Multivariable Optimization: Quantum Annealing & Computation
Sudip Mukherjee, Bikas K. Chakrabarti
TL;DR
This paper discusses the potential of quantum annealing for solving complex NP-hard optimization problems, comparing it with simulated annealing and reviewing studies on spin glass and kinetically constrained systems.
Contribution
It provides a comparative analysis of quantum annealing and simulated annealing, highlighting recent developments and discussing applications to specific models.
Findings
Quantum annealing shows potential advantages over simulated annealing.
Studies on spin glass and kinetically constrained systems demonstrate quantum annealing's applicability.
The paper outlines beneficial features of quantum annealing techniques.
Abstract
Recent developments in quantum annealing techniques have been indicating potential advantage of quantum annealing for solving NP-hard optimization problems. In this article we briefly indicate and discuss the beneficial features of quantum annealing techniques and compare them with those of simulated annealing techniques. We then briefly discuss the quantum annealing studies of some model spin glass and kinetically constrained systems.
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