Abhijith Jayakumar

Quantum ''and'' Computing

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I am a Scientist at the Theoretical Division of Los Alamos National Lab, working on problems at the intersection of Quantum Information, Machine Learning, and Statistical Physics. I was previously a CNLS Postdoctoral Fellow in the same division mentored by Dr. Andrey Lokhov and Dr. Marc Vuffray. Before Los Alamos, I did my Ph.D. focusing on quantum algorithms and M.Tech in computational science from the Indian Institute of Science, working under Prof. Apoorva Patel.

:envelope: abhijithj@lanl.gov

research

quantum computing machine learning statistical physics

Everything that I do can be broadly classified as algorithms research heavily influenced by ideas from Physics. However, very little of it cleanly falls into a single field. This is the most enjoyable and the least pragmatic approach to designing a career in research. This diagram represents each of my papers as a dot, placed by how much it leans on each field. My work falls into a few long-running threads, which are explained below. Hopefully you, the reader, will see the unity between them as clearly as I do.

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Learning theory for exponential families

Provable, sample-efficient recovery of high-dimensional distributions.
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Machine learning for quantum systems

Statistical estimation applied to quantum states and devices.
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Quantum algorithms and applications

What quantum computers can actually do for concrete problems — and where the claims break down.
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Quantum walks and spatial search

Discrete-time quantum walk algorithms for search on graphs.

selected publications

  1. Discrete distributions are learnable from metastable samples
    Abhijith Jayakumar, Andrey Y Lokhov, Sidhant Misra , and 1 more author
    Nature Communications, 2026
  2. Finite Sample Bounds for Learning with Score Matching
    Devin Smedira, Abhijith Jayakumar, Sidhant Misra , and 2 more authors
    In The Thirty Ninth Annual Conference on Learning Theory , 2026
  3. Efficient learning of lattice gauge theories with fermions
    Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov , and 2 more authors
    Phys. Rev. D, Jun 2026
  4. Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow
    Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin , and 1 more author
    IEEE Transactions on Power Systems, Jun 2025
  5. Universal framework for simultaneous tomography of quantum states and SPAM noise
    Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin , and 3 more authors
    Quantum, Jul 2024
  6. Quantum algorithm implementations for beginners
    J Abhijith, Adetokunbo Adedoyin, John Ambrosiano , and 8 more authors
    ACM Transactions on Quantum Computing, Jul 2022
  7. Learning of discrete graphical models with neural networks
    Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra , and 1 more author
    Advances in Neural Information Processing Systems, Jul 2020
  8. Spatial search on graphs with multiple targets using flip-flop quantum walk
    J Abhijith, and Apoorva Patel
    Quantum Information and Computation 18, 1295-1331 (2018), Jul 2018