ArticleNature communications2024
Training an Ising machine with equilibrium propagation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Towards higher order oscillatory Ising machines.Scientific reports · 2026Article
- Combinatorial optimization with Kerr solitons.Science advances · 2026Article
- Intrinsic annealing in a hybrid memristor-magnetic tunnel junction Ising machine.Nature communications · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- SUANPAN: scalable photonic linear vector machine.Light, science & applications · 2026Article
- Trainable computation in molecular networks.bioRxiv : the preprint server for biology · 2025Article
- Training of physical neural networks.Nature · 2025Review
- Quantum equilibrium propagation for efficient training of quantum systems based on Onsager reciprocity.Nature communications · 2025Article
- Self-Contrastive Forward-Forward algorithm.Nature communications · 2025Article
- Temporal Contrastive Learning through implicit non-equilibrium memory.Nature communications · 2025Article
- Training all-mechanical neural networks for task learning through in situ backpropagation.Nature communications · 2024Article
- Machine learning without a processor: Emergent learning in a nonlinear analog network.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine's connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.