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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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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