Exploring polymer classification with a hybrid single-photon quantum approach

TL;DR

This paper introduces a hybrid classical-quantum approach combining deep learning and photonic quantum computing to classify polymers based on their optical properties, showing promising results on current NISQ devices.

Contribution

It presents a novel hybrid framework integrating classical neural networks with a single-photon quantum classifier for polymer classification.

Findings

Successful classification of polymers by optical gap.

Performance comparable to CPU-based noisy simulations.

Proof-of-concept run on a photonic quantum processor.

Abstract

Polymers exhibit complex architectures and diverse properties that place them at the center of contemporary research in chemistry and materials science. As conventional computational techniques, even multi-scale ones, struggle to capture this complexity, quantum computing offers a promising alternative framework for extracting structure-property relationships. Noisy Intermediate-Scale Quantum (NISQ) devices are commonly used to explore the implementation of algorithms, including quantum neural networks for classification tasks, despite ongoing debate regarding their practical impact. We present a hybrid classical-quantum formalism that couples a classical deep neural network for polymer featurization with a single-photon-based quantum classifier native to photonic quantum computing. This pipeline successfully classifies polymer species by their optical gap, with performance in line between CPU-based noisy simulations and a proof-of-principle run on Quandela's Ascella quantum processor. These findings demonstrate the effectiveness of the proposed computational workflow and indicate that chemistryfrelated classification tasks can already be tackled under the constraints of today's NISQ devices.