ArticleNature communications2024
All-to-all reconfigurability with sparse and higher-order Ising machines.
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 12 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
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.
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Who cites it
12 citing papers in PubMed.
- Towards higher order oscillatory Ising machines.Scientific reports · 2026Article
- Higher-order neuromorphic Ising machines-autoencoders and Fowler-Nordheim annealers are all you need for scalability.Nature communications · 2026Article
- Intrinsic annealing in a hybrid memristor-magnetic tunnel junction Ising machine.Nature communications · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- Accelerating hybrid XOR-CNF Boolean satisfiability problems natively with in-memory computing.Nature communications · 2026Article
- SUANPAN: scalable photonic linear vector machine.Light, science & applications · 2026Article
- A hardware demonstration of a universal programmable RRAM-based probabilistic computer for molecular docking.Nature communications · 2025Article
- Generalized Probabilistic Approximate Optimization Algorithm.Nature communications · 2025Article
- Efficient optimization accelerator framework for multi-state spin Ising problems.Nature communications · 2025Article
- Pushing the boundary of quantum advantage in hard combinatorial optimization with probabilistic computers.Nature communications · 2025Article
- Next-generation graph computing with electric current-based and quantum-inspired approaches.Nature communications · 2025Review
- Overcoming quadratic hardware scaling for a fully connected digital oscillatory neural network.Frontiers in neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Domain-specific hardware to solve computationally hard optimization problems has generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising Machines (IM) on the 3-Regular 3-Exclusive OR Satisfiability (3R3X), as a representative hard optimization problem. We first introduce a multiplexed architecture that emulates all-to-all network functionality while maintaining highly parallelized chromatic Gibbs sampling. We implement this architecture in a single Field-Programmable Gate Array (FPGA) and show that running the adaptive parallel tempering algorithm demonstrates competitive algorithmic and prefactor advantages over alternative IMs by D-Wave, Toshiba, and Fujitsu. We also implement higher-order interactions that lead to better prefactors without changing algorithmic scaling for the XORSAT problem. Even though FPGA implementations of p-bits are still not quite as fast as the best possible greedy algorithms accelerated on Graphics Processing Units (GPU), scaled magnetic versions of p-bit IMs could lead to orders of magnitude improvements over the state of the art for generic optimization.
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.