Reverse Quantum Annealing Assisted by Forward Annealing
Abstract
:1. Introduction
2. Graph Coloring
3. Reverse Annealing
RA Parameters
4. Results
4.1. Reverse Distance
4.1.1. Case A
4.1.2. Case B
4.1.3. Case C
4.1.4. Case D
4.2. Scaling Analysis
4.3. Random Initial State
5. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Finnila, A.B.; Gomez, M.A.; Sebenik, C.; Stenson, C.; Doll, J.D. Quantum annealing: A new method for minimizing multidimensional functions. Chem. Phys. Lett. 1994, 219, 343–348. [Google Scholar] [CrossRef]
- Harris, R.; Johnson, M.W.; Lanting, T.; Berkley, A.J.; Johansson, J.; Bunyk, P.; Tolkacheva, E.; Ladizinsky, E.; Ladizinsky, N.; Oh, T.; et al. Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor. Phys. Rev. B 2010, 82, 024511. [Google Scholar] [CrossRef]
- King, A.D.; Nisoli, C.; Dahl, E.D.; Poulin-Lamarre, G.; Lopez-Bezanilla, A. Qubit spin ice. Science 2021, 373, 576–580. [Google Scholar] [CrossRef] [PubMed]
- Ohzeki, M. Breaking limitation of quantum annealer in solving optimization problems under constraints. Sci. Rep. 2020, 10, 3126. [Google Scholar] [CrossRef]
- Arute, F.; Arya, K.; Babbush, R.; Bacon, D.; Bardin, J.C.; Barends, R.; Biswas, R.; Boixo, S.; Brandao, F.G.S.L.; Buell, D.A.; et al. Quantum supremacy using a programmable superconducting processor. Nature 2019, 574, 505–510. [Google Scholar] [CrossRef] [PubMed]
- Bruzewicz, C.D.; Chiaverini, J.; McConnell, R.; Sage, J.M. Trapped-ion quantum computing: Progress and challenges. Appl. Phys. Rev. 2019, 6, 021314. [Google Scholar] [CrossRef]
- Gyongyosi, L.; Imre, S. A Survey on quantum computing technology. Comput. Sci. Rev. 2019, 31, 51–71. [Google Scholar] [CrossRef]
- Jattana, M.S. Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm. Physica Scripta 2024, 99, 095117. [Google Scholar] [CrossRef]
- McClean, J.R.; Romero, J.; Babbush, R.; Aspuru-Guzik, A. The theory of variational hybrid quantum-classical algorithms. New J. Phys. 2016, 18, 23023. [Google Scholar] [CrossRef]
- Jattana, M.S.; Jin, F.; De Raedt, H.; Michielsen, K. Improved Variational Quantum Eigensolver Via Quasidynamical Evolution. Phys. Rev. Appl. 2023, 19, 024047. [Google Scholar] [CrossRef]
- Kandala, A.; Mezzacapo, A.; Temme, K.; Takita, M.; Brink, M.; Chow, J.M.; Gambetta, J.M. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 2017, 549, 242–246. [Google Scholar] [CrossRef] [PubMed]
- Jattana, M.S.; Jin, F.; De Raedt, H.; Michielsen, K. Assessment of the Variational Quantum Eigensolver: Application to the Heisenberg Model. Front. Phys. 2022, 10, 907160. [Google Scholar] [CrossRef]
- Willsch, D.; Jattana, M.S.; Willsch, M.; Schulz, S.; Jin, F.; De Raedt, H.; Michielsen, K. Hybrid Quantum Classical Simulations. arXiv 2022, arXiv:2210.02811. [Google Scholar]
- Ohkuwa, M.; Nishimori, H.; Lidar, D.A. Reverse annealing for the fully connected p-spin model. Phys. Rev. A 2018, 98, 022314. [Google Scholar] [CrossRef]
- Kadowaki, T.; Nishimori, H. Quantum annealing in the transverse Ising model. Phys. Rev. E 1998, 58, 5355–5363. [Google Scholar] [CrossRef]
- Santoro, G.E.; Martoňák, R.; Tosatti, E.; Car, R. Theory of Quantum Annealing of an Ising Spin Glass. Science 2002, 295, 2427–2430. [Google Scholar] [CrossRef]
