Quantum Annealing of Hard Problems
Thomas Jorg, Florent Krzakala, Jorge Kurchan, A. C. Maggs
TL;DR
This paper discusses quantum annealing as a tunneling-based optimization method, analyzing its performance on hard problems through statistical mechanics techniques, with mixed results from numerical simulations.
Contribution
It provides an analytical study of quantum annealing's efficiency on certain optimization problems using spin glass models.
Findings
Performance varies depending on problem class
Analytical methods reveal conditions for efficiency
Numerical simulations show mixed outcomes
Abstract
Quantum annealing is analogous to simulated annealing with a tunneling mechanism substituting for thermal activation. Its performance has been tested in numerical simulation with mixed conclusions. There is a class of optimization problems for which the efficiency can be studied analytically using techniques based on the statistical mechanics of spin glasses.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.