J. Kyle Brubaker on Muck Rack

J. Kyle Brubaker

As seen in: Nature, Physical Review B
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with J. Kyle

Contact J. Kyle, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Designing quantum annealing schedules using Bayesian optimization

|
By Bmw Ag, J. Kyle Brubaker, Hidetoshi Nishimori, Interdisciplinary Theoretical
| Physical Review B
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 optimization is able to identify schedules resulting in fidelities several orders of magnitude better than standard protocols for both quantum and reverse annealing, as applied to the p-spin model.
Open in Who Shared Issue with article?

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

|
By J. Kyle Brubaker, Helmut G. Katzgraber
| Nature Verified
reply to S. Boettcher Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00587-0 (2022). This is a preview of subscription content, access via your institution Access options Subscribe to Nature+ Get immediate online access to Nature and 55 other Nature journal Subscribe to Journal Get full journal access for 1 year $99.00 only $8.25 per issue All prices are NET prices. VAT will be added later in the checkout. Tax calculation will be finalised during checkout.
Open in Who Shared View Duplicates (2) Issue with article?

Graph coloring with physics-inspired graph neural networks

|
By J. Kyle Brubaker, Helmut G. Katzgraber, Zhihuai Zhu
| Physical Review B
Abstract 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 model. Generalizations to other multiclass problems such as community detection, data clustering, and the minimum clique cover problem are straightforward.
Open in Who Shared Issue with article?
See all 5 articles
 •  Privacy  •  Terms  •  Security and Legal  •   •   •   • 

© 2026 Muck Rack

SOC 2® Certified