Sanjaya Lohani, PhD

SANJAYA LOHANI, PhD

Research Scientist/Engineer

C2QA| Co-design center for quantum advantage fellow researcher

Elec. & Comp. Engineering, UIC

1020 SEO MC 154,

851 S. Morgan Street

Chicago, IL 60607

slohan3@uic.edu* (Please email me for the recent CV)

Ph.D. | 2020

Physics and Engineering Physics, Tulane University, New Orleans, USA

PROFESSIONAL EXPERIENCE

10/2021 - Present

Researcher at Elec. & Comp. Eng., University of Illinois Chicago

Visiting Scholar at Oak Ridge National Laboratory (ORNL), Oak Ridge (12/2022 - Present)

05/2021 – 10/2021

Researcher at IBM-HBCU Quantum Center, Howard University, Washington

Visiting Scholar at UIC, Chicago

05/2020 – 05/2021 (Joint Appointment)

Researcher at Quantum Information and Nonlinear Optics group, Tulane

Machine Learning Engineer at Defendry

HONORS AND AWARDS

Member – IOP Science, since 06/2022.
Department of Energy – C2QA fellow researcher at UIC | 2021.
IBM-HBCU Quantum Center fellow researcher | 05/2021 - 10/2021.
Stipend from the Tensorflow – Google, 2020.
Elizabeth Land Parks and Franklin Parks Fellow – Tulane University, 2018.
Incubic-Milton Chang Award, 2019.
Emil Wolf Outstanding Finalist Award, 2019.
GSSA Travel Awards (2019, 2018, 2015).
SSE Dean’s Office Travel Awards (2019, 2015).
Best Runner-up Talk (2018).
Honorable Mention Received at SSE Research Day (2017).
Materials Computation Center (MCC) Award (2015).
Workshop Travel Award to Berlin, Germany (2015).

GRANTS

PI/co-PI:

$50,000 | 1 Year (Oct. 2022 - Oct. 2023)

Cross-Cutting Research Seed Funding. DOE, Co-design Center for Quantum Advantage (C2QA).

Contributed:

Research Funding, US Department of Energy, Office of Science, NQIS Research Centers, Co-design Center for Quantum Advantage (C2QA), DE-SC0012704 (PI: Thomas A. Searles).
Research Funding, US ONR, N000141912374 (PI: Ryan T. Glasser).
Research Funding,ARL/ARO, W911NF-19-2-0087, W911NF-20-2-0168. (PI: Ryan T. Glasser).

SOFTWARE (Open Source)

GitHub: https://github.com/slohani-ai

PyPi Package: mlphys

pip install mlphys

Tutorials:

Generating Biased Distributions of Random Quantum States
Inference on Pre-trained Networks
Superposition OAM Tensors
Non-Superposition OAM Tensors
Hands-on Coding Examples for the Data-Centric in quantum information science

Web Application:

https://mlphys.streamlit.app

PUBLICATIONS

Articles:

Lohani, S., Lukens, J. M., Davis, 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, 083009.
Lohani, S., Regmi, S., Lukens, J. M., Glasser, R. T., Searles, T. A., and Kirby, B. T. (2023). Dimensionadaptive machine-learning-based quantum state reconstruction. Quantum Machine Intelligence, 5(1), 1-10.
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, 04LT01.
Leamer, J.M., Zhang, W. Savino, N.J., Saripalli, R.K., Lohani, S., Glasser, R.T., Bondar, D.I. 2022. Classical optical analogue of quantum discord. The European Physical Journal Special Topics, 1-7.
Bart, M., Regmi, P., Lior, C., Shaw, H. C., Safavi, H., Lohani, S., Searles, T. A., Kirby, B. T., Lee, H., and Glasser. R. T. 2022. Deep learning for Enhanced Free-Space Optical Communications. (Accepted at Machine Learning: Science and Technology)
Savino, N.J., Lohani, S., and Glasser R.T., (2022). Deep learning for eavesdropper detection in free-space optical on-off keying. Optics Continuum, 1(12), pp.2416-2425.
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. Physics Review Research, 3, p. 043145.

∗This research has been featured in the IBM Quantum – Qiskit blog and official Twitter page. A Hardware-Aware Approach to Improving Quantum State Tomography, Feb 15, 2022, Qiskit.

Lohani, S., Searles, T.A., Kirby, B.T. and Glasser, R., 2021. On the experimental feasibility of quantum state reconstruction via machine learning. IEEE Transactions on Quantum Engineering, 2, pp. 1-10.
Danaci, O., Lohani, S., Kirby, B.T. and Glasser, R.T., 2021. Machine learning pipeline for quantum state estimation with incomplete measurements. Machine Learning: Science and Technology, 2(3), p.035014.
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.

