Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

ORAL

Abstract

Quantum sensing promises to revolutionize sensing applications by employing quantum

states of light or matter as sensing probes. Photons are the clear choice as quantum probes

for remote sensing because they can travel to and interact with a distant target. Existing

schemes are mainly based on the quantum illumination framework, which requires a quantum

memory to store a single photon of an initially entangled pair until its twin reflects off

a target and returns for final correlation measurement. Existing demonstrations are limited

to tabletop experiments, and expanding the sensing range faces various roadblocks,

including long-time quantum storage and photon loss and noise when transmitting quantum

signals over long distances. We propose a novel quantum sensing framework that addresses

these challenges using quantum frequency combs with path identity for remote sensing of signatures

(“qCOMBPASS”). The combination of two key quantum phenomena, namely quantum

induced coherence by path indistinguishability8,9 and quantum frequency combs,

allows for quantum remote sensing without requiring a quantum memory. We develop the

basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce

the qCOMBPASS equation – a quantum analog of the well-known LIDAR equation

in classical remote sensing. We also describe an experimental scheme to demonstrate the concept

using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various

applications in remote quantum sensing, imaging, metrology, and communications. These

include detection and ranging of low-reflectivity objects, measurement of small displacements

of a remote target with precision beyond the standard quantum limit (SQL), standoff

hyperspectral quantum imaging, and very-long-baseline interferometry.

Presenters

  • Diego R Dalvit

    Los Alamos National Laboratory

Authors

  • Diego R Dalvit

    Los Alamos National Laboratory

  • Tyler Volkoff

    Los Alamos National Laboratory

  • Yunseok Choi

    Los Alamos National Laboratory

  • Abul K Azad

    Los Alamos National Laboratory

  • Abul K Azad

    Los Alamos National Laboratory

  • Hou-tong Chen

    Los Alamos National Laboratory

  • Peter Milonni

    University of Rochester

  • Peter Milonni

    University of Rochester