Research area / 01

Optimization & Machine Learning

Quantum and quantum-inspired algorithms for constrained optimization, sampling, learning, and autonomous reasoning problems.

We investigate algorithmic approaches for hard optimization, learning and inference problems, with an emphasis on measurable performance, constraints, and scientific relevance.

Research includes quantum approximate optimization, annealing, sampling, quantum-assisted machine learning, and rigorous comparisons with classical methods.

Related output

Publications

Open paper

A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications

Ruomin Zhu, Abhishek Kumar Singh, Jérémie Laydevant, Fan O. Wu, Ari Kapelyan, Davide Venturelli, Kyle Jamieson, Peter L. McMahon arXiv preprint arXiv:2604.17109

arXiv Academy

Open paper

Iterative warm-start optimization with quantum imaginary time evolution

PC Lotshaw, T Morris, S Hadfield, R Bennink arXiv preprint arXiv:2604.26047

arXiv

Figure-derived thumbnail for Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits — 2026

Open paper

Selected highlight

Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits

Stuart Hadfield, Filip B. Maciejewski, Davide Venturelli arXiv preprint arXiv:2606.28234

arXiv

Open paper

Partitioned Iterative Quantum Scheduling of Satellites for Urgent Disaster Response: Case study of Wildfire

Lucas T. Braydwood, Taejin Park, Hirofumi Hashimoto, Zoe Gonzalez Izquierdo, Andrew Michaelis, Eleanor Rieffel, Shon Grabbe arXiv preprint arXiv:2606.12310

arXiv

Figure-derived thumbnail for Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems — 2026

Open paper

Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Andrea Grimaldi, Christian Duffee, Eleonora Raimondo, Edoardo Piccolo, Deborah Volpe, Filip B. Maciejewski, Mario Carpentieri, Massimo Chiappini, Pedram Khalili Amiri, Davide Venturelli, Giovanni Finocchio arXiv preprint arXiv:2605.07884

arXiv Academy

Figure-derived thumbnail for Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting — 2026

Open paper

Selected highlight

Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting

Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis, George Pennington, Sebastian Brandhofer, Stefan Woerner, Daniel J. Egger, Davide Venturelli arXiv preprint arXiv:2607.09368

arXiv Code repository Hardware: IBM

Open paper

Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting

Ata Akbari Asanjan, Filip Wudarski, Daniel O'Connor, Shaun Geaney, Elena Strbac, P. Aaron Lott, Davide Venturelli arXiv preprint arXiv:2601.00172

arXiv

Open paper

Setting angles in quantum approximate optimization at utility-scale

Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh, Conrad J. Haupt, Alberto Baiardi, Dimitrios Athanasakos, M. Emre Sahin, Oscar Wallis, George Pennington, Christian Arenz, Sebastian Brandhofer, Georgios Korpas, Ieva Čepaitė, J. A. Montañez-Barrera, Jakub Marecek, Davide Venturelli, Stephan Eidenbenz, David E. Bernal Neira, Daniel J. Egger arXiv preprint arXiv:2606.05311

arXiv Academy

Open paper

From quantum feature maps to quantum reservoir computing: perspectives and applications

Casper Gyurik, Filip Wudarski, Evan Philip, Antonio Sannia, Hossein Sadeghi, Oleksandr Kyriienko, Davide Venturelli, Antonio A. Gentile arXiv preprint arXiv:2510.01797

arXiv Academy Hardware: Pasqal

Open paper

Measurement-driven Quantum Approximate Optimization

T Stollenwerk, S Hadfield arXiv preprint arXiv:2512.21046

arXiv

Open paper

Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers

Shuvro Chowdhury, Navid Anjum Aadit, Andrea Grimaldi, Eleonora Raimondo, Atharva Raut, P. Aaron Lott, Johan H. Mentink, Marek M. Rams, Federico Ricci-Tersenghi, Massimo Chiappini, Luke S. Theogarajan, Tathagata Srimani, Giovanni Finocchio, Masoud Mohseni, Kerem Y. Camsari Nature Communications 16 (1), 9193

DOI arXiv Academy

Open paper

Quantum DPLL and Generalized Constraints in Iterative Quantum Algorithms

LT Brady, S Hadfield arXiv preprint arXiv:2509.02689

arXiv

Open paper

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Mert Esencan, Tarun Advaith Kumar, Ata Akbari Asanjan, P Aaron Lott, Masoud Mohseni, Can Unlu, Davide Venturelli, Alan Ho arXiv preprint arXiv:2407.00071

arXiv

2024

Open paper

Hybrid quantum-classical reservoir computing for simulating chaotic systems

Filip Wudarski, Daniel O'Connor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, Davide Venturelli arXiv preprint arXiv:2311.14105

arXiv Hardware: OQC

Figure-derived thumbnail for Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense Ising optimization problems — 2023

Open paper

Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense Ising optimization problems

Filip B. Maciejewski, Stuart Hadfield, Benjamin Hall, Mark Hodson, Maxime Dupont, Bram Evert, James Sud, M. Sohaib Alam, Zhihui Wang, Stephen Jeffrey, Bhuvanesh Sundar, P. Aaron Lott, Shon Grabbe, Eleanor G. Rieffel, Matthew J. Reagor, Davide Venturelli arXiv preprint arXiv:2308.12423

arXiv Academy Hardware: Rigetti

Open paper

Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor

Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley, Kevin J. Satzinger, Frank Arute, Kunal Arya, Juan Atalaya, Joseph C. Bardin, Rami Barends, Sergio Boixo, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett, Nicholas Bushnell, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Sean Demura, Andrew Dunsworth, Daniel Eppens, Austin Fowler, Brooks Foxen, Craig Gidney, Marissa Giustina, Rob Graff, Steve Habegger, Alan Ho, Sabrina Hong, Trent Huang, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody Jones, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Seon Kim, Paul V. Klimov, Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Pavel Laptev, Mike Lindmark, Martin Leib, Orion Martin, John M. Martinis, Jarrod R. McClean, Matt McEwen, Anthony Megrant, Xiao Mi, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Charles Neill, Florian Neukart, Murphy Yuezhen Niu, Thomas E. O'Brien, Bryan O'Gorman, Eric Ostby, Andre Petukhov, Harald Putterman, Chris Quintana, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Andrea Skolik, Vadim Smelyanskiy, Doug Strain, Michael Streif, Marco Szalay, Amit Vainsencher, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Leo Zhou, Hartmut Neven, Dave Bacon, Erik Lucero, Edward Farhi, Ryan Babbush Nature Physics 17, 332-336

DOI arXiv Hardware: Google