∗This research has been selected to be featured on the front cover of the issue: Advanced Quantum Technologies, Vol. 4, No. 3, March 2021.

Bhusal, N., Lohani, S., You, C., Hong, M., Fabre, J., Zhao, P., Knutson, E.M., Glasser, R.T. and Magaña-Loaiza, O.S., 2021. Front Cover: Spatial Mode Correction of Single Photons Using Machine Learning (Adv. Quantum Technol. 3/2021). Advanced Quantum Technologies, 4(3), p.2170031.

Lohani, S., Knutson, E.M. and Glasser, R.T., 2020. Generative machine learning for robust free-space communication. Nature - Communications Physics, 3(1), pp.1-8.
Lohani, S., Kirby, B.T., Brodsky, M., Danaci, O. and Glasser, R.T., 2020. Machine learning assisted quantum state estimation. Machine Learning: Science and Technology, 1(3), p.035007.
Lohani, S. and Glasser, R.T., 2020. Coherent optical communications enhanced by machine intelligence. Machine Learning: Science and Technology, 1(3), p.035006.
Lohani, S., Knutson, E.M., Zhang, W. and Glasser, R.T., 2019. Dispersion characterization and pulse prediction with machine learning. OSA Continuum, 2(12), pp.3438-3445.
Lohani, S. and Glasser, R.T., 2018. Turbulence correction with artificial neural networks. Optics letters, 43(11), pp.2611-2614.
Lohani, S., Knutson, E.M., O’Donnell, M., Huver, S.D. and Glasser, R.T., 2018. On the use of deep neural networks in optical communications. Applied optics, 57(15), pp.4180-4190.
Reilly, A. M., Cooper, R. I., Adjiman, C. S., Bhattacharya, S., Boese, A. D., Brandenburg, J. G., ... Lohani, S., .... & Groom, C. R., 2016. Report on the sixth blind test of organic crystal structure prediction methods. Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 72(4), 439-459.

Reviewed Proceedings: (Two or more pages)

Regmi S., Blackwell, A., Khannejad A., Lohani S., Lukens, J.M., Glasser R., Kirby B., Searles T., 2023. Bayesian quantum state reconstruction with a learning-based tuned prior. Quantum 2.0 (pp. QM4B-3). Optica Publishing Group.
Lohani, S., Lukens, J.M., Jones, D.E., Searles, T.A., Glasser, R.T. and Kirby, B.T., 2022. Machine Learning and Bayesian mean estimation meet biased quantum state distributions. CLEO: Applications and Technology (pp. AW4P-2). Optica Publishing Group.
Lohani, S., Kirby, B. T., Glasser, R. T. and Searles, T. A. 2021. Scaling properties of pre-trained neural network-based quantum state tomography, Quantum Techniques in Machine Learning (QTML). Booklet of extended abstracts.
Lohani, S., Kirby, B.T., Glasser, R. and Searles, T.A., 2021. On the efficacy of classical deep learning methods on quantum information science. In Laser Science (pp. JW7A-113). Optical Society of America.
Savino, N.J., Bart, M.P., Regmi P., Lohani, S., Cohen, L., Wylie, S.K., Shaw, H.C., Safavi, H., Lee, H., Searles, T.A., Kirby, B.T., and Glasser, R.T., 2021. Bit-Error rate reduction of free-space optical on-off keying with atmospheric effects. In Frontiers in Optics (pp. JTh5A-137). Optical Society of America.
Savino, N.J., Lohani, S., and Glasser, R.T., 2020, September. Simulated Eavesdropper Detection in Free- Space Optics ON-OFF Keying with Deep Learning. In Frontiers in Optics (pp. FTh5E-6). Optical Society of America.
Lohani, S., Savino, N.J. and Glasser, R.T., 2020, September. Free-space optical ON-OFF keying communications with deep learning. In Frontiers in Optics (pp. FTh5E-4). Optical Society of America.
Danaci, O., Lohani, S., Kirby, B.T., Brodsky, M. and Glasser, R.T., 2020, September. Quantum State Estimation from Partial Tomography Data Using a Stack of Machine Learning Models and Imputation. In Frontiers in Optics (pp. FTu8D-5). Optical Society of America.
Lohani, S. and Glasser, R.T., 2019, September. Robust free space oam communications with unsupervised machine learning. In Frontiers in Optics (pp. FTu5B-3). Optical Society of America.
Bhusal, N., Lohani, S., You, C., Lambert, A., Knutson, E.M., Dowling, J.P., Glasser, R.T. and Magaña-Loaiza, O.S., 2019, September. Artificial Neural Networks for Turbulence Correction of Structured Light. In Frontiers in Optics (pp. FTu5B-2). Optical Society of America.
Knutson, E.M., Lohani, S., Danaci, O., Huver, S.D. and Glasser, R.T., 2016, September. Deep learning as a tool to distinguish between high orbital angular momentum optical modes. In Optics and Photonics for Information Processing X (Vol. 9970, p. 997013). International Society for Optics and Photonics.

