Quantum Policy Gradient Algorithm with Optimized Action Decoding
Nico Meyer, Daniel D. Scherer, Axel Plinge, Christopher Mutschler, Michael J. Hartmann
Abstract
Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on applications in quantum reinforcement learning, we propose an action decoding procedure for a quantum policy gradient approach. We introduce a quality measure that enables us to optimize the classical post-processing required for action selection, inspired by local and global quantum measurements. The resulting algorithm demonstrates a significant performance improvement in several benchmark environments. With this technique, we successfully execute a full training routine on a 5-qubit hardware device. Our method introduces only negligible classical overhead and has the potential to improve VQC-based algorithms beyond the field of quantum reinforcement learning.
BibTeX
@inproceedings{icml2023_quantumpolicygra,
title = {Quantum Policy Gradient Algorithm with Optimized Action Decoding},
author = {Nico Meyer and Daniel D. Scherer and Axel Plinge and Christopher Mutschler and Michael J. Hartmann},
booktitle = {ICML 2023},
year = {2023}
}