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Model-Based Framework for Continuous Adaptation and Evolution of Quantum-Classical Hybrid Systems

Published: 09 July 2024 Publication History

Abstract

Although quantum computing has been attracting increasing attention, hardware restrictions are tight in current implementations. Intensive design exploration is therefore essential to match requirements, such as the problem scale and acceptable error rate, with potential designs to combine quantum computing and classical computing. The design decision made in this way is often fragile as it is sensitive to the problem scale as well as still evolving quantum services. We need continuous design decision, or adaptation and evolution, given changes in requirements or environments. In this paper, we present a framework for model-based engineering to support the continuous adaptation and evolution of quantum-classical hybrid systems. Modeling in our framework involves not only potential designs, but also rationale or evidence of design decision, which often requires simulation and experiments. This focus allows for tracing and analyzing whether the past decision is still valid or not, or whether there is uncertainty and we need further simulation and experiments. The usage of the framework is demonstrated with an example problem from steel manufacturing.

References

[1]
Shaukat Ali, Tao Yue, and Rui Abreu. 2022. When software engineering meets quantum computing. Commun. ACM 65, 4 (mar 2022), 84–88.
[2]
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, and Mattia Fiorentini. 2022. A case study of variational quantum algorithms for a job shop scheduling problem. EPJ Quantum Technology 9, 5 (2022).
[3]
Michael E. Beverland, Prakash Murali, Matthias Troyer, Krysta M. Svore, Torsten Hoefler, Vadym Kliuchnikov, Guang Hao Low, Mathias Soeken, Aarthi Sundaram, and Alexander Vaschillo. 2022. Assessing requirements to scale to practical quantum advantage. https://arxiv.org/abs/2211.07629.
[4]
Gordon Blair, Nelly Bencomo, and Robert B. France. 2009. Models@ run.time. Computer 42, 10 (2009), 22–27.
[5]
Gerardo Canfora, Massimiliano Di Penta, Raffaele Esposito, and Maria Luisa Villani. 2005. An approach for QoS-aware service composition based on genetic algorithms. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation(GECCO ’05). Association for Computing Machinery, New York, NY, USA, 1069–1075.
[6]
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles. 2021. Variational Quantum Algorithms. Nat Rev Phys 3 (August 2021), 625–644.
[7]
Antonio García de la Barrera, Ignacio García-Rodríguez de Guzmán, Macario Polo, and Mario Piattini. 2023. Quantum software testing: State of the art. Journal of Software: Evolution and Process 35, 4 (2023), e2419. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/smr.2419
[8]
Felix Gemeinhardt, Antonio Garmendia, and Manuel Wimmer. 2021. Towards Model-Driven Quantum Software Engineering. In 2021 IEEE/ACM 2nd International Workshop on Quantum Software Engineering (Q-SE). 13–15. https://doi.org/10.1109/Q-SE52541.2021.00010
[9]
Ilie-Daniel Gheorghe-Pop, Nikolay Tcholtchev, Tom Ritter, and Manfred Hauswirth. 2020. Quantum DevOps: Towards Reliable and Applicable NISQ Quantum Computing. In 2020 IEEE Globecom Workshops (GC Wkshps. 1–6.
[10]
Tadashi Kadowaki and Hidetoshi Nishimori. 1998. Quantum annealing in the transverse Ising model. Phys. Rev. E 58 (Nov 1998), 5355–5363. Issue 5.
[11]
Stuart Kent. 2002. Model Driven Engineering. In Integrated Formal Methods, Michael Butler, Luigia Petre, and Kaisa Sere (Eds.). 286–298.
[12]
Tom Krüger and Wolfgang Mauerer. 2020. Quantum Annealing-Based Software Components: An Experimental Case Study with SAT Solving. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops(ICSEW’20). Association for Computing Machinery, New York, NY, USA, 445–450.
[13]
