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Found 5 papers, 1 papers with code
Penalized Principal Component Analysis using Nesterov Smoothing
In this paper, we explore the penalized eigenvalue problem (PEP) which reformulates the computation of the first eigenvector as an optimization problem and adds an L1 penalty constraint.
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor
We demonstrate neuromorphic computing is suitable for sampling low energy solutions of binary sparse coding QUBO models, and although Loihi 1 is capable of sampling very sparse solutions of the QUBO models, there needs to be improvement in the implementation in order to be competitive with simulated annealing.
Boolean Hierarchical Tucker Networks on Quantum Annealers
Quantum annealing is an emerging technology with the potential to solve some of the computational challenges that remain unresolved as we approach an era beyond Moore's Law.
Quantum Physics
Reducing quantum annealing biases for solving the graph partitioning problem
We first quantify the bias of the implementation of the constraint on the quantum annealer, that is, we require, in an unbiased implementation, that any two vertices have the same likelihood of being assigned to the same or to different parts of the partition.
graph partitioning
Quantum Physics
Optimizing embedding-related quantum annealing parameters for reducing hardware bias
One way to deal with these imperfections and to improve the quality of the annealing results is to apply a variety of pre-processing techniques such as spin reversal (SR), anneal offsets (AO), or chain weights (CW).
