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Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

Quantum Contextual Bandits: Integrating Bandit Exploration into Quantum Neural Network

Tianyi Chen, Shiva Raj Pokhrel, Jiang Fang, Yinlong Liu, Jiyan Sun, Liru Geng, Gang Li

Published Oct 5, 2025• 0 citations
Publisher

Abstract

Supervised quantum learning methods face notable limitations in dynamic, real-world environments due to their reliance on static labels and limited adaptability. To address these challenges, we propose a novel online learning framework-Quantum Contextual Bandit (QCB)-that integrates quantum neural networks (QNNs) with contextual bandit (CB) algorithms. The QCB framework enables adaptive decision-making by incorporating bandit-based exploration into QNN training, making it particularly suitable for applications such as recommender systems and resource allocation in next-generation 5G/6G networks. To mitigate the adverse effects of quantum noise-including depolarizing, Pauli, and shot noise, the framework leverages a gradient-free optimization approach, enhancing robustness and convergence stability. Experimental results on various datasets demonstrate that QCB consistently outperforms traditional QNN training methods with identical circuit architectures. Notably, the model achieves over 99 % accuracy under ideal conditions and sustains high performance under noisy quantum environments. These results underscore the potential of QCB as a scalable, noise-resilient solution for adaptive learning in quantum machine learning systems.

