Quantum Reinforcement Learning

EasyChair Preprint 14862

Abstract

Quantum Reinforcement Learning (QRL) merges the principles of quantum computing with reinforcement learning (RL) to enhance the efficiency and effectiveness of decision-making processes. Traditional RL algorithms rely on classical computation to iteratively update policies based on interactions with an environment. However, these methods often struggle with scalability and computational limitations, especially in complex or high-dimensional spaces. QRL leverages quantum computing's ability to process information exponentially faster and handle large-scale problems more efficiently.

In QRL, quantum algorithms are used to represent and solve RL problems, utilizing quantum states and operations to perform policy evaluation and optimization. Quantum superposition and entanglement enable QRL to explore a broader range of strategies simultaneously, potentially accelerating learning rates and improving performance. Moreover, quantum advantage can be realized through enhanced exploration of state-action spaces and faster convergence to optimal policies.

This paper explores recent advancements in QRL, discussing theoretical foundations, algorithmic developments, and practical implementations. We also highlight key challenges, such as the integration of quantum hardware with RL frameworks and the development of scalable quantum algorithms. Future directions include investigating hybrid quantum-classical approaches and expanding QRL applications across various domains, from finance to robotics.

Keyphrases: Quantum Reinforcement Learning, entanglement, quantum algorithms, quantum superposition

@booklet{EasyChair:14862,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Reinforcement Learning},
  howpublished = {EasyChair Preprint 14862},
  year      = {EasyChair, 2024}}