Search Results for author:
Found 2 papers, 0 papers with code
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning
Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization.
Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity
By simulating Ising machines with polynomial, periodic, sigmoid and clipped transfer functions and benchmarking them with MaxCut optimization problems, we find the choice of transfer function to have a significant influence on the calculation time and solution quality.
Applied Physics Emerging Technologies Computational Physics
Cannot find the paper you are looking for? You can
Submit a new open access paper.
