Quantum framework for Reinforcement Learning: integrating Markov Decision Process, quantum arithmetic, and trajectory search

2024-12-24

AI Summary

This work addresses the efficiency bottlenecks of classical reinforcement learning (RL) in modeling Markov decision processes (MDPs) and searching optimal trajectories. Methodologically, it introduces the first end-to-end fully quantum RL framework: MDPs are entirely mapped onto the quantum domain, with states and actions encoded as quantum superpositions; state transitions are implemented via parameterized quantum circuits; reward evaluation and optimal trajectory search are performed natively on quantum hardware using quantum phase estimation and amplitude amplification—without classical intervention; and a hyper-permutation mechanism is proposed to accelerate policy-space exploration. The core contribution is the pioneering “quantum-superposition-driven RL paradigm,” enabling purely quantum execution of state evolution, reward assessment, and policy optimization. Experiments demonstrate significant improvements over classical baselines in both policy convergence speed and sample efficiency, providing the first empirical validation of full quantum RL feasibility and its quantum advantage.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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. Experimental results demonstrate the capacity of a quantum model to achieve quantum advantage 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 the field of quantum reinforcement learning (QRL) by offering a robust framework for understanding and exploiting quantum computing in RL systems.

Problem

• Quantum Arithmetic
• Reinforcement Learning
• Markov Decision Processes

Innovation

• Quantum Arithmetic Enhanced Reinforcement Learning
• Quantum Markov Decision Process
• Trajectory Search Method