Recently, research at the intersection of quantum mechanics and machine learning has gained attention. This interdisciplinary field aims to tackle the computational efficiency of machine learning by leveraging quantum computing and to derive novel machine learning algorithms inspired by quantum principles. Despite substantial progress in quantum science research, several challenges persist, including the preservation of quantum coherence, mitigation of environmental constraints, advancing quantum computer development, and formulating comprehensive quantum machine learning algorithms. To date, a comprehensive theoretical framework for quantum machine learning is lacking, with much of the research still in the exploratory and experimental stages. This study conducts a thorough survey on quantum machine learning, with the aim of classifying quantum machine learning algorithms while addressing the existing challenges and potential solutions in this emerging field.

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量子机器学习：分类、挑战和解决方案


最近，量子力学和机器学习交叉领域的研究引起了人们的关注。这个跨学科领域旨在通过利用量子计算来解决机器学习的计算效率问题，并衍生受量子原理启发的新型机器学习算法。尽管量子科学研究取得了重大进展，但仍然存在一些挑战，包括保持量子相干性、减轻环境限制、推进量子计算机开发以及制定全面的量子机器学习算法。迄今为止，量子机器学习缺乏全面的理论框架，大部分研究仍处于探索和实验阶段。本研究对量子机器学习进行了全面调查，旨在对量子机器学习算法进行分类，同时解决这一新兴领域的现有挑战和潜在解决方案。