An introduction to quantum annealing

TL;DR

This survey introduces quantum annealing as a quantum-inspired heuristic for solving complex optimization problems, highlighting its principles, applications, and preliminary research on quantum dissipation techniques.

Contribution

It provides an overview of quantum annealing methods, their use in combinatorial optimization, and explores new approaches like quantum dissipation for system state preparation.

Findings

Quantum annealing offers effective heuristics for hard optimization problems.

Preliminary results suggest quantum dissipation can drive systems to low-energy states.

The survey bridges classical and quantum approaches in optimization.

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.