The content discusses various research articles related to Ising machines, combinatorial optimization, and neural networks. It includes studies on hardware solvers of combinatorial optimization problems, energy-based models, deep learning, invertible logic, probabilistic computing, and quantum annealing. The references cover a wide range of topics including stochastic p-bits, magnetic tunnel junctions, restricted Boltzmann machines, and generative adversarial networks. The articles explore the applications of Ising machines in solving optimization problems with higher-order spin interactions, efficient optimization, and ultrafast statistical sampling. Additionally, the content includes information on hardware specifications such as the AMD Xilinx U250 data sheet and D-Wave Ocean documentation. The research articles cited in the content provide insights into the advancements in machine learning, quantum computing, and neural network training, showcasing the interdisciplinary nature of modern research in computational science and technology.
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Source link: https://www.nature.com/articles/s41928-024-01182-4
Training deep Boltzmann networks with sparse Ising machines #neuralnetworks
Preeti
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