Unitary Foundation believes that research is important for quantum open source. We focus not only on our own research interests, but also building tools to enable others, like those in our microgrant program. Check out some of the many research projects we’ve been involved in or supported below!
Unitary Labs research with Mitiq, QuTiP and long-term projects
The Unitary Foundation research GitHub repo has source code for a selection of our research projects.
N. Lambert, E. Giguère, P. Menczel, B. Li, P. Hopf, G. Suárez, M. Gali, J. Lishman, R. Gadhvi, R. Agarwal, A. Galicia, N. Shammah, P. Nation, J. R. Johansson, S. Ahmed, S. Cross, A. Pitchford, F. Nori, QuTiP 5: The Quantum Toolbox in Python. arXiv preprint (2024), [2412.04705]
P. Lougovski, O. Parekh, J. Broz, M. Byrd, J.C. Chapman, Y. Chembo, W.A. de Jong, E. Figueroa, T.S. Humble, J. Larson, G. Quiroz, G. Ravi, N. Shammah, K.M. Svore, W. Wu, W.J. Zeng, Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking, January 7, 2024, United States, osti.gov/biblio/2001045, DOI:10.2172/2001045
A. Mari, V. Russo, Quantum error mitigation by layerwise Richardson extrapolation, arXiv preprint (2024), [2402.04000]
T. G.-Tiron, Y. Hindy, R. LaRose, A. Mari, and W. J. ZengDigital zero noise extrapolation for quantum error mitigation, 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), 306-316, (2021), [2005.10921]
A. Mari, N. Shammah, and W. J. Zeng, Extending quantum probabilistic error cancellation by noise scaling, Phys. Rev. A, 104, 052607, (2021), [104.052607]
A. Tambde, A Programming Language For Quantum Oracle Construction, arXiv, (2021), [2110.12487]
U. Azad and A. Sinha, qLEET: Visualizing Loss Landscapes, Expressibility, Entangling Power and Training Trajectories for Parameterized Quantum Circuits, Quantum Inf Process 22, 256 (2023), [2205.02095], doi:10.1007/s11128-023-03998-z
Z. C. Seskiret al, Quantum Games and Interactive Tools for Quantum Technologies Outreach and Education, Optical Engineering, Vol. 61, Issue 8, 081809, (2022), [2202.07756], doi:10.1117/1.OE.61.8.081809
K. Jankiewicz, P. Migdal, and P. Grabarz, Virtual Lab by Quantum Flytrap: Interactive simulation of quantum mechanics, Association for Computing Machinery, Article 175, 1–4, (2021), doi:10.1145/3491101.3519885
V. Russo, toqito — Theory of quantum information toolkit: A Python package for studying quantum information, Journal of Open Source Software, 6(61), 3082, (2021), doi:10.21105/joss.03082
O. Lockwood, Optimizing Quantum Variational Circuits with Deep Reinforcement Learning, arXiv, (2022), [2109.03188]
Martin Larocca, Diego Wisniacki, K-GRAPE: A Krylov Subspace approach for the efficient control of quantum many-body dynamics, Phys. Rev. A 103, 023107 (2021), [2010.03598], doi:10.1103/PhysRevA.103.023107
S. Diadamo, J. Nötzel, B. Zanger and M. M. Beşe, QuNetSim: A Software Framework for Quantum Networks, in IEEE Transactions on Quantum Engineering, vol. 2, pp. 1-12, (2021), Art no. 2502512, [2003.06397], doi: 10.1109/TQE.2021.3092395
AK. Obeid , PD. Bruza and P. Wittek, Evaluating probabilistic programming languages for simulating quantum correlations PLoS ONE 14(1): e0208555. (2020), [1811.04424], doi:10.1371/journal.pone.0208555