• Open Access

Detecting and tracking bacteria with quantum light

Gaetana Spedalieri, Lolita Piersimoni, Omar Laurino, Samuel L. Braunstein, and Stefano Pirandola
Phys. Rev. Research 2, 043260 – Published 19 November 2020

Abstract

The field of quantum sensing aims at improving the detection and estimation of classical parameters that are encoded in physical systems by resorting to quantum sources of light and quantum detection strategies. The same approach can be used to improve the current classical measurements that are performed on biological systems. Here we consider the scenario of two bacteria (E. coli and Salmonella) growing in a Luria-Bertani broth and monitored by classical spectrophotometers. Their concentration can be related to the optical transmissivity via the Beer-Lambert-Bouguer's law and their growth curves can be described by means of Gompertz functions. Starting from experimental data points, we extrapolate the growth curves of the two bacteria and we study the theoretical performance that would be achieved with a quantum setup. In particular, we discuss how the bacterial growth can in principle be tracked by irradiating the samples with orders of magnitude fewer photons, identifying the clear superiority of quantum light in the early stages of growth. We then show the superiority and the limits of quantum resources in two basic tasks: (i) the early detection of bacterial growth and (ii) the early discrimination between two bacteria species.

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  • Received 27 June 2020
  • Accepted 27 October 2020

DOI:https://doi.org/10.1103/PhysRevResearch.2.043260

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyPhysics of Living SystemsInterdisciplinary Physics

Authors & Affiliations

Gaetana Spedalieri1,2, Lolita Piersimoni3,4, Omar Laurino5, Samuel L. Braunstein1, and Stefano Pirandola1

  • 1Computer Science, University of York, York YO10 5GH, United Kingdom
  • 2Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 3Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle 06120, Germany
  • 4Biological Chemistry, Medical School, University of Michigan, Ann Arbor, Michigan 48109, USA
  • 5Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, Massachusetts 02138, USA

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Issue

Vol. 2, Iss. 4 — November - December 2020

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