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• W4313432981 abstract "Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical systems, e.g., deep neural networks. While existing work demonstrates and exploits the strength of PQC-based models, the design choices of PQC architectures and the interactions between different quantum circuits on learning tasks are generally underexplored. In this work, we introduce a Multi-objective Evolutionary Architecture Search framework for parameterized quantum circuits (MEAS-PQC), which uses a multi-objective genetic algorithm with quantum-specific configurations to perform efficient searching of optimal PQC architectures. Experimental results show that our method can find architectures that have superior learning performance on three benchmark RL tasks, and are also optimized for additional objectives including reductions in quantum noise and model size. Further analysis of patterns and probability distributions of quantum operations helps identify performance-critical design choices of hybrid quantum-classical learning systems." @default.
• W4313432981 created "2023-01-06" @default.
• W4313432981 creator A5044142640 @default.
• W4313432981 creator A5074556234 @default.
• W4313432981 date "2023-01-03" @default.
• W4313432981 modified "2023-10-17" @default.
• W4313432981 title "Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits" @default.
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• W4313432981 doi "https://doi.org/10.3390/e25010093" @default.
• W4313432981 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36673234" @default.
• W4313432981 hasPublicationYear "2023" @default.
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