Benchmarking Quantum Reinforcement Learning

2025-01-27

AI Summary

Quantum reinforcement learning (QRL) lacks statistically rigorous performance evaluation standards, leading to unsubstantiated claims of quantum advantage. Method: We propose the first statistically principled benchmarking framework for QRL, centered on sample complexity estimation and hypothesis testing to formally define “statistically significant superiority.” We design a suite of multi-scale QRL environments with tunable complexity and conduct systematic comparisons among classical RL baselines (DQN, PPO) and state-of-the-art QRL algorithms. Contribution/Results: Empirical evaluation reveals that QRL achieves only limited, task-specific statistical advantages—most existing claims of quantum superiority fail standard significance tests. This work fills a critical methodological gap in QRL assessment and establishes a reproducible, scalable statistical benchmark for objectively quantifying quantum gains.

Technology Category

• Machine Learning: Quantum Machine Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements

Abstract

Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.

Problem

• Quantum Reinforcement Learning
• Performance Evaluation
• Statistical Methods

Innovation

• Quantum Reinforcement Learning
• Sample Complexity
• Performance Evaluation