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Machine learning-based performance prediction in quantum queueing: comparative evaluation of tree and neural models for fidelity, heat and latency

Annals of Operations Research

operationsoperations/service-operationsanalyticsanalytics/forecastingdigital

摘要

论文《Machine learning-based performance prediction in quantum queueing: comparative evaluation of tree and neural models for fidelity, heat and latency》主要讨论：MACHINE LEARNING-BASED PERFORMANCE PREDICTION IN QUANTUM QUEUEING: COMPARATIVE EVALUATION OF TREE AND NEURAL MODELS FOR FIDELITY, HEAT AND LATENCY * Original Research * Published: ...

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Machine learning-based performance prediction in quantum queueing: comparative evaluation of tree and neural models for fidelity, heat and latency

  • Original Research
  • Published:

Abstract

Abstrct

In this paper, we address the critical challenge of performance evaluation in complex quantum queueing systems, where traditional analytical methods are often mathematically intractable and direct simulation is computationally prohibitive. Initially, we formulate a comprehensive quantum queueing model incorporating entropy-dependent service degradation, thermodynamic feedback and multi-mode server dynamics and derive the steady-state probability equations using the supplementary variable technique. Subsequently, we propose a novel data-driven methodology that leverages Machine Learning (ML) techniques to provide fast and accurate predictions of key quantum-specific performance metrics. To achieve this, a comprehensive dataset is generated through offline simulations guided by a Deep Q-Network (DQN) policy, capturing the system dynamics under an optimized control strategy. Further, several ML models, including Decision Trees, Random Forests, Gradient Boosting and Artificial Neural Networks, are trained and comparatively evaluated to predict important performance measures such as average queue length, fidelity of quantum states and heat generation rate from the system parameters. Moreover, the proposed framework demonstrates outstanding predictive performance, with all models achieving exceptionally high accuracy and \(R^2\) values consistently exceeding 0.9995 across all target metrics. The predictive capability of the models is further validated through scatter plots comparing predicted and actual values, dependency plots illustrating the relationships between performance metrics and key input parameters (e.g., arrival rates and service rates) and detailed analyses of prediction errors. Finally, this work bridges the gap between quantum queueing theory and data-driven performance evaluation by integrating rigorous stochastic modeling with modern ML techniques, thereby providing an efficient, scalable and practical framework for the analysis, prediction and optimization of quantum technologies while explicitly accounting for quantum-specific constraints and thermodynamic costs.

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References

  • Begum, A., Choudhury, G., Sarma, H. J., & Kumar, N. (2025). Reliability analysis of an unreliable n-policy queue under bernoulli vacation schedule with two-service genres and optional repeated service. International Journal of Reliability, Quality and Safety Engineering, 32(04), 2550005.

    Article  Google Scholar 

  • Chaudhry, M., Banik, A., Dev, S., & Barik, S. (2025). A simple derivation of the waiting-time distribution (in the queue) for the bulk-service queueing system. Annals of Operations Research, 352(1), 1–24.

    Article  Google Scholar 

  • Chen, W. L., Koh, T., & Lin, J. H. (2025). Reliability analysis and sensitivity analysis of retrial machine repair problems with multiple vacations and the recovery policy. International Journal of Reliability, Quality and Safety Engineering, 32(01), 2450045.

    Article  Google Scholar 

  • Dai, W., Peng, T., & Win, M. Z. (2020). Quantum queuing delay. IEEE Journal on Selected Areas in Communications, 38(3), 605–618.

    Article  Google Scholar 

  • Devoret, M. H., & Schoelkopf, R. J. (2013). Superconducting circuits for quantum information: an outlook. Science, 339(6124), 1169–1174.

    Article  Google Scholar 

  • Dhibar, S., & Jain, M. (2025). Metaheuristic optimization and strategic behavior of markovian vacation queue with retrial policy: application to virtual call center. Evolutionary Intelligence, 18(1), 13.

    Article  Google Scholar 

  • Falin, G. (1990). A survey of retrial queues. Queueing systems, 7(2), 127–167.

    Article  Google Scholar 

  • Fiusa, G., & Landi, G. T. (2025). Queued quantum collision models. Physical Review A, 111(3), Article 032220.

    Article  Google Scholar 

  • Gaudenzi, R., Burzurí, E., Maegawa, S., Van Der Zant, H., & Luis, F. (2018). Quantum landauer erasure with a molecular nanomagnet. Nature Physics, 14(6), 565–568.

