M.Sc. Salvatore Zammuto

Doctoral Candidate

Research Interests

• High-Performance Computing / Quantum Computing (HPCQC)
• Quantum Optimization and Machine Learning
• Hybrid Quantum-Classical Algorithms
• Quantum Simulation
• Quantum Error Correction

My main research goal is to investigate hybrid quantum-classical algorithms to accelerate machine learning-based quantum simulations in the HPCQC context. At the moment, my primary focus is on the parallelization of the ansatz expansion approach in post-variational algorithmic constructions. Alongside that, I also work on hardware-accelerated, reinforcement learning-based approaches for Minimum-Weight Perfect Matching decoding in surface codes.

Projects

• Munich Quantum Valley (MQV)

Teaching

• SS2025: Seminar on Integration of Quantum Computing Accelerators with HPC Systems

Publications

• Chella, Antonio, Salvatore Gaglio, Maria Mannone, Giovanni Pilato, Valeria Seidita, Filippo Vella, and Salvatore Zammuto. “Quantum Planning for Swarm Robotics” Robotics and Autonomous Systems 161 (March 1, 2023): 104362.
• Chella, Antonio, Salvatore Gaglio, Giovanni Pilato, Filippo Vella, and Salvatore Zammuto. 2022. "A Quantum Planner for Robot Motion" Mathematics 10, no. 14: 2475.