no code implementations • 6 Jun 2023 • Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz, Grant Salton, Zhihuai Zhu, Elton Yechao Zhu, Serdar Kadıoğlu, Sima E. Borujeni, Helmut G. Katzgraber
We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula.
no code implementations • 3 Feb 2023 • Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original results, thereby refuting the comment's performance statements.
no code implementations • 3 Feb 2023 • 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 graphs, for which greedy algorithms are expected to perform well.
no code implementations • 8 Jun 2022 • 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.
1 code implementation • 3 Feb 2022 • Martin J. A. Schuetz, J. Kyle Brubaker, Zhihuai Zhu, Helmut G. Katzgraber
We show how graph neural networks can be used to solve the canonical graph coloring problem.
3 code implementations • 2 Jul 2021 • Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
Combinatorial optimization problems are pervasive across science and industry.
no code implementations • 17 Dec 2020 • Amin Barzegar, Anuj Kankani, Salvatore Mandrà, Helmut G. Katzgraber
Optimization plays a significant role in many areas of science and technology.
Disordered Systems and Neural Networks Quantum Physics
no code implementations • 17 Dec 2020 • Elisabetta Valiante, Maritza Hernandez, Amin Barzegar, Helmut G. Katzgraber
As such, physics-inspired techniques -- commonly used in fundamental physics studies -- are ideally suited to solve optimization problems in a binary format.
Quantum Physics
1 code implementation • 28 May 2020 • Dilina Perera, Inimfon Akpabio, Firas Hamze, Salvatore Mandra, Nathan Rose, Maliheh Aramon, Helmut G. Katzgraber
We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions.
Quantum Physics Disordered Systems and Neural Networks Other Computer Science
no code implementations • 15 Mar 2019 • Philipp Hauke, Helmut G. Katzgraber, Wolfgang Lechner, Hidetoshi Nishimori, William D. Oliver
Quantum annealing is a computing paradigm that has the ambitious goal of efficiently solving large-scale combinatorial optimization problems of practical importance.
Quantum Physics
no code implementations • 17 Aug 2016 • Roberto Santana, Zheng Zhu, Helmut G. Katzgraber
In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology.
no code implementations • 2 Oct 2014 • Roberto Santana, Ross B. McDonald, Helmut G. Katzgraber
Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation.