most citedPotential Applications of Quantum Computing at Los Alamos National Laboratory

3 citations · 3 across the 2 of their papers we have counts for

collaborators

8 papers

quant-ph2026

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,…

quant-ph20263 cited

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…

quant-ph2025

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…

quant-ph2025

Cost of Emulating a Small Quantum Annealing Problem in the Circuit-Model

Javier Gonzalez-Conde, Zachary Morrell, Marc Vuffray +2

Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-m…

cond-mat.stat-mech2025

Classical Criticality via Quantum Annealing

Pratik Sathe, Andrew D. King, Susan M. Mniszewski +3

Quantum annealing provides a powerful platform for simulating magnetic materials and realizing statistical physics models, presenting a compelling alternative to classical Monte Ca…

quant-ph2025

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…