publications

The complete list, newest first. The research page groups these by topic.

2026

  1. Discrete distributions are learnable from metastable samples
    Abhijith Jayakumar, Andrey Y Lokhov, Sidhant Misra , and 1 more author
    Nature Communications, 2026
  2. Conditions for Quantum Advantage in AC Power Flow
    Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin , and 1 more author
    arXiv preprint arXiv:2608.06711, 2026
  3. The Quantum Hamiltonian Analysis Toolkit: Lowering the Barrier to Quantum Computing with Hamiltonians
    Brendan K. Krueger, Stephan Eidenbenz, Shamminuj Aktar , and 8 more authors
    arXiv preprint arXiv:2605.11162, 2026
  4. Discrete Diffusion with Sample-Efficient Estimators for Conditionals
    Karthik Elamvazhuthi, Abhijith Jayakumar, and Andrey Y Lokhov
    arXiv preprint arXiv:2602.20293, 2026
  5. Computationally sufficient statistics for Ising models
    Abhijith Jayakumar, Shreya Shukla, Marc Vuffray , and 2 more authors
    arXiv preprint arXiv:2602.12449, 2026
  6. 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
  7. Efficient learning of lattice gauge theories with fermions
    Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov , and 2 more authors
    Phys. Rev. D, Jun 2026

2025

  1. Learning response functions of analog quantum computers: analysis of neutral-atom and superconducting platforms
    Cenk Tüysüz, Abhijith Jayakumar, Carleton Coffrin , and 2 more authors
    arXiv preprint arXiv:2503.12520, Jun 2025
  2. Learning of Statistical Field Theories
    Shreya Shukla, Abhijith Jayakumar, and Andrey Y Lokhov
    arXiv preprint arXiv:2511.09859, Jun 2025
  3. Autoregressive Pairwise Graphical Models Efficiently Find Ground State Representations of Stoquastic Hamiltonians
    Yuchen Pang, Abhijith Jayakumar, Evan McKinney , and 3 more authors
    In 2025 IEEE International Conference on Quantum Computing and Engineering (QCE) , Jun 2025
  4. Optimization Proxies using Limited Labeled Data and Training Time–A Semi-Supervised Bayesian Neural Network Approach
    Parikshit Pareek, Abhijith Jayakumar, Kaarthik Sundar , and 2 more authors
    International Conference on Machine Learning, Jun 2025
  5. 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

2024

  1. Potential Applications of Quantum Computing at Los Alamos National Laboratory
    Andreas Bärtschi, Francesco Caravelli, Carleton Coffrin , and 8 more authors
    arXiv preprint arXiv:2406.06625, Jun 2024
  2. Universal framework for simultaneous tomography of quantum states and SPAM noise
    Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin , and 3 more authors
    Quantum, Jul 2024
  3. Learning energy-based representations of quantum many-body states
    Abhijith Jayakumar, Marc Vuffray, and Andrey Y. Lokhov
    Phys. Rev. Res., Aug 2024

2022

  1. Reliable quantum kernel classification using fewer circuit evaluations
    Abhay Shastry, Apoorva Patel, Chiranjib Bhattacharyya , and 1 more author
    arXiv preprint arXiv:2210.06971, Aug 2022
  2. Quantum algorithm implementations for beginners
    J Abhijith, Adetokunbo Adedoyin, John Ambrosiano , and 8 more authors
    ACM Transactions on Quantum Computing, Aug 2022

2020

  1. Learning of discrete graphical models with neural networks
    Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra , and 1 more author
    Advances in Neural Information Processing Systems, Aug 2020

2019

  1. Improving the query complexity of quantum spatial search in two dimensions
    J Abhijith, and Apoorva Patel
    Quantum Information & Computation, Aug 2019

2018

  1. 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), Aug 2018