Quantum-enhanced barcode decoding and pattern recognition

TL;DR

This paper demonstrates that quantum entangled sources and measurements significantly outperform classical methods in barcode decoding and pattern recognition, with theoretical bounds and numerical verification for handwritten digit classification.

Contribution

It generalizes quantum hypothesis testing to multi-partite settings, introducing quantum-enhanced techniques for barcode decoding and pattern recognition, and compares their effectiveness to classical strategies.

Findings

Quantum entanglement improves barcode decoding accuracy.

Pattern recognition is easier with larger Hamming distances between classes.

Quantum sensors outperform classical methods in handwritten digit classification.

Abstract

Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.