ArticleNature communications2022
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Analytical Prediction of Critical Transitions in Oscillator Networks With Non-Local Links.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hardware Acceleration of Stochastic Neural Networks Enabled by Bit-Cell Level Co-Design of Magnetic Tunnel Junctions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Precision meets speed through an FPGA-based natively sparse Ising machine for combinatorial optimization.Nature communications · 2026Article
- Combinatorial optimization with Kerr solitons.Science advances · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- Article
- A monolithically integrated optical Ising machine.Nature communications · 2025Article
- Training an Ising machine with equilibrium propagation.Nature communications · 2024Article
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
- Random Lasers as Social Processes Simulators.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training neural networks compared to digital computers. Here, we introduce a universal concept to achieve ultrafast statistical sampling with analog Ising machines by injecting noise. With an opto-electronic Ising machine, we experimentally demonstrate that this can be used for accurate sampling of Boltzmann distributions and for unsupervised training of neural networks, with equal accuracy as software-based training. Through simulations, we find that Ising machines can perform statistical sampling orders-of-magnitudes faster than software-based methods. This enables the use of Ising machines beyond combinatorial optimization and makes them into efficient tools for machine learning and other applications.
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Registered trials
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