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

TL;DR

This paper introduces a hybrid Quantum-Classical Bayesian Neural Network for medical data classification that enhances uncertainty estimation, potentially increasing trustworthiness for clinical applications.

Contribution

It presents a novel hybrid model combining classical CNNs with quantum circuits for stochastic weights and explores architectural features influencing performance.

Findings

Quantum circuit-based stochastic weights improve uncertainty estimation.

Best architectures show larger uncertainty gaps between correct and incorrect classifications.

Slight decrease in predictive accuracy with increased uncertainty awareness.

Abstract

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.