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AUTHOR(S): Eustache Muteba A., Nikos E. Mastorakis
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TITLE Enhancing Quantum Deep Q-Learning with Aspect-Oriented Programming: Cross-Cutting Optimization |
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ABSTRACT This paper proposes an enhancement to the Contract-based Quantum Deep Q-Learning (QDQL) model through the integration of Aspect-Oriented Programming (AOP), a paradigm that enables the clean separation of contract enforcement logic from the core learning agent. In this approach, aspects act as modular interceptors that transparently apply contracts (i.e., domain rules or constraints) during the agent’s decision-making process. To facilitate the structured and scalable integration of these enforcement mechanisms within the Quantum Deep Q-Learning architecture, the use of design patterns is introduced as a formal method for defining both the structural organization and behavioral interactions of system components. As a practical use case, the approach is applied to adaptive oncology treatment recommendation, where Aspect-Oriented Programming AOP provides a principled and modular means of enforcing critical clinical constraints, such as compliance with medical protocols, ethical standards, and patient-specific conditions, without tightly coupling them to the core learning algorithm. |
KEYWORDS Aspect-Oriented Programming, Cross-Cutting Concerns, design patterns Quantum Deep Q-Learning, Hybrid Systems, Oncology |
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Cite this paper Eustache Muteba A., Nikos E. Mastorakis. (2025) Enhancing Quantum Deep Q-Learning with Aspect-Oriented Programming: Cross-Cutting Optimization. International Journal of Computers, 10, 277-283 |
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