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QCA Project Works I Edizione

QCA Project Works I Edizione

  1. Quantum Optimization in Auction Design (in collaboration with Accenture)

This study addresses complex combinatorial optimization problems in auction systems using quantum-enhanced algorithms. By modeling auctions as Quadratic Unconstrained Binary Optimization (QUBO) problems, the team applied the Quantum Approximate Optimization Algorithm (QAOA) and a Hybrid Quantum Genetic Algorithm (HQGA) to several real-world scenarios. Benchmarking showed strong performance improvements and demonstrated the potential of hybrid quantum-classical frameworks to tackle NP-hard problems in resource allocation and market design. The results provide a promising foundation for future commercial quantum applications in strategic decision-making.

  1. Max-K-CUT with Measurement-Based Quantum Computing (in collaboration with Leonardo)

This research applies Measurement-Based Quantum Computing (MBQC) to the Max-K-Cut problem, a fundamental NP-hard problem with applications in clustering, traffic optimization, and resource allocation. By comparing MBQC with traditional gate-based quantum models using QAOA, the study found that MBQC may provide better approximation ratios and lower computational depth in some cases. Using cluster states and adaptive measurement patterns, this approach also offers hardware advantages for photonic quantum systems.

  1. Route Optimization in Cultural Sites via QAOA (in collaboration with University of Salerno)

This project tackles the challenge of optimizing visitor routes within cultural sites, aiming to maximize the number of points of interest visited within a distance constraint. By transforming the problem into a QUBO and solving it using the Quantum Approximate Optimization Algorithm (QAOA), the system explores multiple possible solutions simultaneously and efficiently discards infeasible paths through penalized constraints. A case study set in ancient Pompeii showed the model’s ability to generate optimal or near-optimal routes using limited quantum resources. Future plans include scaling the system to larger graphs and developing APIs for integration into mobile cultural tour apps.

  1. Quantum Error Mitigation with Fuzzy Clustering (in collaboration with Quantware and University of Naples Federico II)

This project addresses one of the main challenges in NISQ-era quantum computing: measurement errors. Using Fuzzy C-Means Clustering, the team developed a software-based error mitigation technique to correct readout errors in quantum circuits, achieving significantly improved result accuracy even under increasing noise conditions. This cost-effective, hardware-independent approach enhances the reliability of quantum computation, and can be extended to larger circuits and real-world quantum tasks in the near future.

  1. Quantum Genetic Algorithms for Network Signal Setting Design (in collaboration with University of Salerno and Netcom Engineering)

This project investigates hybrid quantum-classical approaches to optimize urban traffic management within smart mobility systems. Focusing on the Network Signal Setting Design (NSSD) problem, it explores how evolutionary algorithms can be enhanced with quantum computing techniques using the Quantum Mating Operator (QMO). The aim is to optimize traffic light configurations to reduce travel time, energy consumption, and improve public transport flow. A quantum-enhanced version of NSGA-II was tested on simulated traffic models using both ideal and noisy quantum environments. Results demonstrated that the noisy quantum variant achieved superior performance (measured via hypervolume indicators) compared to classical counterparts. This confirms quantum potential even on near-term hardware (NISQ). Future directions include integrating these algorithms into comprehensive smart road systems, with multi-objective optimization encompassing route efficiency, emissions, noise, and safety.

  1. Quantum Machine Learning for Anomaly Detection in Telecom Networks (in collaboration with University of Naples Federico II)

This project explores the use of Quantum Machine Learning (QML) to improve predictive maintenance in telecommunications infrastructure by detecting anomalous events based on weather and sensor data. Using real-world datasets from anomaly detectors deployed across Italy, the team developed and compared classical neural networks (MLPs) and quantum models built with Variational Quantum Circuits (VQCs). Results showed comparable performance between classical and quantum models and paves the way for scalable quantum-enhanced monitoring in telecom systems.