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Weighted Feedback-Based Quantum Algorithm for Excited States Calculation (2404.19386v2)

Published 30 Apr 2024 in quant-ph

Abstract: Drawing inspiration from the Lyapunov control technique for quantum systems, feedback-based quantum algorithms have been proposed for calculating the ground states of Hamiltonians. In this work, we consider extending these algorithms to tackle calculating excited states. Inspired by the weighted subspace-search variational quantum eigensolver algorithm, we propose a novel weighted feedback-based quantum algorithm for excited state calculation. We show that depending on how we design the weights and the feedback law, we can prepare the $p$th excited state or lowest energy states up to the $p$th excited state. Through an application in quantum chemistry, we show the effectiveness of the proposed algorithm, evaluating its efficacy via numerical simulations.

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References (30)
  1. M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio et al., “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, 2021.
  2. H. L. Tang, V. Shkolnikov, G. S. Barron, H. R. Grimsley, N. J. Mayhall, E. Barnes, and S. E. Economou, “qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor,” PRX Quantum, vol. 2, no. 2, p. 020310, 2021.
  3. L. Zhu, H. L. Tang, G. S. Barron, F. Calderon-Vargas, N. J. Mayhall, E. Barnes, and S. E. Economou, “Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer,” Physical Review Research, vol. 4, no. 3, p. 033029, 2022.
  4. W. Lavrijsen, A. Tudor, J. Müller, C. Iancu, and W. De Jong, “Classical optimizers for noisy intermediate-scale quantum devices,” in 2020 IEEE international conference on quantum computing and engineering (QCE).   IEEE, 2020, pp. 267–277.
  5. A. B. Magann, K. M. Rudinger, M. D. Grace, and M. Sarovar, “Feedback-based quantum optimization,” Physical Review Letters, vol. 129, no. 25, p. 250502, 2022.
  6. ——, “Lyapunov-control-inspired strategies for quantum combinatorial optimization,” Physical Review A, vol. 106, no. 6, p. 062414, 2022.
  7. D. Wakeham and J. Ceroni, “Feedback-based quantum optimization (falqon),” https://pennylane.ai/qml/demos/tutorial_falqon/, 05 2021, date Accessed: 2024-04-17.
  8. J. B. Larsen, M. D. Grace, A. D. Baczewski, and A. B. Magann, “Feedback-based quantum algorithm for ground state preparation of the fermi-hubbard model,” arXiv preprint arXiv:2303.02917, 2023.
  9. A. B. Magann, S. E. Economou, and C. Arenz, “Randomized adaptive quantum state preparation,” arXiv preprint arXiv:2301.04201, 2023.
  10. M. Motta and J. E. Rice, “Emerging quantum computing algorithms for quantum chemistry,” Wiley Interdisciplinary Reviews: Computational Molecular Science, vol. 12, no. 3, p. e1580, 2022.
  11. K. Ura, T. Imoto, T. Nikuni, S. Kawabata, and Y. Matsuzaki, “Analysis of the shortest vector problems with quantum annealing to search the excited states,” Japanese Journal of Applied Physics, vol. 62, no. SC, p. SC1090, 2023.
  12. M. Motta, C. Sun, A. T. Tan, M. J. O’Rourke, E. Ye, A. J. Minnich, F. G. Brandao, and G. K.-L. Chan, “Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution,” Nature Physics, vol. 16, no. 2, pp. 205–210, 2020.
  13. Y. Seki, Y. Matsuzaki, and S. Kawabata, “Excited state search using quantum annealing,” Journal of the Physical Society of Japan, vol. 90, no. 5, p. 054002, 2021.
