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<p>Quantum computing holds significant potential to enhance and accelerate diagnostic pathways in medical image processing by advancing the capabilities for high-dimensional data analytics and complex pattern recognition. However, the early developmental stage of quantum hardware and algorithms presents a significant challenge for translation to clinical practice, especially the limited numbers of qubits, noisy devices, and integration of non-quantum data. This review explores and evaluates recent developments related to quantum computing methods to diagnose neurological diseases. The studies reviewed are categorized based on their algorithmic framework such as variational quantum circuits, quantum kernel methods, hybrid quantum-classical architectures, and quantum-inspired optimization algorithms. These new approaches offer innovation within techniques for encoding and processing neuroimaging and electrophysiological data, improving classification accuracy and computational efficiency. The review reflects all major findings and describes how these quantum approaches enhance diagnostic precision and operational throughput in real‑world settings. To the best of our knowledge, this is the first comprehensive review of quantum computing methods for diagnosing neurological diseases. This study presents existing techniques, highlights their strengths and limitations, and outlines future research directions to overcome current hardware and integration challenges.</p>

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A Review of Simulated Quantum Computing Applications in Neurological Disease Detection and Classification: Methods and Perspectives

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • S. Alden Jenish,
  • S. Keerthana,
  • R. Karthik

摘要

Quantum computing holds significant potential to enhance and accelerate diagnostic pathways in medical image processing by advancing the capabilities for high-dimensional data analytics and complex pattern recognition. However, the early developmental stage of quantum hardware and algorithms presents a significant challenge for translation to clinical practice, especially the limited numbers of qubits, noisy devices, and integration of non-quantum data. This review explores and evaluates recent developments related to quantum computing methods to diagnose neurological diseases. The studies reviewed are categorized based on their algorithmic framework such as variational quantum circuits, quantum kernel methods, hybrid quantum-classical architectures, and quantum-inspired optimization algorithms. These new approaches offer innovation within techniques for encoding and processing neuroimaging and electrophysiological data, improving classification accuracy and computational efficiency. The review reflects all major findings and describes how these quantum approaches enhance diagnostic precision and operational throughput in real‑world settings. To the best of our knowledge, this is the first comprehensive review of quantum computing methods for diagnosing neurological diseases. This study presents existing techniques, highlights their strengths and limitations, and outlines future research directions to overcome current hardware and integration challenges.