Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts -- which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts -- therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.

翻译：量子机器学习目前受到很大关注,但与古典机器学习技术相比,它对于实际应用的实用应用的用处仍然不清楚,但是,有迹象表明,某些量子机器学习算法可能会提高与其古典对等机构的培训能力 -- -- 在缺乏培训数据的情况下,这可能特别有益 -- -- 这些情况自然出现在医疗分类任务中。在本文件中,提出了不同混合量子古典神经网络,其量子电路设计和编码技术各不相同。它们适用于二维和三维医学成像数据,例如,具有不同、潜在坏坏坏的、计算成像扫描中的损伤的数据。这些QCCNN的性能已经类似于其古典对等机构,因此鼓励进一步研究将这些算法应用于医学成像任务的方向。