Optimizing CAR T cell design using quantum convolutional neural networks

Abstract

Chimeric antigen receptor (CAR) T-cells are promising new medicines that apply to the treatment of many cancers, and potentially represent novel approaches to treat infectious diseases and autoimmunity. A central challenge in expanding and enhancing CAR T-cell functions is identifying beneficial combinations of intracellular costimulatory motifs to elicit desired phenotypes. This is due to the large motif combinatorial space and high experimental costs, in time and resources, to generate and measure CAR performance. This results in a highly data-constrained problem where developing accurate predictive models of CAR T-cell behavior is difficult. State-of-the-art machine learning (ML) models based on convolutional neural networks (CNNs) combined with long-short term memory have been shown to reach an accuracy of 70% when predicting CAR T-cell stemness and cytotoxicity. With the clear need for more accurate predictive models, we investigated the performance of quantum convolutional neural networks (QCNNs) as a novel ML model for improved predictive performance. QCNNs carry advantages over classical CNNs, including good generalization in underdetermined problems and usage of significantly fewer training parameters as these scale logarithmically with the number of qubits. QCNNs also have advantages over certain other quantum ML methods, including the absence of barren plateaus. Our study showed that QCNN matches and occasionally exceeds the performance of CNNs in classifying CAR T cells by cytotoxicity levels. Employing larger and more expressive QCNN models may further enhance performance, potentially resulting in a superior predictive tool for CAR T-cell phenotypes.