Abstract 4373117: Quantum Computing based Echocardiographic Diagnosis and Analysis in Congenital Heart Disease: Feasibility and Superiority to conventional Deep Learning Approaches
Abstract
Background:
Quantum computing (QC) has emerged as an innovative technology to enhance machine learning through quantum mechanical properties such as superposition, entanglement, and projection into high-dimensional complex Hilbert spaces. These properties can improve the representational capacity and generalization of deep learning models. We explore the feasibility and efficacy of integrating quantum technology into convolutional neural networks (CNN) for echocardiographic analysis of real world data covering the spectum of congenital heart disease (CHD).
Methods:
We developed a hybrid deep learning algorithm incorporating a quantum computing (QC) layer within a ResNet-50 convolutional neural network. The QC layer included 4 qubits, with angle embedding and strongly entangling layers for quantum processing. Two supervised classification tasks were evaluated: (1) diagnosis classification and (2) echocardiographic view classification using a challenging dataset of echocardiographic images from patients with congenital and structural heart disease. The model was benchmarked against a conventional state-of-the art CNN model. Training/inference were conducted on high-performance classical GPUs and the QC layer were simulated and applied directly to an IBM 127-qubit Eagle r3 quantum processor (Sherbrooke, Canada).
Results:
Models were trained and tested on echocardiographic data derived from 262 patients and 62 controls. Diagnoses included tetralogy of Fallot (n=30), TGA (n=48),…
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