activity
20172025
most citedOptimization of population annealing Monte Carlo for large-scale spin-glass simulations

29 citations · 69 across the 6 of their papers we have counts for

collaborators

6 papers

quant-ph2025★ 1 cited

Direct comparison of stochastic driven nonlinear dynamical systems for combinatorial optimization

Junpeng Hou, Amin Barzegar, Helmut G. Katzgraber

Combinatorial optimization problems are ubiquitous in industrial applications. However, finding optimal or close-to-optimal solutions can often be extremely hard. Because some of t…

cond-mat.dis-nn2020★ 1 cited

Optimization and benchmarking of the thermal cycling algorithm

Amin Barzegar, Anuj Kankani, Salvatore Mandrà +1

Optimization plays a significant role in many areas of science and technology. Most of the industrial optimization problems have inordinately complex structures that render finding…

quant-ph2020★ 21 cited

Computational Overhead of Locality Reduction in Binary Optimization Problems

Elisabetta Valiante, Maritza Hernandez, Amin Barzegar +1

Recently, there has been considerable interest in solving optimization problems by mapping these onto a binary representation, sparked mostly by the use of quantum annealing machin…

cond-mat.dis-nn2018★ 13 cited

Numerical observation of a glassy phase in the three-dimensional Coulomb glass

Amin Barzegar, Juan Carlos Andersen, Moshe Schechter +1

The existence of an equilibrium glassy phase for charges in a disordered potential with long-range electrostatic interactions has remained controversial for many years. Here we con…

cond-mat.dis-nn2018★ 4 cited

Distribution of interevent avalanche times in disordered and frustrated spin systems

John Ferre, Amin Barzegar, Helmut G. Katzgraber +1

Hysteresis loops and the associated avalanche statistics of spin systems, such as the random-field Ising and Edwards-Anderson spin-glass models, have been extensively studied. A pa…

cond-mat.dis-nn2017★ 29 cited

Optimization of population annealing Monte Carlo for large-scale spin-glass simulations

Amin Barzegar, Christopher Pattison, Wenlong Wang +1

Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel an…