Quantum Optimization of Fully-Connected Spin Glasses
By Davide Venturelli, S. Mandrá, S. Knysh, B. O’Gorman, R. Biswas, Vadim N. Smelyanskiy. 2014
241 citations recorded by the source corpus.
The Sherrington-Kirkpatrick model with random $\pm1$ couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph. The performance of the optimizer compares and correlates to simulated annealing. When considering the effect of the static noise, which degrades the performance of the annealer, one can estimate an improvement on the comparative scaling of the two methods in favor of the D-Wave machine. The optimal choice of parameters of the embedding on the Chimera graph is shown to be associated to the emergence of the spin-glass critical temperature of the embedded problem.
Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Condensed Matter Physics
View Paper: https://www.semanticscholar.org/paper/2f825737485b7c899e0ac5368e8f4b8284aaf876
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In Artificial Intelligence
In Atomic and Molecular Physics, and Optics
Citations in the corpus
- Quantum annealing for industry applications: introduction and review (2021, unknown): “In the context of perturbed problem Hamiltonians, Ven-turelli et al [180] compared QA and simulated thermal annealing to measure the effects of disorder in ground state distributions on the Sherrington–Kirkpatrick model (SKM) with couplings randomized from a bimodal distribution of values ± 1 [162].”
- Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer (2018, methodology): “Each instance has been solved 100 times using SA and the temperature schedule has been set to be linear in the inverse temperature, which is a typical choice for SA in the literature [46, 47].”