Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement

The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of inter...

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Main Authors: Krisnanda, Tanjung, Paterek, Tomasz, Paternostro, Mauro, Liew, Timothy Chi Hin
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/169936
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1699362023-08-21T15:42:50Z Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement Krisnanda, Tanjung Paterek, Tomasz Paternostro, Mauro Liew, Timothy Chi Hin School of Physical and Mathematical Sciences Centre for Quantum Technologies, NUS MajuLab, International Joint Research Unit UMI 3654, CNRS Science::Physics Gravity-Induced Entanglement Neuromorphic The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is trained to learn and sense the entanglement of the former. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognise quantum states, it is possible to sense the amount of entanglement after training with only non-entangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilise our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques. Ministry of Education (MOE) Published version T. K. and T. C. H. L. acknowledge the support by the Singapore Ministry of Education under its AcRF Tier 2 Grant No. T2EP50121-0006. T. P. is supported by the Polish National Agency for Academic Exchange NAWA Project No. PPN/PPO/2018/1/00007/U/ 00001 and Xiamen University Malaysia Research Fund (Grant No. XMUMRF/2022-C10/IPHY/0002). M. P. acknowledges the support by the European Union’s Horizon 2020 FET-Open project TEQ (766900), the Leverhulme Trust Research Project Grant UltraQuTe (Grant No. RGP-2018-266), the Royal Society Wolfson Fellowship (RSWF/R3/183013), the UK EPSRC (EP/ T028424/1), the Department for the Economy Northern Ireland under the US-Ireland R&D Partnership Programme, and the Horizon Europe EIC-Pathfinder project QuCoM (101046973). 2023-08-15T06:30:52Z 2023-08-15T06:30:52Z 2023 Journal Article Krisnanda, T., Paterek, T., Paternostro, M. & Liew, T. C. H. (2023). Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement. Physical Review D, 107(8), 086014-1-086014-14. https://dx.doi.org/10.1103/PhysRevD.107.086014 2470-0010 https://hdl.handle.net/10356/169936 10.1103/PhysRevD.107.086014 2-s2.0-85159692693 8 107 086014-1 086014-14 en T2EP50121-0006 Physical Review D © 2023 American Physical Society. All rights reserved. This paper was published in Physical Review D and is made available with permission of American Physical Society. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Physics
Gravity-Induced Entanglement
Neuromorphic
spellingShingle Science::Physics
Gravity-Induced Entanglement
Neuromorphic
Krisnanda, Tanjung
Paterek, Tomasz
Paternostro, Mauro
Liew, Timothy Chi Hin
Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
description The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is trained to learn and sense the entanglement of the former. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognise quantum states, it is possible to sense the amount of entanglement after training with only non-entangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilise our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Krisnanda, Tanjung
Paterek, Tomasz
Paternostro, Mauro
Liew, Timothy Chi Hin
format Article
author Krisnanda, Tanjung
Paterek, Tomasz
Paternostro, Mauro
Liew, Timothy Chi Hin
author_sort Krisnanda, Tanjung
title Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
title_short Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
title_full Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
title_fullStr Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
title_full_unstemmed Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
title_sort quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
publishDate 2023
url https://hdl.handle.net/10356/169936
_version_ 1779156288577470464