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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 study NP-hard graphs of hyperspins. 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 impressively 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.
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Papers共 450 篇Author StatisticsCo-AuthorSimilar Experts
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IEEE Transactions on Terahertz Science and Technologyno. 99 (2024): 1-13
Richard Zhipeng Wang, James S. Cummins,Marvin Syed,Nikita Stroev, George Pastras, Jason Sakellariou, Symeon Tsintzos,Alexis Askitopoulos, Daniele Veraldi,Marcello Calvanese Strinati,Silvia Gentilini,Davide Pierangeli,Claudio Conti,Natalia G. Berloff
CoRR (2024)
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npj quantum informationno. 1 (2024)
NEW JOURNAL OF PHYSICSno. 3 (2024)
PHOTONICS RESEARCHno. 11 (2023): 1838-1846
PHYSICAL REVIEW Ano. 3 (2023)
arXiv (Cornell University) (2023)
arXiv (Cornell University) (2023)
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Author Statistics
#Papers: 450
#Citation: 12071
H-Index: 55
G-Index: 95
Sociability: 6
Diversity: 3
Activity: 98
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