In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This model is a symbiosis of a classical Convolutional NN that performs ultra-sound image processing and a quantum circuit that generates its stochastic weights, within a Bayesian learning framework. To test the utility of this idea for the possible future deployment in the medical sector we track multiple behavioral metrics that capture both predictive performance as well as model's uncertainty. It is our ambition to create a hybrid model that is capable to classify samples in a more uncertainty aware fashion, which will advance the trustworthiness of these models and thus bring us step closer to utilizing them in the industry. We test multiple setups for quantum circuit for this task, and our best architectures display bigger uncertainty gap between correctly and incorrectly identified samples than its classical benchmark at an expense of a slight drop in predictive performance. The innovation of this paper is two-fold: (1) combining of different approaches that allow the stochastic weights from the quantum circuit to be continues thus allowing the model to classify application-driven dataset; (2) studying architectural features of quantum circuit that make-or-break these models, which pave the way into further investigation of more informed architectural designs.

翻译：本研究提出了一种量子-经典贝叶斯神经网络（QCBNN），能够对经典医学数据集进行具有不确定性感知的分类。该模型是执行超声图像处理的经典卷积神经网络与在贝叶斯学习框架内生成随机权重的量子电路的共生体。为验证该思想在未来医疗领域部署的实用性，我们追踪了多个行为指标，这些指标既涵盖预测性能也捕捉模型的不确定性。我们的目标是构建一种能以更高不确定性感知方式分类样本的混合模型，这将提升这些模型的可信度，从而推动其在工业领域的实际应用。我们针对该任务测试了多种量子电路配置，其中最优架构在正确与错误识别样本之间展现出比经典基准更大的不确定性差距，尽管预测性能略有下降。本文的创新点体现在两方面：（1）结合多种方法实现量子电路生成连续随机权重，从而支持模型对实际应用驱动数据集进行分类；（2）研究决定量子电路架构成败的关键特征，为后续探索更具信息量的架构设计奠定基础。