.png){kind=icon}
.png){kind=icon}
Return
Multidimensional hyperspin machine
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-022-34847-9.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and show that the hyperspin machine finds to a very good approximation the ground state of complex graphs. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call dimensional annealing. When interpolating between the XY and the Ising model, the dimensional annealing substantially increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies. Spin simulators can solve many combinatorial optimization problems that can be represented by spin models, but they are limited to low-dimensional spins. Here the authors propose a simulator of multidimensional spins in arbitrary dimension, using a system of coupled parametric oscillators with a common pump.
Keywords:
PHASE-DIAGRAM
ISING SPINS
RENORMALIZATION
EQUATION
NETWORK
MODEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15.7
Papers:
9.2W
Citations:
91.2W