54 citations · 395 across the 33 of their papers we have counts for
36 papers
Exact computation of the network modularity with branch-and-bound
Emily Weng, Georg Hahn
We consider the computation of the network modularity, a common measure of the structure of networks or graphs that quantifies the division of a network into modules or clusters. T…
Increasing the Hardness of Posiform Planting Using Random QUBOs for Programmable Quantum Annealer Benchmarking
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Posiform planting is a method for constructing QUBO problems with a single unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investiga…
Comparing Quantum Annealing and Spiking Neuromorphic Computing for Sampling Binary Sparse Coding QUBO Problems
Kyle Henke, Elijah Pelofske, Garrett Kenyon +1
We consider the problem of computing a sparse binary representation of an image. To be precise, given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse b…
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor
Kyle Henke, Elijah Pelofske, Georg Hahn +1
We consider the problem of computing a sparse binary representation of an image. To be precise, given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse b…
Posiform Planting: Generating QUBO Instances for Benchmarking
Georg Hahn, Elijah Pelofske, Hristo N. Djidjev
We are interested in benchmarking both quantum annealing and classical algorithms for minimizing Quadratic Unconstrained Binary Optimization (QUBO) problems. Such problems are NP-h…
Initial State Encoding via Reverse Quantum Annealing and h-gain Features
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization probl…