bottleneck in automata-based string solving. We also propose powerful heuristics that significantly improve performance in practice. Implemented on top of Z3-Noodler, our method vastly outperforms existing solvers on benchmarks with relational constraints. It solves more instances and runs orders of magnitude faster.

Ondrej Alexaj, Vojtěch Havlena, Lukáš Holík et al.

We present Kofola, an efficient tool for complementation and inclusion checking of Büchi automata, two central tasks in automata-theoretic verification with applications in model checking, monitoring, and theorem proving. Kofola implements a state-of-the-art modular complementation framework that decomposes the input automaton into strongly connected components and applies to each component a complementation algorithm tailored to its structural properties. Building on this modular construction, Kofola also provides modular inclusion checking with new heuristics. A key ingredient is a new on-the-fly emptiness-checking algorithm for the simple generalized Rabin pair condition produced by our complementation, allowing the search to terminate as soon as the explored state space suffices. Empirical evaluation shows that Kofola is highly competitive with state-of-the-art complementation and inclusion-checking tools: it is the most robust tool in our evaluation and often outperforms competitors by several orders of magnitude on benchmarks from practical applications.

Yu-Fang Chen, Chiao Hsieh, Ondřej Lengál et al.

We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning a regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.