DOI
10.1109/smc58881.2025.11342551

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Supervised quantum learning methods face notable limitations in dynamic, real-world environments due to their reliance on static labels and limited adaptability. To address these challenges, we propose a novel online learning framework-Quantum Contextual Bandit (QCB)-that integrates quantum neural networks (QNNs) with contextual bandit (CB) algorithms. The QCB framework enables adaptive decision-making by incorporating bandit-based exploration into QNN training, making it particularly suitable for applications such as recommender systems and resource allocation in next-generation 5G/6G networks. To mitigate the adverse effects of quantum noise-including depolarizing, Pauli, and shot noise, the framework leverages a gradient-free optimization approach, enhancing robustness and convergence stability. Experimental results on various datasets demonstrate that QCB consistently outperforms traditional QNN training methods with identical circuit architectures. Notably, the model achieves over 99 % accuracy under ideal conditions and sustains high performance under noisy quantum environments. These results underscore the potential of QCB as a scalable, noise-resilient solution for adaptive learning in quantum machine learning systems.
Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, computational instability, and the barren plateau phenomenon. This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm, a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations. By freezing the QNN at a random initialization and utilizing its static QNTK as a kernel for ridge regression, QNTK-UCB bypasses the unstable training dynamics inherent in explicit parameterized quantum circuit training while fully exploiting the unique quantum inductive bias. For a time horizon TTT and KKK actions, our theoretical analysis reveals a significantly improved parameter scaling of Ω((TK)3)Ω((TK)^3)Ω((TK)3) for QNTK-UCB, a substantial reduction compared to Ω((TK)8)Ω((TK)^8)Ω((TK)8) required by classical NeuralUCB algorithms for similar regret guarantees. Empirical evaluations on non-linear synthetic benchmarks and quantum-native variational quantum eigensolver tasks demonstrate QNTK-UCB's superior sample efficiency in low-data regimes. This work highlights how the inherent properties of QNTK provide implicit regularization and a sharper spectral decay, paving the way for achieving ``quantum advantage'' in online learning.
This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks (QNNs) in the Noisy Intermediate-Scale Quantum (NISQ) era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance QNN efficiency, security, and privacy. Specifically, established optimization strategies, including efficient parameter initialization, residual quantum circuit connections, and systematic quantum architecture exploration, are integrated to mitigate issues such as barren plateaus and error propagation. Moreover, the methodology incorporates current defensive mechanisms against adversarial attacks. Finally, Quantum Federated Learning (QFL) is adopted within this framework to facilitate privacy-preserving collaborative training across distributed quantum systems. Collectively, this synthesized approach seeks to enhance the robustness and real-world applicability of QNNs, laying the foundation for reliable quantum-enhanced machine learning applications in finance, healthcare, and cybersecurity.
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation methods, such as zero-noise extrapolation (ZNE), typically assume static noise, real noisy intermediate-scale quantum (NISQ) systems exhibit dynamic, time-varying noise that remains largely unaddressed. To overcome this critical gap, our work introduces a novel adaptive noise mitigation framework for VQCs that integrates ZNE with contextual multi-armed bandits (CMAB), enabling dynamic, context-aware selection of circuit-folding levels based on ansatz parameters (e.g., depth, parameter count) and the evolving noise environment. Unlike fixed-fold or heuristic ZNE, our approach uses online adaptation to improve the accuracy of ZNE and reduce redundant quantum circuit executions. Our extensive simulations and experiments on real quantum hardware reveal the following important properties: (i) deeper VQCs accumulate noise, degrading accuracy and increasing the number of quantum circuit executions; (ii) ZNE restores estimator fidelity when the folding level is chosen appropriately; and (iii) CMAB-guided folding cuts quantum circuit execution round trips by up to 40\%, bytes exchanged by up to 35\%, and end-to-end cost by up to 30\% under a 10~Mbps budget, with up to 6.9\% higher estimator fidelity (CIFAR-10, depth 3, noise band η=0.05η=0.05η=0.05), versus fixed-fold and grid-search ZNE. These results demonstrate substantial performance gains over existing noise mitigation methods, underscoring the effectiveness of our design in supporting robust noise mitigation for VQCs. The source code is also publicly released to support reproducibility.
Quantum Machine Learning (QML) presents a paradigm shift in computational paradigms, offering the potential for breakthroughs in various fields, including machine learning. This paper explores the application of Quantum Neural Networks (QNNs) to a novel adaptive optimization framework, termed "Quantum Adaptive Optimizer" (QAO). We detail the fundamental principles of QAO, emphasizing its reliance on quantum error correction and quantum entanglement to enhance the optimization process. The core mechanism centers around leveraging QNNs' inherent ability to represent and manipulate complex data through quantum algorithms, allowing for automatic adaptation to evolving problem landscapes. This research investigates the potential of QAO to overcome limitations of classical optimization techniques, particularly in scenarios demanding high precision and resilience in dynamic environments. The paper concludes with a discussion of the challenges and future directions of this innovative approach.
Quantum Neural Networks (QNNs) represent a promising paradigm in artificial intelligence, leveraging the unique properties of quantum mechanics to potentially surpass classical neural networks. This paper investigates a novel approach to QNNs – Adaptive Quantum Neural Network Adjustment – designed to dynamically optimize network parameters, thereby enhancing performance across a range of applications. We propose a mechanism that iteratively adjusts the network's weights based on feedback from a quantum-enhanced learning process, fostering a more robust and adaptable system. The paper details the architecture of the adaptive adjustment mechanism, explores its impact on network performance through simulations, and provides a preliminary analysis of its potential benefits. The core of the work centers around a novel quantum-inspired optimization strategy to address the limitations of traditional training methods. This research highlights the potential of incorporating adaptive adjustment into QNN architectures to achieve superior results.
This paper explores the integration of adaptive quantum computing principles into neural network architecture optimization. Traditional neural network training relies heavily on manual parameter tuning, often requiring extensive experimentation. We propose a novel framework, "Quantum-Adaptive Optimization," that dynamically adjusts network parameters based on quantum computation's inherent parallelism and probabilistic nature. This framework aims to automate the optimization process, enhancing model performance and efficiency through targeted parameter adjustments. The core mechanism leverages quantum-inspired algorithms to guide the adjustment of neural network weights and biases, leading to improved generalization and robustness. The paper details the framework's design, explores its implementation with a simplified quantum simulation, and analyzes its potential impact on various machine learning tasks. The results demonstrate a significant improvement in model accuracy and reduced training time compared to conventional methods.