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Digitized Counterdiabatic Quantum Algorithms for Logistics Scheduling (2405.15707v2)

Published 24 May 2024 in quant-ph and cond-mat.mes-hall

Abstract: We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO)algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic protocols. Thereafter, we digitize the global unitary to encode it in a digital quantum computer. For the job-shop scheduling problem, we aim at finding the optimal schedule for a robot executing a number of tasks under specific constraints, such that the total execution time of the process is minimized. For the traveling salesperson problem, the goal is to find the path that covers all cities and is associated with the shortest traveling distance. We consider both hybrid and pure versions of DCQO algorithms and benchmark the performance against digitized quantum annealing and the quantum approximate optimization algorithm (QAOA). In comparison to QAOA, the DCQO solution is improved by several orders of magnitude in success probability using the same number of two-qubit gates. Moreover, we implement our algorithms on cloud-based superconducting and trapped-ion quantum processors. Our results demonstrate that circuit compression using counterdiabatic protocols is amenable to current NISQ hardware and can solve logistics scheduling problems, where other digital quantum algorithms show insufficient performance.

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References (21)
  1. L. K. Grover, A fast quantum mechanical algorithm for database search, arXiv e-prints , quant-ph/9605043 (1996), arXiv:quant-ph/9605043 [quant-ph] .
  2. N. N. Hegade, X. Chen, and E. Solano, Digitized counterdiabatic quantum optimization, Physical Review Research 4, L042030 (2022).
  3. N. N. Hegade and E. Solano, Digitized-counterdiabatic quantum factorization, arXiv preprint arXiv:2301.11005  (2023).
  4. M. Demirplak and S. A. Rice, Adiabatic population transfer with control fields, The Journal of Physical Chemistry A 107, 9937 (2003).
  5. M. V. Berry, Transitionless quantum driving, Journal of Physics A: Mathematical and Theoretical 42, 365303 (2009).
  6. A. del Campo, Shortcuts to adiabaticity by counterdiabatic driving, Physical review letters 111, 100502 (2013).
  7. A. Lucas, Ising formulations of many NP problems, Frontiers in Physics 2, 5 (2014), arXiv:1302.5843 [cond-mat.stat-mech] .
  8. D. Pisinger and S. Ropke, Large neighborhood search, in Handbook of Metaheuristics, edited by M. Gendreau and J.-Y. Potvin (Springer International Publishing, Cham, 2019) pp. 99–127.
  9. M. Booth, S. P. Reinhardt, and A. Roy, Partitioning optimization problems for hybrid classical/quantum execution technical report (2017).
  10. E. Osaba, X.-S. Yang, and J. Del Ser, Chapter 9 - Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics, in Nature-Inspired Computation and Swarm Intelligence (Academic Press, 2020) pp. 135–164.
  11. A. K. Mandal and P. Kumar Deva Sarma, Novel applications of ant colony optimization with the traveling salesman problem in dna sequence optimization, in 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) (2022) pp. 1–6.
  12. K. Nałęcz-Charkiewicz and R. M. Nowak, Algorithm for DNA sequence assembly by quantum annealing, BMC Bioinformatics 23, 122 (2022).
  13. S. Jain, Solving the traveling salesman problem on the d-wave quantum computer, Frontiers in Physics 9, 10.3389/fphy.2021.760783 (2021).
  14. F. Glover, G. Kochenberger, and Y. Du, A Tutorial on Formulating and Using QUBO Models, arXiv e-prints , arXiv:1811.11538 (2018), arXiv:1811.11538 [cs.DS] .
  15. T. Kadowaki and H. Nishimori, Quantum annealing in the transverse Ising model, Phys. Rev. E 58, 5355 (1998).
  16. E. Farhi, J. Goldstone, and S. Gutmann, A Quantum Approximate Optimization Algorithm, arXiv e-prints , arXiv:1411.4028 (2014), arXiv:1411.4028 [quant-ph] .
  17. D. Sels and A. Polkovnikov, Minimizing irreversible losses in quantum systems by local counterdiabatic driving, Proceedings of the National Academy of Sciences 114, E3909 (2017).
  18. T. Hatomura and K. Takahashi, Controlling and exploring quantum systems by algebraic expression of adiabatic gauge potential, Physical Review A 103, 012220 (2021).
  19. K. Takahashi and A. del Campo, Shortcuts to adiabaticity in krylov space, arXiv preprint arXiv:2302.05460  (2023).
  20. Getting started with native gates, https://ionq.com/docs/getting-started-with-native-gates, accessed: 2023-08-22.
  21. E. Osaba, E. Villar-Rodriguez, and I. Oregi, A systematic literature review of quantum computing for routing problems, IEEE Access 10, 55805 (2022).
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