Reviewed Conference Abstracts:

Lohani, S., Lukens, J. M., Glasser, R. T., Kirby, B. T. and Searles, T. A. 2022. Data-centric artificial intelligence in quantum information science. Workshop on Innovative Nanoscale Devices and Systems (WINDS). Dec. 4 – 9.
Regmi, S., Lohani, S., Lukens, J., Glasser, R., Kirby, B and Searles, T. 2022. Dimension-adaptive quantum state tomography with machine learning. Workshop on Innovative Nanoscale Devices and Systems (WINDS). Dec. 4 – 9.
Regmi, S., Lohani, S., Lukens, J., Glasser, R., Kirby, B and Searles, T. 2022. Quantum state reconstruction for systems with different dimensions using machine learning. Prairie Section APS Fall 2022 Meeting.
Lohani, S., Lukens, J., Glasser, R., Searles, T., and Kirby, B. 2022. Data-centric approach to machine learning in quantum state reconstruction. Jonathan P. Dowling Memorial Conference on Quantum Science and Technology.
Leamer, J.M., Zhang, W. Savino, N.J., Saripalli, R.K., Lohani, S., Glasser, R.T., Bondar, D.I. 2022. Classical optical analog of quantum discord. Jonathan P. Dowling Memorial Conference on Quantum Science and Technology.
Regmi, S., Lohani, S., Lukens, J., Glasser, R., Searles, T., and Kirby, B. 2022. Quantum state tomography for systems with different dimension using neural network. Jonathan P. Dowling Memorial Conference on Quantum Science and Technology.
Lohani, S., Lukens, J.M., Jones, D.E., Searles, T.A., Glasser, R.T. and Kirby, B.T., 2022. Quantum state reconstruction with biased distributions of quantum states. Bulletin of the American Physical Society.
Blackwell, A., Gomez, M., Danaci, O., Lohani, S., Kirby, B., Glasser, R., & Searles, T. (2022). Demonstrating a Quantum Permutation Algorithm with Higher Qubit Near-term Intermediate Scale Quantum Processors. Bulletin of the American Physical Society.
Lohani, S., Lukens, J.M., Danaci, O., Jones, D.E., Glasser, R.T., Kirby, B.T., and Searles, T.A., 2022. A machine learning perspective on quantum state tomography: a case study using NISQ era hardware. SPIE Photonics West.
Lohani, S., Lukens, J.M., Jones, D.E., Searles, T.A., Glasser, R.T. and Kirby, B.T., 2021.Biased distributions of random quantum states for high-performance quantum state reconstruction. Workshop on Innovative Nanoscale Devices and Systems (WINDS). November 28 – December 3.
Glasser, R.T., Lohani, S., Kirby, B.T., Brodsky, M., Danaci, O. and Searles, T.A., 2021, March. Machine learning for enhancing quantum state estimation. In Optical and Quantum Sensing and Precision Metrology (Vol. 11700, p. 117001D). International Society for Optics and Photonics.
Lohani, S., Knutson, E., Tkach, S., Huver, S., Glasser, R. and Deep Science AI Collaboration, 2017, March. Enhancing optical communication with deep neural networks. In APS March Meeting Abstracts (Vol. 2017, pp. H27-002).

INVITED TALKS

Reinforcement learning assisted variational quantum eigensolvers. Workshop on Quantum and AI, MIT, May 18 - 19, 2023.
Quantum state reconstruction with machine learning. IQUIST Young Researchers’ Seminar. The University of Illinois at Urbana-Champaign, Sep 28, 2022.
Quantum state tomography with machine learning. Seminar at University of Texas at San Antonio, Sep 23, 2022. Quantum state tomography with learning-based techniques. Quantum Thursday, March 31, 2022, C2QA| Co-design Center for Quantum Advantage - Brookhaven National Laboratory.
A machine learning perspective on quantum state tomography: a case study using NISQ era hardware. SPIE-Photonics West, 22 - 27 January 2022, San Francisco, CA.
On the experimental feasibility of quantum state reconstruction. Fermilab HEP/QIS, 22 Nov. 2021.
An overview of quantum state tomography using IBM Q computer. IBM-HBCU Quantum Center Faculty Meeting, July 27, 2021, USA.

