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A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
This study investigates the effects of various techniques -- data re-uploading, input scaling, output scaling -- and introduces exponential learning rate decay in the quantum proximal policy optimization algorithm's actor-VQC.
Weight Re-Mapping for Variational Quantum Algorithms
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications.
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