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Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search

Autor: Yu, Lian-Hui, Li, Xiao-Yu, Chen, Geng, Zhu, Qin-Sheng, Li, Hui, Yang, Guo-Wu

Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. Unlike previous methods that treat it as a static search process, from a perspective on QAS as an item retrieval task in vast search space, we decompose

Externí odkaz: http://arxiv.org/abs/2410.23560

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QUSL: Quantum Unsupervised Image Similarity Learning with Enhanced Performance

Autor: Yu, Lian-Hui, Li, Xiao-Yu, Chen, Geng, Zhu, Qin-Sheng, Li, Hui, Yang, Guo-Wu

Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current quantum schemes neglect adaptive adjustments for unsupervised task sc

Externí odkaz: http://arxiv.org/abs/2404.02028

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A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

Autor: Li, Xiao-Yu, Zhu, Qin-Sheng, Hu, Yong, Wu, Hao, Yang, Guo-Wu, Yu, Lian-Hui, Chen, Geng

Publikováno v: Quantum 8, 1232 (2024)

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we i

Externí odkaz: http://arxiv.org/abs/2307.08640

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Spin-orbit amplitudes for decays with arbitrary spin

Autor: Li, Xiao-yu, Dong, Xiang-Kun, Jing, Hao-Jie

Publikováno v: Nuclear Physics A (2023) 122761

In this paper, we propose a method to construct the decay amplitudes in the orbital ($L$) and spin ($S$) coupling scheme for particles with arbitrary spins. For the $1\to 2$ decay with only massive particles involved, the angular dependence is comple

Externí odkaz: http://arxiv.org/abs/2212.06417

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