3 citations · 11 across the 17 of their papers we have counts for
17 papers
Operator Score Matching for Learning Quantum Hamiltonians
Shreya Shukla, Abhijith Jayakumar, Andrey Y. Lokhov
Learning quantum Hamiltonians from low-temperature thermal state measurements is a fundamental problem in quantum physics. Scalability of existing methods is limited by the complex…
Conditions for Quantum Advantage in AC Power Flow
Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin +1
This paper aims to contextualize the requirements for Quantum Computing (QC) algorithms to achieve a quantum advantage in solving the alternating current power flow (ACPF) problem,…
Finite Sample Bounds for Learning with Score Matching
Devin Smedira, Abhijith Jayakumar, Sidhant Misra +2
Learning of continuous exponential family distributions with unbounded support remains an important area of research for both theory and applications in high-dimensional statistics…
The Quantum Hamiltonian Analysis Toolkit: Lowering the Barrier to Quantum Computing with Hamiltonians
Brendan K. Krueger, Stephan Eidenbenz, Shamminuj Aktar +8
We present the Quantum Hamiltonian Analysis Toolkit (QHAT), a newly developed application that provides a user-friendly interface for studying Hamiltonians and performing Hamiltoni…
Computationally sufficient statistics for Ising models
Abhijith Jayakumar, Shreya Shukla, Marc Vuffray +2
Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms fo…
Discrete Diffusion with Sample-Efficient Estimators for Conditionals
Karthik Elamvazhuthi, Abhijith Jayakumar, Andrey Y. Lokhov
We study a discrete denoising diffusion framework that integrates a sample-efficient estimator of single-site conditionals with round-robin noising and denoising dynamics for gener…
Efficient Learning of Lattice Gauge Theories with Fermions
Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov +2
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the S…
Learning of Statistical Field Theories
Shreya Shukla, Abhijith Jayakumar, Andrey Y. Lokhov
Recovering microscopic couplings directly from data provides a route to solving the inverse problem in statistical field theories, one that complements the traditional-often comput…
Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians
Yuchen Pang, Abhijith Jayakumar, Evan McKinney +3
We introduce Autoregressive Graphical Models (AGMs) as an Ansatz for modeling the ground states of stoquastic Hamiltonians. Exact learning of these models for smaller systems show…
Learning response functions of analog quantum computers: analysis of neutral-atom and superconducting platforms
Cenk Tüysüz, Abhijith Jayakumar, Carleton Coffrin +2
Analog quantum computation is an attractive paradigm for the simulation of time-dependent quantum systems. Programmable analog quantum computers have been realized in hardware usin…
Discrete distributions are learnable from metastable samples
Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra +1
Physically motivated stochastic dynamics are widely used to sample from high-dimensional distributions. However, such samplers often get trapped in metastable states, approximately…
Potential Applications of Quantum Computing at Los Alamos National Laboratory
Andreas Bärtschi, Francesco Caravelli, Carleton Coffrin +16
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural t…
Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow
Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin +1
Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical quantum advantage can be sai…
Universal framework for simultaneous tomography of quantum states and SPAM noise
Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin +3
We present a general denoising algorithm for performing simultaneous tomography of quantum states and measurement noise. This algorithm allows us to fully characterize state prepar…
Learning Energy-Based Representations of Quantum Many-Body States
Abhijith Jayakumar, Marc Vuffray, Andrey Y. Lokhov
Efficient representation of quantum many-body states on classical computers is a problem of enormous practical interest. An ideal representation of a quantum state combines a succi…
Shot-frugal and Robust quantum kernel classifiers
Abhay Shastry, Abhijith Jayakumar, Apoorva Patel +1
Quantum kernel methods are a candidate for quantum speed-ups in supervised machine learning. The number of quantum measurements N required for a reasonable kernel estimate is a cri…
Learning of Discrete Graphical Models with Neural Networks
Abhijith J., Andrey Y. Lokhov, Sidhant Misra +1
Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a disc…