Program / completed

AFRL QIS

The AFRL–NYSTEC–USRA Quantum Information Science program supported quantum algorithms, annealing, software, and Feynman Quantum Academy research.

This historical Quantum Information Science program connected USRA, the Air Force Research Laboratory, and NYSTEC. Publications acknowledge support through contract FA8750-19-3-6101, including research conducted through the Feynman Quantum Academy.

The program supported quantum optimization, quantum software, and investigation of quantum annealing and noisy processors. The dates above describe the linked publication record, rather than an asserted contract end date.

Related output / newest first

Program publications

7 linked records
Staff involved

Davide VenturelliFilip WudarskiZhihui WangDavid Bernal NeiraEugeniu PlamadealaKyle BoothMasoud MohseniSergey Knysh

Figure-derived thumbnail for Mixer-Phaser Ansätze for Quantum Optimization with Hard Constraints
2021
Open paper

Mixer-Phaser Ansätze for Quantum Optimization with Hard Constraints

Ryan LaRose, Eleanor Rieffel, Davide Venturelli Quantum Machine Intelligence

Figure-derived thumbnail for Physics-Inspired Heuristics for Soft MIMO Detection in 5G New Radio and Beyond
2021
Open paper

Physics-Inspired Heuristics for Soft MIMO Detection in 5G New Radio and Beyond

Minsung Kim, Salvatore Mandrà, Davide Venturelli, Kyle Jamieson Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 42-55

Figure-derived thumbnail for Quantum algorithms with local particle number conservation: noise effects and error correction
2020
Open paper

Quantum algorithms with local particle number conservation: noise effects and error correction

Michael Streif, Martin Leib, Filip Wudarski, Eleanor Rieffel, Zhihui Wang Physical Review A, 103(4), 042412

Figure-derived thumbnail for Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems
2020
Open paper

Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems

Minsung Kim, Davide Venturelli, Kyle Jamieson Proceedings of the 19th ACM Workshop on Hot Topics in Networks, 110-116

Figure-derived thumbnail for Quantum annealing speedup of embedded problems via suppression of Griffiths singularities
2020
Open paper

Quantum annealing speedup of embedded problems via suppression of Griffiths singularities

Sergey Knysh, Eugeniu Plamadeala, Davide Venturelli Physical Review B

2020
Open paper

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, Masoud Mohseni arXiv preprint arXiv:2003.02989

Figure-derived thumbnail for Integer programming techniques for minor-embedding in quantum annealers
2019
Open paper

Integer programming techniques for minor-embedding in quantum annealers

David E Bernal, Kyle EC Booth, Raouf Dridi, Hedayat Alghassi, Sridhar Tayur, Davide Venturelli Lecture Notes in Computer Science, 112-129