ArticleNature communications2026
250 magnetic tunnel junctions-based probabilistic Ising machine.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
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Who cites it
2 citing papers in PubMed.
- A parallel magnetic tunnel junction-based probabilistic Ising processor for efficient quadratic optimization.Nature communications · 2026Article
- A hardware demonstration of a universal programmable RRAM-based probabilistic computer for molecular docking.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In combinatorial optimization, probabilistic Ising machines have gained significant attention for their acceleration of Monte-Carlo sampling with the potential to reduce time-to-solution in finding approximate ground states. However, to be viable in real applications, further advances in scalability and energy efficiency are necessary. Here, we experimentally demonstrate a scalable probabilistic Ising machine based on 250 spin-transfer-torque magnetic tunnel junctions. Our computing approach integrates spintronic tunable true random number generators with advanced annealing techniques. For sparsely connected graphs, the proposed massive parallel architecture enables a cluster parallel update method that overcomes the serial limitations of Gibbs sampling, leading to a 10 times acceleration without hardware changes. Furthermore, we prove experimentally that the simulated quantum annealing boosts solution quality 20 times over conventional simulated annealing while also increasing robustness to device variability. In addition, we propose a next generation chiplet-based architecture for future large-scale, high-performance, and energy-efficient unconventional computing hardware.
Identifiers
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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.