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PPO-Q: Proximal Policy Optimization with Parametrized Quantum Policies or Values by Jin, Yu-Xin; Wang, Zi-Wei; Xu, Hong-Ze; Zhuang, Wei-Feng; Hu, Meng-Jun; Liu, Dong E. is a scholarly article available to read on EtoBox.

What is PPO-Q: Proximal Policy Optimization with Parametrized Quantum Policies or Values about?

Quantum machine learning (QML), which combines quantum computing with machine learning, is widely believed to hold the potential to outperform traditional machine learning in the era of noisy intermediate-scale quantum (NISQ). As one of the most important types of QML, quantum reinforcement learning (QRL) with parameterized quantum circuits as agents has received extensive attention in the past few years. Various algorithms and techniques have been introduced, demonstrating the effectiveness of QRL in solving some popular benchmark environments such as CartPole, FrozenLake, and MountainCar. However, tackling more complex environments with continuous action spaces and high-dimensional state spaces remains challenging within the existing QRL framework. Here we present PPO-Q, which, by integrating hybrid quantum-classical networks into the actor or critic part of the proximal policy optimization (PPO) algorithm, achieves state-of-the-art performance in a range of complex environments with significantly reduced training parameters. The hybrid quantum-classical networks in the PPO-Q incorporate two additional traditional neural networks to aid the parameterized quantum circuits in manag

Author

Jin, Yu-Xin; Wang, Zi-Wei; Xu, Hong-Ze; Zhuang, Wei-Feng; Hu, Meng-Jun; Liu, Dong E.

Published

2025

Language

EN