Sanjaya Lohani, Ph.D.

Assistant Professor of Department of Electrical & Computer Engineering
Affiliate Faculty of O’Donnel Data Science and Research Institute

Department

ECE
Email

slohani@smu.edu
Office Location

Junkins 315
Website

Dr. Lohani is an Assistant Professor in the Department of Electrical and Computer Engineering at Bobby B. Lyle School of Engineering, èƵapp. Prior to èƵapp, Dr. Lohani was a Co-design Center for Quantum Advantage (C2QA) fellow research engineer in the Department of Electrical and Computer Engineering at the University of Illinois Chicago (UIC). Dr. Lohani also worked as a fellow researcher at the IBM-HBCU Quantum Center in Washington DC, and as a postdoctoral AI researcher in the Quantum Information and Non-linear Group at Tulane University.

Dr. Lohani has been recognized with the prestigious "Elizabeth Land Parks and Franklin Parks" fellowship for developing a computer artificial intelligence technique for free-space quantum and classical optical communications. Additionally, he has received awards such as the Incubic-Milton Chang Award, Emil Wolf Outstanding Finalist, and the Materials Computation Center (MCC) Award.

Dr. Lohani’s research group is dedicated to developing innovative and impactful solutions across a wide spectrum of exciting and flourishing fields of quantum science and engineering, including artificial intelligence, quantum computation and control, quantum sensing, communication and networking, and cutting-edge quantum technologies.

Education

M.S., Ph.D. -- Physics and Engineering Physics, Tulane University, New Orleans, LA
B.S., M.S. -- Physics, Tribhuvan University, Nepal

Research

  • Open Quantum Systems – Quantum Network and Communication, Quantum Sensing and Tomography
  • Quantum Computing and Control - Quantum High Performance Computing, Quantum Algorithms, Quantum Machine Learning
  • Neuromorphic Computing, Quantum Embedding, Diffractive AI, AI-on-Chips

Publications

  • Lohani, S., Lukens, J.M., Davis, A.A., Khannejad, A., Regmi, S., Jones, D.E., Glasser, R.T., Searles, T.A. and Kirby, B.T., 2023. Demonstration of machine-learning-enhanced Bayesian quantum state estimation. New Journal of Physics, 25(8), p.083009.
  • Lohani, S., Lukens, J.M., Glasser, R.T., Searles, T.A. and Kirby, B.T., 2022. Data-centric machine learning in quantum information science. Machine Learning: Science and Technology, 3(4), p.04LT01.
  • Lohani, S., Lukens, J.M., Jones, D.E., Searles, T.A., Glasser, R.T. and Kirby, B.T., 2021. Improving application performance with biased distributions of quantum states. Physical Review Research, 3(4), p.043145.
  • Bhusal, N., Lohani, S., You, C., Hong, M., Fabre, J., Zhao, P., Knutson, E.M., Glasser, R.T. and Magaña‐Loaiza, O.S., 2021. Spatial mode correction of single photons using machine learning. Advanced Quantum Technologies, 4(3), p.2000103.
  • Lohani, S., Searles, T.A., Kirby, B.T. and Glasser, R.T., 2021. On the experimental feasibility of quantum state reconstruction via machine learning. IEEE Transactions on Quantum Engineering, 2, pp.1-10.
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