Quantum Machine Learning Applications to Medical Images: A Survey

Abstract

Classical deep learning achieves high accuracy in image analysis tasks but require large volume of data for generalisation. Medical image datasets are often small and expensive to annotate. Arguably, quantum machine learning (QML) has the following benefits over classical machine learning (ML). (i) Quantum superposition and entanglement allow quantum machines to excel over the computational competence of classical computers. (ii) By parallel processing, QML solves problems faster. (iii) QML produces promising results with limited-size image datasets and limited-parameter circuits. The practical advantages of QML over classical methods are still emerging and not consistent across all application domains. Ongoing research in quantum hardware and algorithms are expected to bridge this gap. In this review, we provide an outline of quantum neural networks, quantum convolution neural networks and various hybrid models characterised by continuous learning. We also explore human brain-inspired quantum neuromorphic computing using quantum spiking neural networks, characterised by learning from discrete neuromorphic spikes. We discuss quantum circuits, used for different medical image applications, from the perspectives of the circuit topology, the numbers of input and measurement qubits and rotation and entanglement gates. Furthermore, we conducted a systematic review of the literature on QML-based medical image applications, datasets and benchmarks, and the analysis of the research gap separately indicating possible improvement opportunities.