  14. N. P. Bauman, H. Liu, E. J. Bylaska, S. Krishnamoorthy, G. H. Low, C. E. Granade, N. Wiebe, N. A. Baker, B. Peng, M. Roetteler et al., “Toward quantum computing for high-energy excited states in molecular systems: quantum phase estimations of core-level states,” Journal of Chemical Theory and Computation, vol. 17, no. 1, pp. 201–210, 2020.
  15. J. Wen, Z. Wang, C. Chen, J. Xiao, H. Li, L. Qian, Z. Huang, H. Fan, S. Wei, and G. Long, “A full circuit-based quantum algorithm for excited-states in quantum chemistry,” Quantum, vol. 8, p. 1219, 2024.
  16. C. L. Cortes and S. K. Gray, “Quantum krylov subspace algorithms for ground-and excited-state energy estimation,” Physical Review A, vol. 105, no. 2, p. 022417, 2022.
  17. K. M. Nakanishi, K. Mitarai, and K. Fujii, “Subspace-search variational quantum eigensolver for excited states,” Physical Review Research, vol. 1, no. 3, p. 033062, 2019.
  18. O. Higgott, D. Wang, and S. Brierley, “Variational quantum computation of excited states,” Quantum, vol. 3, p. 156, 2019.
  19. J. R. McClean, M. E. Kimchi-Schwartz, J. Carter, and W. A. De Jong, “Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states,” Physical Review A, vol. 95, no. 4, p. 042308, 2017.
  20. I. G. Ryabinkin, S. N. Genin, and A. F. Izmaylov, “Constrained variational quantum eigensolver: Quantum computer search engine in the fock space,” Journal of chemical theory and computation, vol. 15, no. 1, pp. 249–255, 2018.
  21. R. Santagati, J. Wang, A. A. Gentile, S. Paesani, N. Wiebe, J. R. McClean, S. Morley-Short, P. J. Shadbolt, D. Bonneau, J. W. Silverstone et al., “Witnessing eigenstates for quantum simulation of hamiltonian spectra,” Science advances, vol. 4, no. 1, p. eaap9646, 2018.
  22. S. A. Rahman, Ö. Karabacak, and R. Wisniewski, “Feedback-based quantum algorithm for excited states calculation,” arXiv preprint arXiv:2404.04620, 2024.
  23. L. Mackey, “Deflation methods for sparse pca,” Advances in neural information processing systems, vol. 21, 2008.
  24. A. B. Magann, M. D. Grace, H. A. Rabitz, and M. Sarovar, “Digital quantum simulation of molecular dynamics and control,” Physical Review Research, vol. 3, no. 2, p. 023165, 2021.
  25. S. Cong and F. Meng, “A survey of quantum lyapunov control methods,” The Scientific World Journal, vol. 2013, 2013.
  26. S. Grivopoulos and B. Bamieh, “Lyapunov-based control of quantum systems,” in 42nd IEEE International Conference on Decision and Control (IEEE Cat. No. 03CH37475), vol. 1.   IEEE, 2003, pp. 434–438.
  27. D. A. Fedorov, B. Peng, N. Govind, and Y. Alexeev, “Vqe method: a short survey and recent developments,” Materials Theory, vol. 6, no. 1, p. 2, 2022.
  28. C. Hempel, C. Maier, J. Romero, J. McClean, T. Monz, H. Shen, P. Jurcevic, B. P. Lanyon, P. Love, R. Babbush et al., “Quantum chemistry calculations on a trapped-ion quantum simulator,” Physical Review X, vol. 8, no. 3, p. 031022, 2018.
  29. Z. Zong, S. Huai, T. Cai, W. Jin, Z. Zhan, Z. Zhang, K. Bu, L. Sui, Y. Fei, Y. Zheng et al., “Determination of molecular energies via variational-based quantum imaginary time evolution in a superconducting qubit system,” Science China Physics, Mechanics & Astronomy, vol. 67, no. 4, pp. 1–11, 2024.
  30. R. K. Malla, H. Sukeno, H. Yu, T.-C. Wei, A. Weichselbaum, and R. M. Konik, “Feedback-based quantum algorithm inspired by counterdiabatic driving,” arXiv preprint arXiv:2401.15303, 2024.
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