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Optimization of Reinforcement Learning Using Quantum Computation
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DOI：10.1109/ACCESS.2024.3506656.png)
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Abstract
En 中文
Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL's potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.
Keywords:
Quantum computing
Qubit
Quantum circuit
Optimization
Training
Q-learning
Logic gates
Encoding
Decision making
Computers
Advantage actor critic
deep Q network
policy gradient
Q learning
quantum approximate optimization algorithm
quantum computing
reinforcement learning
variational quantum circuit
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
Efficient Dimensionality Reduction Strategies for Quantum Reinforcement Learning.png)
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