Research
Research
Quantum machine learning (QML) is an emerging field that combines the principles of quantum computing with classical machine learning techniques, with the aim of improving processing and analysis of complex data. Despite the initial hype, QML models currently underperform compared to most, if not all, classical counterparts.
My research focuses on understanding whether the challenges in QML come from intrinsic limitations, from the need for better optimization techniques, or if we are simply approaching it the wrong way.
As quantum computing progresses toward the era of fault-tolerant quantum systems, deep learning is emerging as a powerful tool to accelerate this transition. Deep learning techniques are being explored to address key challenges in quantum computing, such as circuit synthesis, compilation, and error correction. These methods can optimize quantum operations, reduce error rates, and improve overall system performance.
I am interested in exploring how these tools can make today’s quantum devices more efficient and bring us closer to reliable, large-scale quantum computing.
M. Incudini, D. Lizzio Bosco, F. Martini, M. Grossi, G. Serra, and A. Di Pierro, Automatic and effective discovery of quantum kernels, IEEE Transactions on Emerging Topics in Computational Intelligence (2024) [link]
D. Lizzio Bosco, R. Romanello, G. Serra, and C. Piazza, Softer is Better: Tweaking Quantum Dropout to Enhance Quantum Neural Network Trainability, International Conference on Quantum Communications, Networking, and Computing (QCNC 2025) [link]
D. Lizzio Bosco, R. Romanello, and G. Serra, Periodic Unitary Encoding for Quantum Anomaly Detection of Temporal Series, 1st International Conference on Quantum Software (IQSOFT 2025)
F. Martini, D. Lizzio Bosco, C. Barbanera, et al., Securities transaction settlement optimization on superconducting quantum devices, [arXiv: 2501.08794]
D. Lizzio Bosco, B. Portelli, and G. Serra, Integrated encoding and quantization to enhance quanvolutional neural networks, [arXiv: 2410.05777]
D. Spina, K. Roitero, et al., Report on the Hands-On PhD Course on Responsible AI from the Lens of an Information Access Researcher, SIGIR Forum [link]