Scientific Results

Calibration of Quantum Sensors by Neural Networks

Year: 2019

Authors: Cimini V., Gianani I., Spagnolo N., Leccese F., Sciarrino F., Barbieri M.

Autors Affiliation: Univ Roma Tre, Dipartimento Sci, Via Vasca Navale 84, I-00146 Rome, Italy; Sapienza Univ Roma, Dipartimento Fis, Piazzale Aldo Moro 5, I-00185 Rome, Italy; CNR, ISC, Consiglio Nazl Ric, Via Taurini 19, I-00185 Rome, Italy; CNR, Ist Nazl Ott, Largo Enrico Fermi 6, I-50125 Florence, Italy

Abstract: Introducing quantum sensors as a solution to real world problems demands reliability and controllability outside of laboratory conditions. Producers and operators ought to be assumed to have limited resources readily available for calibration, and yet, they should be able to trust the devices. Neural networks are almost ubiquitous for similar tasks for classical sensors: here we show the applications of this technique to calibrating a quantum photonic sensor. This is based on a set of training data, collected only relying on the available probe states, hence reducing overhead. We found that covering finely the parameter space is key to achieving uncertainties close to their ultimate level. This technique has the potential to become the standard approach to calibrate quantum sensors.

Journal/Review: PHYSICAL REVIEW LETTERS

Volume: 122 (23)      Pages from: 230502-1  to: 230502-6

KeyWords: TOMOGRAPHY
DOI: 10.1103/PhysRevLett.123.230502

Citations: 2
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