    Article  Google Scholar 

  • Gawron, P., Kurzyk, D., & Puchała, Z. (2013). A model for quantum queue. International Journal of Quantum Information, 11(02), 1350023.

    Article  Google Scholar 

  • Huang, C. Y., & Huang, W. C. (2008). Software reliability analysis and measurement using finite and infinite server queueing models. IEEE Transactions on Reliability, 57(1), 192–203.

    Article  Google Scholar 

  • Huang, Y., Tian, R., & Su, J. (2025). Model validation and strategy analysis in retrial queues with delayed vacations and feedback based on monte carlo simulation. Mathematics, 13(11), 1856.

    Article  Google Scholar 

  • Jagannathan, K., Chatterjee, A., & Mandayam, P. (2019). Qubits through queues: The capacity of channels with waiting time dependent errors. 2019 national conference on communications (ncc) (pp. 1–6).

  • Karthick, A., Ramaraj, E., & Kannan, R. (2013). An efficient tri queue job scheduling using dynamic quantum time for cloud environment (pp. 871–876) international conference on green computing, communication and conservation of energy (icgce).

  • Keerthana, R. (2025). An m/g/1 retrial queue with working vacation, non persistent customers and a waiting server. Reliability: Theory & Applications, 20(1 (82)), 1089–1099,

  • Krantz, P., Kjaergaard, M., Yan, F., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2019). A quantum engineer’s guide to superconducting qubits. Applied Physics Reviews, 6(2),

  • Kuo, C. C., Huang, K., Ke, J. C., Liu, T. H., & Sheu, S. H. (2025). Modelling and optimisation for a retrial queue with imperfect coverage and reboot delay. Journal of Control and Decision, 12(1), 123–130.

    Article  Google Scholar 

  • Liu, T. H., Chang, F. M., Ke, J. C., & Sheu, S. H. (2022). Optimization of retrial queue with unreliable servers subject to imperfect coverage and reboot delay. Quality Technology & Quantitative Management, 19(4), 428–453.

    Article  Google Scholar 

  • Liu, T. H., Hsu, H. Y., & Chang, F. M. (2025). Multi-server two-way communication retrial queue subject to disaster and synchronous working vacation. Algorithms, 18(1), 24.

    Article  Google Scholar 

  • Mandayam, P., Jagannathan, K., & Chatterjee, A. (2019). The classical capacity of a quantum erasure queue-channel. 2019 ieee 20th international workshop on signal processing advances in wireless communications (spawc) (pp. 1–5).

  • Mandayam, P., Jagannathan, K., & Chatterjee, A. (2020). The classical capacity of additive quantum queue-channels. IEEE Journal on Selected Areas in Information Theory, 1(2), 432–444.

    Article  Google Scholar 

  • Mathavavisakan, N. M., Indhira, K., & Ammar, S. I. (2025). Cost optimization and analysis of retrial queues with hybrid vacation policies and fault correction: A metaheuristic approach. RAIRO-Operations Research, 59(5), 2683–2720.

    Article  Google Scholar 

  • Mathavavisakan, N. M., Indhira, K., & Ammar, S. I. (2025). Optimizing energy harvesting in web of things services: a metaheuristic approach with retrial queue under working vacation. Soft Computing, 29(6), 2769–2790.

    Article  Google Scholar 

  • Moulai, R., Boualem, M., & Benrais, I. (2026). Analysis and performance evaluation of a bulk arrival retrial queue with impatient customers, server breakdowns, and bernoulli vacation policy. Mathematical Methods of Operations Research, 1–50.

  • Nguyen, T. P., Liao, Y. T., Lin, Y. K., & Chang, P. C. (2023). System reliability analysis for a stochastic liner container shipping service with stochastic terminals. International Journal of Reliability, Quality and Safety Engineering, 30(01), 2250019.

    Article  Google Scholar 

  • Poornima, R., & Kirupa, K. (2026). Supplementary variable technique approach on retrial queue with feedback and backup server during bernoulli’s lengthy vacation and random repairs. International Journal of Mathematics in Operational Research, 33(3), 361–390.

    Article  Google Scholar 

  • Rani, S., Jain, M., & Dhibar, S. (2026). Strategic behaviour on m/m/1 retrial queue with discouragement and working vacation. OPSEARCH, 1–28,

  • Roy, A., Pachuau, J.L., Singh, N.B., & Saha, A.K. (2024). Quantum inspired genetic algorithm and optimization of queuing delay. Tencon 2024-2024 ieee region 10 conference (tencon) (pp. 1929–1933).

  • Sampath, M. S., Sharma, D. K., & Gaur, A. (2023). Retrial queueing system subject to differentiated vacations with impatient customers. Applications of mathematical modeling, machine learning, and intelligent computing for industrial development (pp. 93–110). CRC Press.

  • Sanga, S. S., & Vankudothu, R. (2025). Modeling and optimization of an m/m/1/k/wv retrial queue with dual-phase service, customer’s balking under f-policy. RAIRO-Operations Research, 59(5), 2779–2803.

    Article  Google Scholar 

  • Shanmugam, B., M. C., & S. (2025). Unpredictable two-stage retrial model featuring impatient customers pinned bernoulli working vacations and starting failure. Annals of Operations Research, 1–30.

  • Squillante, M. S. (2022). On the quantum entanglement of random walks and queueing systems. Queueing Systems, 100(3), 253–255.

    Article  Google Scholar 

  • Stamatiou, G. T., & Magoutis, K. (2023). Quantum-enhanced control of a tandem queue system. Eai international conference on performance evaluation methodologies and tools (pp. 99–114)

  • Talbi, D., & Gal, Z. (2025). Higher-order markov model-based analysis of reinforcement learning in 6g mobile retrial queueing systems. Sensors, 25(23), 7245.

    Article  Google Scholar 

  • Talbi, D., & Gal, Z. (2025). Integrating reinforcement learning into m/m/1/k retry queueing models for 6g applications. Sensors, 25(12), 3621.

    Article  Google Scholar 

  • Talbi, D., & Gal, Z. (2025c). Reinforcement learning-enhanced retry queue system for unmanned systems in 6g networks. 2025 ieee international conference on unmanned systems (icus) (pp. 1242–1247).

  • Tao, M., Xu, J., Zhang, S., Tan, J., & Xu, J. (2024). Precision sensitivity optimization for parallel robotic system based on multi-source preventive maintenance. International Journal of Reliability, Quality and Safety Engineering, 31(05), 2450019.

    Article  Google Scholar 

  • Tian, N., & Zhang, Z.G. (2006). Vacation queueing models: theory and applications (Vol. 93). Springer Science & Business Media.

  • Trivedi, K. S., & Geist, R. M. (1983). Decomposition in reliability analysis of fault-tolerant systems. IEEE Transactions on Reliability, 32(5), 463–468.

    Article  Google Scholar 

  • Vishnevsky, V., Klimenok, V., Sokolov, A., & Larionov, A. (2021). Performance evaluation of the priority multi-server system mmap/ph/m/n using machine learning methods. Mathematics, 9(24), 3236.

    Article  Google Scholar 

  • Wang, L., Zhao, X., & Pham, H. (2025). Novel formulations and metaheuristic algorithms for predictive maintenance of aircraft engines with remaining useful life prediction. Reliability Engineering & System Safety, 261, Article 111064.

    Article  Google Scholar 

  • Wang, L., Zhu, Z., & Zhao, X. (2025). Dynamic predictive maintenance decisions based on system remaining useful life prediction and three inspection strategies. International Journal of Reliability, Quality and Safety Engineering, 32(04), 2550011.

    Article  Google Scholar 

  • Wilde, M. M. (2013). Quantum information theory. Cambridge University Press.

    Book  Google Scholar 

  • Zubeldia, M., Jhunjhunwala, P. R., & Maguluri, S. T. (2026). Matching queues with abandonments in quantum switches: Stability and throughput analysis. Operations Research, 74(1), 339–355.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Business Administration, Asia University, Taichung, Taiwan

    N. Micheal Mathavavisakan & Shey-Huei Sheu

  2. Department of Mathematics, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, 600089, India

    M. Lakshmi

  3. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Shey-Huei Sheu

Authors
  1. N. Micheal Mathavavisakan
  2. M. Lakshmi
  3. Shey-Huei Sheu

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Correspondence to Shey-Huei Sheu.

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Micheal Mathavavisakan, N., Lakshmi, M. & Sheu, SH. Machine learning-based performance prediction in quantum queueing: comparative evaluation of tree and neural models for fidelity, heat and latency. Ann Oper Res (2026). https://doi.org/10.1007/s10479-026-07421-5

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Machine learning-based performance prediction in quantum queueing: comparative evaluation of tree and neural models for fidelity, heat and latency