Ilya Sinayskiy Maria Schuld and Francesco Petruccione. 2015. An introduction to quantum machine learning. Contemporary Physics 56, 2 (2015), 172–185.
[14]
Charles Moussa, Henri Calandra, and Vedran Dunjko. 2020. To quantum or not to quantum: towards algorithm selection in near-term quantum optimization. Quantum Science and Technology 5, 4 (oct 2020), 044009. https://doi.org/10.1088/2058-9565/abb8e5
[15]
OASIS. 2007. Web Services Business Process Execution Language Version 2.0. http://docs.oasis-open.org/wsbpel/2.0/wsbpel-v2.0.html.
[16]
Xin Peng, Bihuan Chen, Yijun Yu, and Wenyun Zhao. 2012. Self-tuning of software systems through dynamic quality tradeoff and value-based feedback control loop. Journal of Systems and Software 85, 12 (2012), 2707–2719. https://doi.org/10.1016/j.jss.2012.04.079 Self-Adaptive Systems.
[17]
Ricardo Pérez-Castillo and Mario Piattini. 2022. Design of classical-quantum systems with UML. Computing 104 (2022), 2375–2403.
[18]
John Preskill. 2018. Quantum Computing in the NISQ era and beyond. Quantum 2 (August 2018), 79.
[19]
Nils Quetschlich, Lukas Burgholzer, and Robert Wille. 2023. Towards an Automated Framework for Realizing Quantum Computing Solutions. arxiv:2210.14928 [quant-ph]
[20]
Marie Salm, Johanna Barzen, Uwe Breitenbücher, Frank Leymann, Benjamin Weder, and Karoline Wild. 2020. The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms. In Service-Oriented Computing, Schahram Dustdar (Ed.). Springer International Publishing, Cham, 66–85.
[21]
Krysta Svore, Alan Geller, Matthias Troyer, John Azariah, Christopher Granade, Bettina Heim, Vadym Kliuchnikov, Mariia Mykhailova, Andres Paz, and Martin Roetteler. 2018. Q#: Enabling Scalable Quantum Computing and Development with a High-level DSL. In Proceedings of the Real World Domain Specific Languages Workshop 2018 (Vienna, Austria) (RWDSL2018). Association for Computing Machinery, New York, NY, USA, Article 7, 10 pages.
[22]
Wim van Dam, Mariia Mykhailova, and Mathias Soeken. 2023. Using Azure Quantum Resource Estimator for Assessing Performance of Fault Tolerant Quantum Computation. In The SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. 1414–1419.
[23]
Benjamin Weder, Johanna Barzen, and Frank Leymann. 2021. MODULO: Modeling, Transformation, and Deployment of Quantum Workflows. In 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). 341–344.
[24]
Benjamin Weder, Johanna Barzen, Frank Leymann, and Daniel Vietz. 2022. Quantum Software Development Lifecycle. Springer International Publishing, Cham, 61–83.
[25]
Benjamin Weder, Uwe Breitenbücher, Frank Leymann, and Karoline Wild. 2020. Integrating Quantum Computing into Workflow Modeling and Execution. In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). 279–291.
[26]
Yi Wei and M. Brian Blake. 2010. Service-Oriented Computing and Cloud Computing: Challenges and Opportunities. IEEE Internet Computing 14, 6 (2010), 72–75.
[27]
Kouki Yonaga, Masamichi Miyama, Masayuki Ohzeki, Koji Hirano, Hirokazu Kobayashi, and Tetsuaki Kurokawa. 2022. Quantum Optimization with Lagrangian Decomposition for Multiple-process Scheduling in Steel Manufacturing. ISIJ International 62, 9 (2022), 1874–1880.
[28]
Tao Yue, Shaukat Ali, and Paolo Arcaini. 2023. Towards Quantum Software Requirements Engineering. In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Vol. 02. 161–164.

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cover image ACM Conferences
Programming '24: Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming
March 2024
159 pages
ISBN:9798400706349
DOI:10.1145/3660829
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 09 July 2024

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Author Tags

  1. DevOps
  2. Hybrid Quantum Computing
  3. [email protected]
  4. Quantum Software Engineering
  5. Self-Adaptive Systems
  6. Services Computing

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