arXiv subjects

Riccardo Romanello

Publications and source records attributed to Riccardo Romanello.

5 recordsLinked to original sources

arXiv2026

A Lumpability-Driven Taxonomy of Strong and Weak Stochastic Bisimilarities with Their Congruence Properties

We study the relationships among the stochastic bisimulation-style equivalences over PEPA - Performance Evaluation Process Algebra definable according to the well known notions of lumpability for the continuous-time Markov chains (CTMCs) underlying process terms. Lumpability is a central tool in the analysis of a CTMC, because it results in aggregations of the state space enjoying properties that are useful for efficiently computing the state probability distribution of the original chain. At the level of process terms, various stochastic bisimilarities accounting for activity types and cumulative rates can be defined over PEPA, which induce different kinds of lumping. Since the formalisations of some of them are scattered across the literature, where they appear under different, and sometimes clashing, names, we collect them within a single, uniform framework, renaming each bisimilarity in a consistent way after the kind of lumping it induces. We present strong and weak variants of what we call ordinary, exact, and strict bisimilarities and show that they respectively induce ordinary, exact, and strict lumpings. We then organise the six bisimilarities into a taxonomy establishing all and only the inclusions holding among them. We also analyse how the taxonomy changes in three special cases: process terms whose underlying CTMCs are time reversible, process terms with no activities of unobservable types, and process terms with no recursion. The paper concludes by investigating the compositionality properties of the six bisimilarities. Some of them are not congruences with respect to the prefix and/or choice operators of PEPA. In that case we single out either a set of process terms over which congruence with respect to those operators is achieved, or the coarsest congruence with respect to them that is contained in the considered bisimilarity.

cs.LO↗

arXiv2026

AlphaCNOT: Learning CNOT Minimization with Model-Based Planning

Quantum circuit optimization is a central task in Quantum Computing, as current Noisy Intermediate Scale Quantum devices suffer from error propagation that often scales with the number of operations. Among quantum operations, the CNOT gate is of fundamental importance, being the only 2-qubit gate in the universal Clifford+T set. The problem of CNOT gates minimization has been addressed by heuristic algorithms such as the well-known Patel-Markov-Hayes (PMH) for linear reversible synthesis (i.e., CNOT minimization with no topological constraints), and more recently by Reinforcement Learning (RL) based strategies in the more complex case of topology-aware synthesis, where each CNOT can act on a subset of all qubits pairs. In this work we introduce AlphaCNOT, a RL framework based on Monte Carlo Tree Search (MCTS) that address effectively the CNOT minimization problem by modeling it as a planning problem. In contrast to other RL- based solution, our method is model-based, i.e. it can leverage lookahead search to evaluate future trajectories, thus finding more efficient sequences of CNOTs. Our method achieves a reduction of up to 32% in CNOT gate count compared to PMH baseline on linear reversible synthesis, while in the constraint version we report a consistent gate count reduction on a variety of topologies with up to 8 qubits, with respect to state-of-the-art RL-based solutions. Our results suggest the combination of RL with search-based strategies can be applied to different circuit optimization tasks, such as Clifford minimization, thus fostering the transition toward the "quantum utility" era.

cs.AI↗

arXiv2025

CNOT Minimal Circuit Synthesis: A Reinforcement Learning Approach

CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exclusively from CNOT gates. Given their widespread use, it is imperative to minimise the number of CNOT gates employed. This problem, known as CNOT minimisation, remains an open challenge, with its computational complexity yet to be fully characterised. In this work, we introduce a novel reinforcement learning approach to address this task. Instead of training multiple reinforcement learning agents for different circuit sizes, we use a single agent up to a fixed size $m$. Matrices of sizes different from m are preprocessed using either embedding or Gaussian striping. To assess the efficacy of our approach, we trained an agent with m = 8, and evaluated it on matrices of size n that range from 3 to 15. The results we obtained show that our method overperforms the state-of-the-art algorithm as the value of n increases.

cs.AI↗

arXiv2025

Exact Persistent Stochastic Non-Interference

Persistent Stochastic Non-Interference (PSNI) was introduced to capture a quantitative security property in stochastic process algebras, ensuring that a high-level process does not influence the observable behaviour of a low-level component, as formalised via lumpable bisimulation. In this work, we revisit PSNI from a performance-oriented perspective and propose a new characterisation based on a refined behavioural relation. We introduce \emph{weak-exact equivalence}, which extends exact equivalence with a relaxed treatment of internal ($\tau$) actions, enabling precise control over quantitative observables while accommodating unobservable transitions. Based on this, we define \emph{Exact PSNI} (EPSNI), a variant of PSNI characterised via weak-exact equivalence. We show that EPSNI admits the same bisimulation-based and unwinding-style characterisations as PSNI, and enjoys analogous compositionality properties. These results confirm weak-exact equivalence as a robust foundation for reasoning about non-interference in stochastic systems.

cs.PF↗

arXiv2022

Neural Networks Reduction via Lumping

The increasing size of recently proposed Neural Networks makes it hard to implement them on embedded devices, where memory, battery and computational power are a non-trivial bottleneck. For this reason during the last years network compression literature has been thriving and a large number of solutions has been been published to reduce both the number of operations and the parameters involved with the models. Unfortunately, most of these reducing techniques are actually heuristic methods and usually require at least one re-training step to recover the accuracy. The need of procedures for model reduction is well-known also in the fields of Verification and Performances Evaluation, where large efforts have been devoted to the definition of quotients that preserve the observable underlying behaviour. In this paper we try to bridge the gap between the most popular and very effective network reduction strategies and formal notions, such as lumpability, introduced for verification and evaluation of Markov Chains. Elaborating on lumpability we propose a pruning approach that reduces the number of neurons in a network without using any data or fine-tuning, while completely preserving the exact behaviour. Relaxing the constraints on the exact definition of the quotienting method we can give a formal explanation of some of the most common reduction techniques.

cs.LG↗