A selection of published articles. Each card includes a link to the PDF file to download the article.
Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
Fabrizio Fagiolo, Nicolò Vescera•arXiv•2025Preprint
The paper proposes a variational algorithm for the Traveling Salesman Problem (TSP) that uses a compact permutation encoding to reduce qubit requirements and an optimize-freeze-reuse strategy: an Ansatz is optimized once on a training instance (via Simulated Annealing), then frozen and reused on new instances with only parameter re-optimization. This avoids repeated structural searches, making the method practical for NISQ devices.
A Variational Quantum Algorithm for the Permutation Flow Shop Scheduling Problem
Marco Baioletti, Fabrizio Fagiolo, Angelo Oddi, Riccardo Rasconi•GECCO '25•2025Workshop
A Variational Quantum Algorithm (VQA) for the Permutation Flow Shop Scheduling Problem (PFSSP), addressing the challenge of encoding its complex objective function and high qubit requirements. By adopting a variational approach, the method represents solutions with a reduced number of qubits and computes the objective function without explicitly constructing a Hamiltonian. Implemented and tested on a simulator, the algorithm shows promising potential for application on real quantum hardware.
Quantum Artificial Intelligence: Some Strategies and Perspectives
Marco Baioletti, Fabrizio Fagiolo, Corrado Loglisci, Vito Nicola Losavio, Angelo Oddi, Riccardo Rasconi, Pier Luigi Gentili•Artificial Intelligence•2025Journal
This work explores the potential of Quantum Artificial Intelligence (QAI), arising from the synergy between Artificial Intelligence (AI) and Quantum Computing (QC). It highlights how QC can provide novel materials and algorithms to enhance AI particularly in optimization and machine learning while AI can help address experimental challenges in QC. The paper discusses these reciprocal benefits and outlines future perspectives for the development of QAI in tackling complex global challenges.
Ansatz Optimization using Simulated Annealing in Variational Quantum Algorithms for the Traveling Salesman Problem
Fabrizio Fagiolo, Nicolò Vescera•International Conference on Quantum Artificial Intelligence•2025Conference
This work introduces a Variational Quantum Algorithm (VQA) for the Traveling Salesman Problem (TSP) that requires only O(log n) qubits. The Ansatz topology is not fixed but evolves through Simulated Annealing, which dynamically adds, removes, or rearranges rotation and entanglement gates to balance exploration and exploitation.