Scholarlink

<p>Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A large scale statistical analysis of quantum and classical neural networks in the medical domain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月9日

查看全文
  • Francesco Ghisoni,
  • Matteo Borrotti,
  • Paolo Mariani

摘要

Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.