Variational data encoding and correlations in quantum-enhanced machine learning
作者机构：Hubei Univ Sch Phys Wuhan 430068 Peoples R China Hubei Univ Technol Sch Sci Wuhan 430068 Peoples R China
出 版 物：《CHINESE PHYSICS B》 (中国物理B（英文版）)
年 卷 期：2024年第33卷第9期
页 面：298-306页
核心收录：
基 金：National Natural Science Foundation of China [12105090 12175057]
主 题：quantum machine learning variational data encoding quantum correlation 03.67.-a 03.67.Ac 03.65.Ud ALGORITHMS
摘 要：Leveraging the extraordinary phenomena of quantum superposition and quantum correlation, quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers. This paper tackles two pivotal challenges in the realm of quantum computing: firstly, the development of an effective encoding protocol for translating classical data into quantum states, a critical step for any quantum computation. Different encoding strategies can significantly influence quantum computer performance. Secondly, we address the need to counteract the inevitable noise that can hinder quantum acceleration. Our primary contribution is the introduction of a novel variational data encoding method, grounded in quantum regression algorithm models. By adapting the learning concept from machine learning, we render data encoding a learnable process. This allowed us to study the role of quantum correlation in data encoding. Through numerical simulations of various regression tasks, we demonstrate the efficacy of our variational data encoding, particularly post-learning from instructional data. Moreover, we delve into the role of quantum correlation in enhancing task performance, especially in noisy environments. Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference, thus advancing the frontier of quantum computing.
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