QCA Project Works II Edizione
- Quantum Clock Synchronization via SeQUeNCe Framework (in collaboration with Leonardo)
This project simulates a Quantum Clock Synchronization (QCS) protocol using the SeQUeNCe quantum network simulation framework. Based on prior entanglement and Ramsey interferometry, the protocol allows two parties (Alice and Bob) to synchronize their clocks without physical transport or classical signal delays. Implemented over a quantum network topology, the simulation involved managing quantum memories, applying quantum rotations, and scheduling events via SeQUeNCe. Results validated key steps of the QCS protocol and highlighted the framework’s potential for exploring scalable quantum networking use cases.
- Quantum Machine Learning for Medical Image Classification (in collaboration with University of Naples Federico II)
This project applies Quantum Machine Learning (QML) to the classification of medical imaging data, evaluating its ability to detect brain tumors from preprocessed inputs. A hybrid approach combines classical feature extraction using InceptionV3 with a Variational Quantum Classifier using angle encoding and data reuploading to capture non-linear features. The project demonstrates the viability of QML for high-stakes applications in healthcare and sets a precedent for scaling these methods with future quantum hardware improvements.
- Quantum Genetic Algorithms for Dynamic Lane Management (in collaboration with University of Salerno and Netcom Engineering)
This project explores the use of a Quantum Mating Operator (QMO) based genetic algorithm for optimizing traffic safety through dynamic lane management in high-risk confluence zones without emergency lanes. The solution integrates quantum-inspired crossover and mutation into a single operator to optimize the activation time of lane drop strategies. Individual solutions represent lane drop activation times and are evaluated based on their fitness in minimizing traffic delays. The algorithm was tested in simulation using VISSIM traffic modeling software, demonstrating improved performance after multiple generations.
- A Comparative Analysis of Quantum Error Mitigation Approaches (in collaboration with University of Naples Federico II)
This study compares Fuzzy C-Means (FCM) clustering with IBM’s standard error mitigation techniques in mitigating readout errors on Noisy Intermediate-Scale Quantum (NISQ) devices. The group evaluated both methods across various noise levels and circuit complexities using benchmark quantum algorithms such as Deutsch-Jozsa and Grover’s algorithm. Results showed that while IBM’s matrix-based correction performs slightly better under low noise and simple circuits, FCM offers superior scalability and accuracy as circuit complexity and noise levels increase.
- A Study on the Effect of Quantum Noise on the HQGA for Optimization Problems (in collaboration with University of Naples Federico II)
This project investigates the impact of quantum noise on the performance of the Hybrid Quantum Genetic Algorithm (HQGA) across a suite of combinatorial and continuous optimization problems, such as Max-Cut, Knapsack, and the Eggholder function. Simulations were run across multiple noisy quantum processing units (QPUs), with noise models including relaxation (T1), dephasing (T2), and readout errors. Interestingly, results showed that quantum noise, often seen as a limitation, can act as a constructive source of stochasticity, improving exploratory behavior in the algorithm. The HQGA maintained or even improved performance under certain noise conditions, suggesting that well-handled noise may enhance evolutionary search mechanisms in hybrid quantum-classical systems.