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.

这篇论文介绍了一种量子 - 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意识的分类。该模型是经典卷积神经网络和生成随机权重的量子电路在贝叶斯学习框架中的协同作用。通过跟踪多个行为度量指标，本研究的目标是创建一个混合模型，能够以更具不确定性的方式对样本进行分类，从而提高这些模型的可信度，并使其更接近工业应用。通过对量子电路的多种设置进行测试，得出最佳架构在正确和错误样本之间显示出更大的不确定性差距，同时稍微降低了预测性能。该论文的创新点有两个：(1) 结合不同方法，使量子电路的随机权重能够连续化，从而使模型能够对应用驱动的数据集进行分类；(2) 研究量子电路的架构特征对这些模型的影响，为进一步研究更加明智的架构设计铺平了道路。

构建连续的量子经典贝叶斯神经网络用于一个经典临床数据集

Building Continuous Quantum-Classical Bayesian Neural Networks for a  Classical Clinical Dataset

We present a general framework for classification of sparse and
irregularly-sampled time series. The properties of such time series can result
in substantial uncertainty about the values of the underlying temporal
processes, while making the data difficult to deal with using standard
classification methods that assume fixed-dimensional feature spaces. To address
these challenges, we propose an uncertainty-aware classification framework
based on a special computational layer we refer to as the Gaussian process
adapter that can connect irregularly sampled time series data to any black-box
classifier learnable using gradient descent. We show how to scale up the
required computations based on combining the structured kernel interpolation
framework and the Lanczos approximation method, and how to discriminatively
train the Gaussian process adapter in combination with a number of classifiers
end-to-end using backpropagation.

提出了一种基于高斯过程适配器的不确定性感知分类框架，通过结构化内核插值框架和 Lanczos 逼近方法进行计算。