Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
This work addresses the problem of improving computational efficiency in reinforcement learning for researchers in quantum machine learning, though it appears incremental as it builds on existing quantum principles.
The paper tackles reinforcement learning by developing a quantum framework that integrates Markov decision processes and quantum search algorithms, achieving quantum enhancement in computational efficiency for RL tasks.
This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov decision process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment interactions entirely within the quantum domain, eliminating reliance on classical computations. Key contributions include the quantum-based state transitions, return calculation, and trajectory search mechanism that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena. The implementation emphasizes the fundamental role of quantum superposition in enhancing computational efficiency for RL tasks. Results demonstrate the capacity of a quantum model to achieve quantum enhancement in RL, highlighting the potential of fully quantum implementations in decision-making tasks. This work not only underscores the applicability of quantum computing in machine learning but also contributes to the field of quantum reinforcement learning (QRL) by offering a robust framework for understanding and exploiting quantum computing in RL systems.