Scalable and Accurate Quantum-
Classical Optimization (SAQUCO)


Project funded by the national scientific program “Petar Beron i NIE”

About

About the project

Develop hybrid classical-quantum methods to help improve the accuracy and scalability of the current quantum annealing computers.

Start date: June 1, 2022

End date: May 31, 2024

Funded by: Bulgarian National Science Fund

Abstract: Quantum annealers such as the commercially available computers from D-Wave systems aim to exploit quantum effects to obtain, in very short times, high-quality approximate solutions for NP-hard problems that are challenging for classical computers. The main hurdles limiting the practical impact of quantum annealing (QA) are related to scalability—the limited sizes of problems the current QA computers can solve—and the noisiness of the hardware, affecting the accuracy of the solutions. The objective of this project is to develop a framework that targets both the scalability and the accuracy issues of QA and that would significantly increase the sizes of the problems that can be solved to near optimality on existing quantum annealers. Specifically, we will design methods for reducing large problems into sub¬problems small enough to fit the QPU, solving them on the annealer, and combining their solutions into a solution of the original problem; and reducing the effects of the hardware errors of the annealer for solving large problems by finding combinations of tunable hardware parameters that reduce the effects of biases and noise. The results of this project will help in increasing the range of problems the current noisy intermediate-scale quantum (NISQ) technology quantum annealers can successfully solve.

Publications

List of relevant publications

Since project's start

  1. H. Djidjev.
    Replication-based quantum annealing error mitigation. 21st ACM International Conference on Computing Frontiers, ACM, 2024
  2. G. Hahn, E. Pelofske, H. Djidjev.
    Posiform planting: generating QUBO instances for benchmarking. Frontiers in Computer Science, 5, Frontiers Media SA, 2023.
  3. E. Pelofske, G. Hahn, H. Djidjev.
    Initial state encoding via reverse quantum annealing and h-gain features. IEEE Transactions on Quantum Engineering, 4, IEEE, 2023
  4. H. Djidjev.
    Quantum annealing with inequality constraints: the set cover problem. Advanced Quantum Technologies, 6, 11, 2023.
  5. H. Djidjev.
    Logical qubit implementation for quantum annealing: augmented Lagrangian approach. Quantum Science and Technology, 8 035013, 2023.
  6. E. Pelofske, G. Hahn, H. Djidjev.
    Noise dynamics of quantum annealers: estimating the effective noise using idle qubits. Quantum Science and Technology, 8 (3), 035005, 2023.
  7. E. Pelofske, G. Hahn, H. Djidjev.
    Solving larger maximum clique problems using parallel quantum annealing. Quantum Information Processing, 22 (5), 219, 2023.
  8. M Bhattarai, I Boureima, E Skau, B Nebgen, H Djidjev, et al.
    Distributed non-negative RESCAL with automatic model selection for exascale data. Journal of Parallel and Distributed Computing, 2023.
  9. E. Pelofske, G. Hahn, H. Djidjev.
    Parallel quantum annealing. Scientific Reports, 12, 1, Nature Publishing Group, 2022
  10. E. Pelofske, G. Hahn, H. Djidjev.
    Inferring the Dynamics of the State Evolution During Quantum Annealing. IEEE Transactions on Parallel and Distributed Systems, 33, 2, IEEE, 2022.
  11. I. Boureima, M. Bhattarai, M. Eren, N. Solovyev, H. Djidjev, B. Alexandrov.
    Distributed Out-of-Memory SVD on CPU/GPU Architectures, IEEE High Performance Extreme Computing Conference (HPEC), 2022, Outstanding Paper award.
  12. J. Abhijith, A. Adedoyin, J. Ambrosiano, P. Anisimov, P. Casper, G. Chennupati, C. Coffrin, H. Djidjev, et al.
    Quantum Algorithm Implementations for Beginners. ACM Transactions on Quantum Computing, 3, 4, 2022.

Selected prior relevant publications

  1. E Pelofske, G Hahn, D O`Malley, HN Djidjev, BS Alexandrov.
    Quantum annealing algorithms for Boolean tensor networks, Scientific Reports 12 (1), 1-19, 2022.
  2. E Pelofske, G Hahn, HN Djidjev.
    Parallel quantum annealing, Scientific Reports 12 (1), 1-11, 2022.
  3. A. Barbosa, E Pelofske, G Hahn, HN Djidjev.
    Using machine learning for quantum annealing accuracy prediction, Algorithms 14 (6), 187, 2021.
  4. A. E Pelofske, G Hahn, H Djidjev.
    Decomposition algorithms for solving NP-hard problems on a quantum annealer, Journal of Signal Processing Systems 93 (4), 405-420, 2021.
  5. A. E Pelofske, G Hahn, H Djidjev.
    Reducing quantum annealing biases for solving the graph partitioning problem, Proceedings of the 18th ACM International Conference on Computing Frontiers, 133-139, 2021.

Team




Our Team:



Hristo Djidjev

Principal Investigator
Institute of Information and Communication Technologies, Sofia, Bulgaria
and
Los Alamos National Laboratory, Los Alamos, NM 87545, USA djidjev@parallel.bas.bg

Svetozar Margenov

Supervisor
Institute of Information and Communication Technologies, Sofia, Bulgaria margenov@parallel.bas.bg

Elijah Pelofske

External Collaborator
Los Alamos National Laboratory, Los Alamos, NM 87545, USA epelofske@lanl.gov

Georg Hahn

External Collaborator
Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA ghahn@hsph.harvard.edu

Contact

Contact Information

Address

Institute of Information and Communication Technologies

Acad.G.Bonchev st. Bl.25A, 1113 Sofia, Bulgaria

Email

djidjev@parallel.bas.bg

Phone

+359 2 979 6611