为经典临床数据集构建连续量子-经典贝叶斯神经网络
Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset
AI总结：
本文提出量子-经典贝叶斯神经网络QCBNN，将经典卷积神经网络与生成连续随机权重的量子电路结合，用于医学超声图像的不确定性感知分类，在预测性能略降的情况下获得比经典基准更大的不确定性差距。
摘要：
在这项工作中，我们提出了一种量子-经典贝叶斯神经网络（QCBNN），它能够对经典医学数据集进行不确定性感知分类。该模型是经典卷积神经网络与量子电路的共生体：经典卷积神经网络执行超声图像处理，量子电路在贝叶斯学习框架内生成其随机权重。为了检验这一想法未来在医疗领域部署的实用性，我们跟踪了多个行为指标，这些指标同时捕捉预测性能和模型的不确定性。我们的目标是创建一个能够以更具不确定性感知的方式对样本进行分类的混合模型，这将提升这些模型的可信度，从而使我们离在工业界应用它们更近一步。我们针对该任务测试了多种量子电路设置，我们最好的架构在正确识别样本与错误识别样本之间显示出比经典基准更大的不确定性差距，代价是预测性能略有下降。本文的创新有两点：（1）结合不同方法，使来自量子电路的随机权重具有连续性，从而让模型能够对应用驱动的数据集进行分类；（2）研究决定这些模型成败的量子电路架构特征，为进一步探索更具信息量的架构设计铺平道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
