Abstract
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.

Abstract
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, simulation of biological systems, or quantum computation, inconsistency has to be addressed in a precise and controlled way. This paper generalises Belnap-Dunn four-valued logic, introducing paraconsistent transition systems (PTS), endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values, and their modal logic.

Abstract
Software is a critical factor in the reliability of computer systems. While the development of hardware is assisted by mature science and engineering disciplines, software science is still in its infancy. This situation is likely to worsen in the future with quantum computer systems. Actually, if quantum computing is quickly coming of age, with potential groundbreaking impacts on many different fields, such benefits come at a price: quantum programming is hard and finding new quantum algorithms is far from straightforward. Thus, the need for suitable formal techniques in quantum software development is even bigger than in classical computation. A lack of reliable approaches to quantum computer programming will put at risk the expected quantum advantage of the new hardware. This position paper argues for the need for a proper quantum software engineering discipline benefiting from precise foundations and calculi, capable of supporting algorithm development and analysis.

Abstract
The present Special Issue of the Journal of Logical and Algebraic Methods in Programming was planned as a tribute to Jose Manuel Esgalhado Valenca on the occasion of his Jubilation. A tribute to a professor, in the deepest sense of the word, a colleague and a friend, but above all to a long and inspiring academic journey that has so profoundly shaped the development of Informatics as a scientific area in Portugal. A scientific area that, as he taught us, needs to be understood broadly: not only as an independent research domain, but also as an educational pillar, a strategy for social and economic development, a foundation for a multifaceted professional career. This preface introduces some steps of such a journey. The Special Issue features a selection of scientific papers written by his collaborators, colleagues and friends, covering the different areas Jose Valenca helped to launch and consolidate in Portugal, namely computational logic, verification and mechanized reasoning, and information security. (c) 2022 Published by Elsevier Inc.

Abstract
Fuzzy programming languages, such as the Fuzzy Arden Syntax (FAS), are used to describe behaviours which evolve in a fuzzy way and thus cannot be characterized neither by a Boolean outcome nor by a probability distribution. This paper introduces a semantics for FAS, focusing on the weighted parallel interpretation of its conditional statement. The proposed construction is based on the notion of a fuzzy multirelation which associates with each state in a program a fuzzy set of weighted possible evolutions. The latter is parametric on a residuated lattice which models the underlying semantic 'truth space'. Finally, a family of dynamic logics, equally parametric on the residuated lattice, is introduced to reason about FAS programs.

Abstract
Several classes of quantum circuits have been shown to provide a quantum computational advantage under certain assumptions. The study of ever more restricted classes of quantum circuits capable of quantum advantage is motivated by possible simplifications in experimental demonstrations. In this paper we study the efficiency of measurement-based quantum computation with a completely flat temporal ordering of measurements. We propose new constructions for the deterministic computation of arbitrary Boolean functions, drawing on correlations present in multi-qubit Greenberger, Horne, and Zeilinger (GHZ) states. We characterize the necessary measurement complexity using the Clifford hierarchy, and also generally decrease the number of qubits needed with respect to previous constructions. In particular, we identify a family of Boolean functions for which deterministic evaluation using non-adaptive MBQC is possible, featuring quantum advantage in width and number of gates with respect to classical circuits.