CONTRIBUTED TALKS

Machine Learning and Bayesian mean estimation meet biased quantum state distributions. CLEO May 17, 2022
Quantum state reconstruction with biased distributions of quantum states. APS March Meeting 2022.
Biased distributions of random quantum states for high-performance quantum state reconstruction. WINDS, Nov 28 - Dec 4, 2021, Hawaii.
Machine learning for optical communications, nonlinear optics and quantum Optics. SAMPL Howard meeting, June 24, 2021.
On the experimental feasibility of quantum state reconstruction via machine learning. SAMPL Howard meeting, Sep. 30, 2021.
Free-space optical on-off keying communications with deep learning, Frontiers in Optics Laser Science, Sep. 17, 2020.
Robust free space oam communications with unsupervised machine learning, Frontiers in Optics Laser Science, Sep 15 - 19, 2019. Machine learning for quantum optics. QUILT: Quantum Information Technology in Louisiana, Nov 02, 2018.
Machine learning for optical communications. PEP Summer Research Colloquium - Tulane, Aug 31, 2018.
Machine learning for classical and Quantum communications. QUILT: Quantum Information Technology in Louisiana, May 30, 2018.
Enhancing optical communication with deep neural networks. APS March Meeting, 2017.
Artificial neural network as a tool to distinguish between high orbital angular momentum optical states. PEP Summer Research Colloquium - Tulane, Sep. 2, 2016.

POSTERS:

Data-centric artificial intelligence in quantum information science. WINDS, Dec 4 - 9, 2022, Hawaii.
Quantum Permutation and State Tomography. Yale University, Second C2QA annual meeting, Oct 5, 2022.
A Hardware-Aware Approach to Improving Quantum State Tomography. July 11-15, 2022, The Theory of Quantum Computation, Communication and Cryptography (TQC).
Data-centric approach to machine learning in quantum state reconstruction.

August 19 -21, ThirdWorkshop for Quantum Repeaters and Networks (WQRN3), University of Chicago.

May 12-13, 2022, Jonathan P. Dowling Memorial Conference on Quantum Science and Technology, Louisiana State University.

Classical Optical Analogue of Quantum Discord. May 12-13, 2022, Jonathan P. Dowling Memorial Conference on Quantum Science and Technology, Louisiana State University.
Quantum State Tomography for Systems with Different Dimensions Using Neural Network.

August 19 -21, ThirdWorkshop for Quantum Repeaters and Networks (WQRN3), University of Chicago.

May 12-13, 2022, Jonathan P. Dowling Memorial Conference on Quantum Science and Technology, Louisiana State University.

April 14, 2022 (World Quantum Day), University of Illinois Urbana Champaign.

Learning from Biased Distributions of Quantum States.

August 19 -21, ThirdWorkshop for Quantum Repeaters and Networks (WQRN3), University of Chicago.

Quantum Information Processing (QIP), March 7 - 11, 2022, California Institute of Technology.

Deep learning techniques in quantum state tomography, C2QA: All Hands Meeting, 2021.
Scaling properties of pre-trained neural-network-based quantum state tomography, Quantum Techniques in Machine Learning, 2021.
On the efficacy of classical deep learning methods on quantum information science. Frontiers in Optics Laser Science, 2021.
Bit-error rate reduction of free-space optical on-off keying with atmospheric effects. Frontiers in Optics Laser Science, 2021.
On the efficacy of classical deep learning methods on quantum information science. Frontiers in quantum computing: University of Rhode Island, 2021.
Turbulence correction with artificial neural networks. SSE Research Day, Tulane, 2018.
Enhancing optical communication with deep neural networks. SSE Research Day, Tulane, 2017.
Scaling of FHI-aims on SPHYNX and CYPRESS.

Max-Planck Society, Berlin, July 2015.

ES2015 Workshop: Developments in electronic structure theory and excited states beyond ground state DFT, June 2015.

SCALA 2015: Scientific Computing around Louisiana, March 2015.

ACTIVE COLLABORATIONS

DEVCOM Army Research Laboratory

Oak Ridge National Laboratory

Brookhaven National Laboratory, Co-design Center for Quantum Advantage (C2QA)

National Aeronautics and Space Administration (NASA) – Goddard Space Flight Center

Virginia Tech

University of Illinois Urbana-Champaign

Tulane University,

Louisiana State University,

Rensselaer Polytechnic Institute

QuSteam

NVIDIA; IBM Quantum Experience

PEER-REVIEW SERVICES

Nature Publishing Group – Nature Communications, Scientific Reports.

Optica and IEEE Publishing Group – Optics Letters, Optics Express, Applied Optics, Photonics Research, Journal of Lightwave Technology, IEEE Access.

∗Received a referee lapel pin from OPTICA for contributions to optics and photonics.

Physical Review Journals – Physical Review Letter, Physical Review Research, Physical Review A.

Others – Applied Physics Letter (AIP); Optics Communications; Computers in Biology and Medicine; International Journal of Numerical Modelling: Electronic Network, Devices and Field; Physical Communication.