Quantum Policy Gradient Algorithm with Optimized Action Decoding

• URL: http://arxiv.org/abs/2212.06663v2
• Date: Mon, 22 May 2023 14:07:04 GMT
• Title: Quantum Policy Gradient Algorithm with Optimized Action Decoding
• Authors: Nico Meyer, Daniel D. Scherer, Axel Plinge, Christopher Mutschler, and Michael J. Hartmann
• Abstract summary: We introduce a novel quality measure that enables us to optimize the classical post-processing required for action selection. With this technique, we successfully execute a full training routine on a 5-qubit hardware device.
• Score: 1.3946033794136758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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. 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.

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