Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Nature

Quantum annealing with manufactured spins

Mark W. Johnson, M. H. S. Amin, S. Gildert, Trevor M. Lanting, Firas Hamze, Neil G. Dickson, R. Harris, A. J. Berkley, J. Johansson, P. Bunyk, E. M. Chapple, Colin Enderud, Jeremy Hilton, Kamran Karimi, E. Ladizinsky, N. Ladizinsky, T. Oh, I. Perminov, C. Rich, M. C. Thom, E. Tolkacheva, C. J. S. Truncik, Sergey V. Uchaikin, J. Wang, B. Wilson, Geordie Rose

Published May 1, 2011• 1,890 citations
Publisher
DOI
10.1038/nature10012

Related papers

Experiments on Quantum Annealing

G. Aeppli, T. F. Rosenbaum

Lecture notes in physics•Nov 16, 2005•2 citations

Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly

Helmut G. Katzgraber, Firas Hamze, Zheng Zhu +2 more

While manufacturing limitations are imposing constraints on Moore's law, researchers are searching for novel computing architectures based on quantum-mechanical effects. However, it remains to be shown that quantum annealing techniques consistently outperform classical simulated annealing to minimize optimization problems.

arXiv:1505.01545•Physical Review X•Sep 1, 2015•92 citations

Advantages of Unfair Quantum Ground-State Sampling

Brian Hu Zhang, Gene Wagenbreth, Víctor Martín-Mayor +1 more

The debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field. Recent technological breakthroughs, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. We examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a prototypical quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than thermal samplers. We demonstrate that (i) quantum annealers sample the ground-state manifolds of spin glasses very differently than thermal optimizers (ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution, and (iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms.

arXiv:1701.01524•Scientific Reports•Apr 13, 2017•26 citations

Assessing the Quantum in Quantum Annealing

Wade DeGottardi, David Ferguson, С. А. Новиков +2 more

Bulletin of the American Physical Society•Mar 7, 2019•0 citations

Beyond Standard Quantum Annealing

Tameem Albash

Bulletin of the American Physical Society•Mar 4, 2020•0 citations

When could NISQ algorithms start to create value in discrete manufacturing ?

Oxana Shaya

Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.

arXiv:2209.09650•Sep 17, 2022•0 citations
The debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field. Recent technological breakthroughs, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. We examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a prototypical quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than thermal samplers. We demonstrate that (i) quantum annealers sample the ground-state manifolds of spin glasses very differently than thermal optimizers (ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution, and (iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms.
Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.