Variational Spin-Squeezing Algorithms on Programmable Quantum Sensors

Raphael Kaubruegger, Pietro Silvi, Christian Kokail, Rick van Bijnen, Ana Maria Rey, Jun Ye, Adam M. Kaufman, and Peter Zoller
Phys. Rev. Lett. 123, 260505 – Published 30 December 2019
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Abstract

Arrays of atoms trapped in optical tweezers combine features of programmable analog quantum simulators with atomic quantum sensors. Here we propose variational quantum algorithms, tailored for tweezer arrays as programmable quantum sensors, capable of generating entangled states on demand for precision metrology. The scheme is designed to generate metrological enhancement by optimizing it in a feedback loop on the quantum device itself, thus preparing the best entangled states given the available quantum resources. We apply our ideas to the generation of spin-squeezed states on Sr atom tweezer arrays, where finite-range interactions are generated through Rydberg dressing. The complexity of experimental variational optimization of our quantum circuits is expected to scale favorably with system size. We numerically show our approach to be robust to noise, and surpassing known protocols.

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  • Received 2 September 2019

DOI:https://doi.org/10.1103/PhysRevLett.123.260505

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Atomic, Molecular & OpticalQuantum Information, Science & Technology

Authors & Affiliations

Raphael Kaubruegger1,2, Pietro Silvi1,2, Christian Kokail1,2, Rick van Bijnen1,2, Ana Maria Rey3,4, Jun Ye3, Adam M. Kaufman3, and Peter Zoller1,2

  • 1Center for Quantum Physics, University of Innsbruck, Innsbruck A-6020, Austria
  • 2Institute for Quantum Optics and Quantum Information of the Austrian Academy of Sciences, Innsbruck A-6020, Austria
  • 3JILA, National Institute of Standards and Technology and University of Colorado and Department of Physics, University of Colorado, Boulder, Colorado 80309, USA
  • 4Center for Theory of Quantum Matter, University of Colorado, Boulder, Colorado 80309, USA

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Issue

Vol. 123, Iss. 26 — 31 December 2019

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