ReviewNature communications2025
Next-generation graph computing with electric current-based and quantum-inspired approaches.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Graph data is crucial for modeling complex relationships in various fields, but conventional graph computing methods struggle to handle increasingly intricate and large-scale graph data. Electric current-based graph computing and Quantum-inspired graph computing offer innovative hardware-based solutions to these challenges. Electric current-based graph computing has progressed from Euclidean graph data to non-Euclidean ones using the memristive crossbar arrays. This Perspective introduces various crossbar array-based electric current-based graph computings, which offer flexibility in representing complex graphs, enabling a wide range of graphical applications in materials, biology, and social science. It also discusses quantum-inspired graph computing, employing probabilistic bits, oscillatory neural networks, and related architectures to solve complex optimization problems. Electric current-based and quantum-inspired graph computing remain in their early stages of evolution, requiring further work to advance materials, devices, and architectures to fully realize their potential. These advancements will open opportunities for more diverse and complex real-world 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.