327 citations · 1.2k across the 57 of their papers we have counts for
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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…
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…
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…
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…