An introduction to quantum annealing

Abstract

Quantum Annealing, or Quantum Stochastic Optimization, is a classical randomized algorithm which provides good heuristics for the solution of hard optimization problems. The algorithm, suggested by the behaviour of quantum systems, is an example of proficuous cross contamination between classical and quantum computer science. In this survey paper we illustrate how hard combinatorial problems are tackled by quantum computation and present some examples of the heuristics provided by Quantum Annealing. We also present preliminary results about the application of quantum dissipation (as an alternative to Imaginary Time Evolution) to the task of driving a quantum system toward its state of lowest energy.

References in corpus (7)

• Mathematical Foundation of Quantum Annealing
• How Powerful is Adiabatic Quantum Computation?
• Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
• An Elementary Proof of the Quantum Adiabatic Theorem
• Efficient algorithm for a quantum analogue of 2-SAT
• Speed and entropy of an interacting continuous time quantum walk
• Quantum Annealing for Clustering