揭示量子机器学习中的独特偏差及其成因与应对策略
Identification and Mitigating Bias in Quantum Machine Learning
- 提出量子机器学习中特有的偏差识别框架
- 发现量子态初始化与测量导致的系统性偏差
- 为量子算法设计提供可解释性改进方向
随着量子机器学习（QML）作为量子计算与人工智能交叉领域的新兴方向，必须正视由量子系统独特性质引发的偏差与挑战。本文系统探讨了量子机器学习中偏差的识别、诊断与应对方法，聚焦三个核心问题：量子机器学习中的偏差呈现何种形态？其产生原因是什么？应如何应对？研究揭示了量子态初始化、测量过程及量子纠缠特性可能引入的系统性偏差，并提出了基于量子电路可解释性的诊断工具与校正机制。该工作为构建更可靠、公平的量子机器学习模型提供了理论基础与实践指导。
原文摘要 · Abstract (English)
As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and challenges that arise from the unique nature of quantum systems. This research includes work on identification, diagnosis, and response to biases in Quantum Machine Learning. This paper aims to provide an overview of three key topics: How does bias unique to Quantum Machine Learning look? Why and how can it occur? What can and should be done about it?
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