By the same authors

From the same journal

Quantum-enhanced barcode decoding and pattern recognition

Research output: Contribution to journalArticlepeer-review

Full text download(s)


Published copy (DOI)



Publication details

JournalPhysical Review Applied
DateAccepted/In press - 11 Nov 2020
DatePublished (current) - 8 Dec 2020
Issue number6
Number of pages17
Original languageEnglish


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.

Bibliographical note

17 pages, 4 figures.
© 2020 American Physical Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

    Research areas

  • quant-ph, cs.LG, physics.optics, stat.ML

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations