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Found 12 papers, 3 papers with code
Explainable AI using expressive Boolean formulas
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.
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
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.
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
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.
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
We solve robot trajectory planning problems at industry-relevant scales.
Graph Coloring with Physics-Inspired Graph Neural Networks
We show how graph neural networks can be used to solve the canonical graph coloring problem.
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
Combinatorial optimization problems are pervasive across science and industry.
Optimization and benchmarking of the thermal cycling algorithm
Optimization plays a significant role in many areas of science and technology.
Disordered Systems and Neural Networks Quantum Physics
Computational Overhead of Locality Reduction in Binary Optimization Problems
As such, physics-inspired techniques -- commonly used in fundamental physics studies -- are ideally suited to solve optimization problems in a binary format.
Quantum Physics
Chook -- A comprehensive suite for generating binary optimization problems with planted solutions
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
Perspectives of quantum annealing: Methods and implementations
Quantum annealing is a computing paradigm that has the ambitious goal of efficiently solving large-scale combinatorial optimization problems of practical importance.
Quantum Physics
Evolutionary Approaches to Optimization Problems in Chimera Topologies
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.
A probabilistic evolutionary optimization approach to compute quasiparticle braids
Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation.
