TY - JOUR
T1 - Quantum-Enhanced Barcode Decoding and Pattern Recognition
AU - Banchi, Leonardo
AU - Zhuang, Quntao
AU - Pirandola, Stefano
N1 - Publisher Copyright:
© 2020 American Physical Society.
PY - 2020/12/8
Y1 - 2020/12/8
N2 - 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 multipartite 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 a 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.
AB - 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 multipartite 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 a 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.
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U2 - 10.1103/PhysRevApplied.14.064026
DO - 10.1103/PhysRevApplied.14.064026
M3 - Article
AN - SCOPUS:85097585346
SN - 2331-7019
VL - 14
JO - Physical Review Applied
JF - Physical Review Applied
IS - 6
M1 - 064026
ER -