Roberto Bondesan

Dr. Roberto Bondesan is a Senior Lecturer in Quantum Computing at the Department of Computing, Faculty of Engineering, Imperial College London. He holds a PhD in theoretical physics from UPMC Paris and has conducted research at the Universities of Cologne and Oxford on topological phases of quantum matter, focusing on materials for quantum computing and electronics. He later led machine learning research at Qualcomm AI, developing quantum neural networks and optimizing chip design using Bayesian optimization, reinforcement learning, and graph neural networks. His current research integrates quantum computing and machine learning to address computational challenges in optimization and quantum system simulation. He is affiliated with Imperial X and actively supervises PhD students through departmental scholarships.

His research interests include quantum algorithms for combinatorial optimization, quantum error correction, and machine learning-driven quantum system simulation. Notable projects involve applying neural networks to quantum error correction and leveraging ML techniques for efficient quantum Gibbs sampling of complex systems like the Fermi-Hubbard model.

• Education:
  • PhD in Theoretical Physics, UPMC Paris

His publications span quantum computing applications, including quantum machine learning frameworks, optimization algorithms, and lattice gauge theories modeled via generative flow networks. He has contributed to advancing quantum advantage assessment in Gaussian processes and probabilistic numeric convolutional neural networks. Current work emphasizes bridging quantum algorithms with practical engineering solutions.

• Awards: No awards explicitly listed in provided texts.

He advises students through Imperial College's scholarships and maintains collaborations with industry (e.g., Qualcomm) and academic networks (Imperial X). His lab focuses on translating theoretical quantum computing breakthroughs into real-world applications.