Quantum Policy Gradient Algorithm with Optimized Action Decoding

Understand

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 a specific action decoding procedure for a quantum policy gradient approach.
• We introduce a novel 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.