Sanjaya Lohani, Ph.D.
Assistant Professor of Department of Electrical & Computer Engineering
Affiliate Faculty of O'Donnell Data Science and Research Institute
| Department |
ECE |
| 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, SMU. Prior to SMU, 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, LAResearch
- 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. https://iopscience.iop.org/article/10.1088/1367-2630/ace6c8/meta
- 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. https://iopscience.iop.org/article/10.1088/2632-2153/ac9036/meta
- 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. https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043145
- 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. https://onlinelibrary.wiley.com/doi/full/10.1002/qute.202000103
- 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. https://ieeexplore.ieee.org/abstract/document/9521839
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