- Perdomo-Ortiz, A.; Venegas-Andraca, S.E.; Aspuru-Guzik, A. A study of heuristic guesses for adiabatic quantum computation. Quantum Inf. Process. 2011, 10, 33–52. [Google Scholar] [CrossRef]
- Pelofske, E.; Hahn, G.; Djidjev, H. Initial state encoding via reverse quantum annealing and h-gain features. arXiv 2023, arXiv:2303.13748. [Google Scholar] [CrossRef] [PubMed]
- Venturelli, D.; Kondratyev, A. Reverse quantum annealing approach to portfolio optimization problems. Quantum Mach. Intell. 2019, 1, 17–30. [Google Scholar] [CrossRef]
- Yamashiro, Y.; Ohkuwa, M.; Nishimori, H.; Lidar, D.A. Dynamics of reverse annealing for the fully connected p-spin model. Phys. Rev. A 2019, 100, 052321. [Google Scholar] [CrossRef]
- Chancellor, N. Modernizing quantum annealing using local searches. New J. Phys. 2017, 19, 023024. [Google Scholar] [CrossRef]
- Arai, S.; Ohzeki, M.; Tanaka, K. Mean field analysis of reverse annealing for code-division multiple-access multiuser detection. Phys. Rev. Res. 2021, 3, 033006. [Google Scholar] [CrossRef]
- Pelofske, E.; Bärtschi, A.; Eidenbenz, S. Simulating Heavy-Hex Transverse Field Ising Model Magnetization Dynamics Using Programmable Quantum Annealers. arXiv 2023, arXiv:2311.01657. [Google Scholar]
- Golden, J.; O’Malley, D. Reverse annealing for nonnegative/binary matrix factorization. PLoS ONE 2021, 16, e0244026. [Google Scholar] [CrossRef]
- Ikeda, K.; Nakamura, Y.; Humble, T.S. Application of Quantum Annealing to Nurse Scheduling Problem. Sci. Rep. 2019, 9, 12837. [Google Scholar] [CrossRef]
- Imoto, T.; Susa, Y.; Miyazaki, R.; Kadowaki, T.; Matsuzaki, Y. Demonstration of the excited-state search on the D-wave quantum annealer. arXiv 2023, arXiv:2305.15974. [Google Scholar]
- Haba, R.; Ohzeki, M.; Tanaka, K. Travel time optimization on multi-AGV routing by reverse annealing. Sci. Rep. 2022, 12, 17753. [Google Scholar] [CrossRef] [PubMed]
- Kim, M.; Singh, A.K.; Venturelli, D.; Kaewell, J.; Jamieson, K. X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism. arXiv 2024, arXiv:2402.18778. [Google Scholar]
- Bando, Y.; Yip, K.W.; Chen, H.; Lidar, D.A.; Nishimori, H. Breakdown of the Weak-Coupling Limit in Quantum Annealing. Phys. Rev. Appl. 2022, 17, 054033. [Google Scholar] [CrossRef]
- Rocutto, L.; Destri, C.; Prati, E. Quantum Semantic Learning by Reverse Annealing of an Adiabatic Quantum Computer. Adv. Quantum Technol. 2021, 4, 2000133. [Google Scholar] [CrossRef]
- King, J.; Mohseni, M.; Bernoudy, W.; Fréchette, A.; Sadeghi, H.; Isakov, S.V.; Neven, H.; Amin, M.H. Quantum-Assisted Genetic Algorithm. arXiv 2019, arXiv:1907.00707. [Google Scholar]
- Garey, M.R.; Johnson, D.S.; Stockmeyer, L. Some Simplified NP-Complete Problems. In Proceedings of the STOC’74: Proceedings of the Sixth Annual ACM Symposium on Theory of Computing, Seattle, WA, USA, 30 April–2 May 1974; Association for Computing Machinery: New York, NY, USA, 1974. [Google Scholar]
- Garey, M.R.; Johnson, D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness; W. H. Freeman & Co.: New York, NY, USA, 1990. [Google Scholar]
- NetworkX Developer Team. NetworkX. 2014. Available online: https://networkx.org/ (accessed on 5 May 2023).
