用量子核方法分类神经元形态，首次在真实数据上验证量子优势潜力。
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
- 采用量子核算法处理神经元形态特征，实现多类分类。
- 在真实数据上表现与经典方法相当，部分配置更优。
- 为神经科学中的自动分类提供新思路，适合量子计算初学者参考。
不同神经元类型的功能表征是长期存在的关键挑战。随着物理量子计算机的出现，量子机器学习算法有望将理论研究转化为实际解决方案。以往研究显示量子算法在人工生成数据上具有优势，小规模二分类实验结果与经典算法相当。然而，使用真实世界数据检验量子优势至关重要。据我们所知，本研究首次提出利用量子系统对神经元形态进行分类，以提升自动多类神经元分类中量子核方法的性能。我们考察了特征工程对分类准确率的影响，发现量子核方法表现与经典方法相似，在多种配置下展现出一定优势。
原文摘要 · Abstract (English)
The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using real-world data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods. We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations.
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