Davide Venturelli leads RIACS quantum computing activities and research programs spanning quantum optimization, emerging computing architectures, and scientific applications.
People / current team
Davide Venturelli
Associate Director, Quantum Technologies. Quantum optimization, emerging computing systems, and research programs.
Scholarly output
Publications
A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications
arXiv preprint arXiv:2604.17109
arXiv Academy
Assessing and advancing the potential of quantum computing: A NASA case study
Future Generation Computer Systems, 160, 598-618
DOI Academy
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization
arXiv preprint arXiv:2407.00071
Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense Ising optimization problems
arXiv preprint arXiv:2308.12423
From quantum feature maps to quantum reservoir computing: perspectives and applications
arXiv preprint arXiv:2510.01797
How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities
Publication venue not recorded
Hybrid quantum-classical reservoir computing for simulating chaotic systems
arXiv preprint arXiv:2311.14105
arXiv Hardware: OQC
Selected highlight
Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits
arXiv preprint arXiv:2606.28234
Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems
arXiv preprint arXiv:2605.07884
arXiv Academy
Quantum annealing implementation of job-shop scheduling
arXiv preprint arXiv:1506.08479
arXiv Hardware: D-Wave
Selected highlight
Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
arXiv preprint arXiv:2607.09368
Quantum Enhanced Greedy Solver for Optimization Problems
arXiv preprint arXiv:2303.05509
arXiv Hardware: Rigetti
Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting
arXiv preprint arXiv:2601.00172
Setting angles in quantum approximate optimization at utility-scale
arXiv preprint arXiv:2606.05311
arXiv Academy