- Tamura, Y.; Sakai, M. Tamuhey/Qubogen. Available online: https://github.com/tamuhey/qubogen (accessed on 5 May 2023).
- Glover, F.; Kochenberger, G.; Hennig, R.; Du, Y. Quantum bridge analytics I: A tutorial on formulating and using QUBO models. Ann. Oper. Res. 2022, 314, 141–183. [Google Scholar] [CrossRef]
- D-Wave Leap. Available online: https://www.dwavesys.com/take-leap (accessed on 5 May 2023).
- Kwok, J.; Pudenz, K. Graph Coloring with Quantum Annealing. arXiv 2020, arXiv:2012.04470. [Google Scholar]
- Chen, J.; Stollenwerk, T.; Chancellor, N. Performance of Domain Wall Encoding for Quantum Annealing. IEEE Trans. Quantum Eng. 2021, 2, 1–14. [Google Scholar] [CrossRef]
- Seki, Y.; Nishimori, H. Quantum annealing with antiferromagnetic fluctuations. Phys. Rev. E 2012, 85, 051112. [Google Scholar] [CrossRef]
- Pelofske, E.; Hahn, G.; Djidjev, H.N. Advanced anneal paths for improved quantum annealing. In Proceedings of the 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), Denver, CO, USA, 12–16 October 2020; pp. 256–266. [Google Scholar] [CrossRef]
- King, A.D.; Carrasquilla, J.; Raymond, J.; Ozfidan, I.; Andriyash, E.; Berkley, A.; Reis, M.; Lanting, T.; Harris, R.; Altomare, F.; et al. Observation of topological phenomena in a programmable lattice of 1,800 qubits. Nature 2018, 560, 456–460. [Google Scholar] [CrossRef]
- Lucas, A. Ising formulations of many NP problems. Front. Phys. 2014, 2, 5. [Google Scholar] [CrossRef]
- Tabi, Z.; El-Safty, K.H.; Kallus, Z.; Hága, P.; Kozsik, T.; Glos, A.; Zimborás, Z. Quantum Optimization for the Graph Coloring Problem with Space-Efficient Embedding. In Proceedings of the 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), Denver, CO, USA, 12–16 October 2020; pp. 56–62. [Google Scholar] [CrossRef]
- Śmierzchalski, T.; Mzaouali, Z.; Deffner, S.; Gardas, B. Efficiency optimization in quantum computing: Balancing thermodynamics and computational performance. Sci. Rep. 2024, 14, 4555. [Google Scholar] [CrossRef] [PubMed]
- Hagberg, A.A.; Schult, D.A.; Swart, P.J. Exploring Network Structure, Dynamics, and Function using NetworkX. In Proceedings of the 7th Python in Science Conference, Pasadena, CA, USA, 21 August 2008; Varoquaux, G., Vaught, T., Millman, J., Eds.; U.S. Department of Energy: Washington, DC, USA, 2008; pp. 11–15. [Google Scholar]
- Jattana, M.S.; Modular Supercomputing and Quantum Computing. Goethe University Data Repository. 2024. Available online: https://gude.uni-frankfurt.de/entities/researchdata/57431ed0-f2df-4e4b-aec8-6df63325b34c/details (accessed on 25 July 2024).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jattana, M.S. Reverse Quantum Annealing Assisted by Forward Annealing. Quantum Rep. 2024, 6, 452-464. https://doi.org/10.3390/quantum6030030
Jattana MS. Reverse Quantum Annealing Assisted by Forward Annealing. Quantum Reports. 2024; 6(3):452-464. https://doi.org/10.3390/quantum6030030
Chicago/Turabian StyleJattana, Manpreet Singh. 2024. "Reverse Quantum Annealing Assisted by Forward Annealing" Quantum Reports 6, no. 3: 452-464. https://doi.org/10.3390/quantum6030030
APA StyleJattana, M. S. (2024). Reverse Quantum Annealing Assisted by Forward Annealing. Quantum Reports, 6(3), 452-464. https://doi.org/10.3390/quantum6030030