M.Sc. Salvatore Zammuto

Doctoral Candidate

Research Interests

• High-Performance Computing / Quantum Computing (HPCQC)
• Hybrid Quantum-Classical Algorithms
• Accelerated Tensor-Network Kernels
• Quantum Simulation

My main research goal is to investigate hybrid quantum-classical algorithms and their execution infrastructure within HPCQC systems. At the moment, my primary focus is on hybrid tensor network-quantum computational kernel libraries for quantum simulation. In parallel, I also work on HPCQC scheduling, and hardware-aware compilation of hybrid algorithms.

Projects

• Quantum Algorithm Engineering (COMET-QAE)
• Munich Quantum Valley (MQV) - Quantum Development Environment, System Software & Integration (Q-DESSI)

Teaching

• SS2026: Seminar on Integration of Quantum Computing Accelerators with HPC Systems
• SS2025: Seminar on Integration of Quantum Computing Accelerators with HPC Systems

Publications

• S. Zammuto and M. Schulz, "An HPCQC-tailored Approach for Scalable Measurement of Physical Observables," ISC High Performance 2026 Research Paper Proceedings (41st International Conference), Hamburg, Germany, 2026, pp. 1-12, doi: 10.23919/ISC.2026.11520505.
• Chella, A., Gaglio S., Mannone M., Pilato G., Seidita V., Vella F., and Zammuto S. . “Quantum Planning for Swarm Robotics” Robotics and Autonomous Systems 161 (March 1, 2023): 104362.
• Chella, A., Gaglio S., Pilato G., Vella F., and Zammuto S.. 2022. "A Quantum Planner for Robot Motion" Mathematics 10, no. 14: 2475.