Literature DB >> 35322252

Optimal metrology with programmable quantum sensors.

Christian D Marciniak1, Thomas Feldker1, Ivan Pogorelov1, Raphael Kaubruegger2,3, Denis V Vasilyev2,3, Rick van Bijnen2,3, Philipp Schindler1, Peter Zoller2,3, Rainer Blatt1,2, Thomas Monz4,5.   

Abstract

Quantum sensors are an established technology that has created new opportunities for precision sensing across the breadth of science. Using entanglement for quantum enhancement will allow us to construct the next generation of sensors that can approach the fundamental limits of precision allowed by quantum physics. However, determining how state-of-the-art sensing platforms may be used to converge to these ultimate limits is an outstanding challenge. Here we merge concepts from the field of quantum information processing with metrology, and successfully implement experimentally a programmable quantum sensor operating close to the fundamental limits imposed by the laws of quantum mechanics. We achieve this by using low-depth, parametrized quantum circuits implementing optimal input states and measurement operators for a sensing task on a trapped-ion experiment. With 26 ions, we approach the fundamental sensing limit up to a factor of 1.45 ± 0.01, outperforming conventional spin-squeezing with a factor of 1.87 ± 0.03. Our approach reduces the number of averages to reach a given Allan deviation by a factor of 1.59 ± 0.06 compared with traditional methods not using entanglement-enabled protocols. We further perform on-device quantum-classical feedback optimization to 'self-calibrate' the programmable quantum sensor with comparable performance. This ability illustrates that this next generation of quantum sensor can be used without previous knowledge of the device or its noise environment.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Entities:  

Year:  2022        PMID: 35322252     DOI: 10.1038/s41586-022-04435-4

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   69.504


  29 in total

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Journal:  Nature       Date:  2021-06-09       Impact factor: 49.962

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