Výsledky vyhledávání - "J. Kyle Brubaker"

Akademický článek

Designing quantum annealing schedules using Bayesian optimization

Autor: Jernej Rudi Finžgar, Martin J. A. Schuetz, J. Kyle Brubaker, Hidetoshi Nishimori, Helmut G. Katzgraber

Publikováno v: Physical Review Research, Vol 6, Iss 2, p 023063 (2024)

We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with results for two paradigmatic spin models. We find that Bayesian optimiza

Externí odkaz: https://doaj.org/article/f2b613213af74958b0efaef4910948e2

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Akademický článek

Graph coloring with physics-inspired graph neural networks

Autor: Martin J. A. Schuetz, J. Kyle Brubaker, Zhihuai Zhu, Helmut G. Katzgraber

Publikováno v: Physical Review Research, Vol 4, Iss 4, p 043131 (2022)

We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multiclass node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts mode

Externí odkaz: https://doaj.org/article/2e28c2843c6c4696af8a618cd8c8ec03

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Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set

Autor: Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber

We provide a comprehensive reply to the comment written by Chiara Angelini and Federico Ricci-Tersenghi [arXiv:2206.13211] and argue that the comment singles out one particular non-representative example problem, entirely focusing on the maximum inde

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23555962c19cd9de597afec77dc257e3
http://arxiv.org/abs/2302.03602

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Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems

Autor: Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber

We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] and argue that the comment singles out one particular non-representative example problem, entirely focusing on the maximum cut problem (MaxCut) on sparse g

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee4e1881be563108e15c853aa4dba0bc

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Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms

Autor: Martin J.A. Schuetz, J. Kyle Brubaker, Henry Montagu, Yannick van Dijk, Johannes Klepsch, Philipp Ross, Andre Luckow, Mauricio G.C. Resende, Helmut G. Katzgraber

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The cor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3002c131cc431525c949cc987a79634c
http://arxiv.org/abs/2206.03651

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Combinatorial optimization with physics-inspired graph neural networks

Autor: Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber

Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at unprecedented scales, but a unifying framework that incorporates insights from statistical physics is stil

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a74e3867a89c1eca9389b058defe3e5
https://resolver.caltech.edu/CaltechAUTHORS:20220505-181556200

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