Self-Learning, Self-Evolving Smart Quantum Technologies for Secure Communication

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Researchers from Louisiana State University have actually presented a clever quantum innovation for the spatial mode correction of single photons. In a paper included on the cover of the March 2021 problem of Advanced Quantum Technologies, the authors make use of the self-learning and self-evolving functions of synthetic neural networks to fix the distorted spatial profile of single photons.

The authors, PhD prospect Narayan Bhusal, postdoctoral scientist Chenglong You, college student Mingyuan Hong, undergraduate trainee Joshua Fabre, and Assistant Professor Omar S. Magaña‐Loaiza of LSU—together with partners Sanjaya Lohani, Erin M. Knutson, and Ryan T. Glasser of Tulane University and Pengcheng Zhao of Qingdao University of Science and Technology—report on the capacity of expert system to fix spatial modes at the single-photon level.

“The random phase distortion is one of the biggest challenges in using spatial modes of light in a wide variety of quantum technologies, such as quantum communication, quantum cryptography, and quantum sensing,” stated Bhusal. “In this paper, we use artificial neurons to correct distorted spatial modes of light at the single-photon level. Our method is remarkably effective and time-efficient compared to conventional techniques. This is an exciting development for the future of free-space quantum technologies.”

The recently established method improves the channel capability of optical interaction procedures that count on structured photons.

“One important goal of the Quantum Photonics Group at LSU is to develop robust quantum technologies that work under realistic conditions,” stated Magaña‐Loaiza. “This smart quantum technology demonstrates the possibility of encoding multiple bits of information in a single photon in realistic communication protocols affected by atmospheric turbulence. Our technique has enormous implications for optical communication and quantum cryptography. We are now exploring paths to implement our machine learning scheme in the Louisiana Optical Network Initiative (LONI) to make it smart, secure, and quantum.” 

The U.S. Army Research Office is supporting Magaña‐Loaiza’s research study on a task entitled “Quantum Sensing, Imaging, and Metrology using Multipartite Orbital Angular Momentum.”

“We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science,” stated Dr. Sara Gamble, program supervisor at the Army Research Office, an aspect of DEVCOM ARL. “The team’s result is an exciting step forward in developing this understanding, and it has the potential to ultimately enhance the Army’s sensing and communication capabilities on the battlefield.”

Reference: “Spatial Mode Correction of Single Photons Using Machine Learning” by Narayan Bhusal, Sanjaya Lohani, Chenglong You, Mingyuan Hong, Joshua Fabre, Pengcheng Zhao, Erin M. Knutson, Ryan T. Glasser and Omar S. Magaña‐Loaiza, 22 January 2021, Advanced Quantum Technologies.
DOI: 10.1002/qute.202000103

The Louisiana Quantum Initiative is a statewide undertaking to advance the research study and innovation of quantum systems in the context of the 2nd quantum transformation and establish the method and technological facilities of quantum-driven networks and gadgets. The huge constellation of Louisiana researchers who become part of the effort incorporates scientists from all over the state, from both public and personal organizations. The effort is a community of research study that depends on emergent and vibrant associations and efforts amongst organizations along with private members.

The Quantum Photonics Group in the Department of Physics and Astronomy at LSU examines unique homes of light and their capacity for establishing quantum innovations. The group likewise carries out speculative research study in the fields of quantum plasmonics, quantum imaging, quantum metrology, quantum simulation, quantum interaction, and quantum cryptography.



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