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Found 7 papers, 2 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: 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.
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.
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.
Entangling nuclear spins in distant quantum dots via an electron bus
We propose a protocol for the deterministic generation of entanglement between two ensembles of nuclear spins surrounding two distant quantum dots.
Mesoscale and Nanoscale Physics Quantum Physics
