用进化算法找最优量子-经典强化学习架构，发现经典模型仍更优。
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm
- 用正则化进化搜索量子-经典混合架构
- 最佳混合模型排第11，经典模型整体领先
- 分析性能差异，提炼高效混合设计原则
近期量子机器学习研究提倡使用混合模型以应对当前噪声中等规模量子（NISQ）设备的局限性，但多数研究缺乏对所选架构选择的解释与区分优质与劣质混合架构的标准。本研究通过正则化进化算法，为著名的近端策略优化（PPO）强化学习算法搜索最优的量子-经典混合架构。最终结果显示，经典模型在所有唯一模型中占据主导地位，最佳混合模型仅位列第十一。同时，本文尝试解释导致该结果的因素，分析不同模型表现差异的原因，旨在深化对高效混合架构设计准则的理解。
原文摘要 · Abstract (English)
Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of them was the explanations and interpretations of the choices that were made to pick those exact architectures and the differentiation between good and bad hybrid architectures, this research attempts to tackle that gap in the literature by using the Regularized Evolution algorithm to search for the optimal hybrid classical-quantum architecture for the Proximal Policy Optimization (PPO) algorithm, a well-known reinforcement learning algorithm, ultimately the classical models dominated the leaderboard with the best hybrid model coming in eleventh place among all unique models, while we also try to explain the factors that contributed to such results,and for some models to behave better than others in hope to grasp a better intuition about what we should consider good practices for designing an efficient hybrid architecture.
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