Quantum Annealing and Analog Quantum Computation

TL;DR

This paper reviews recent advances in quantum annealing, a method that uses quantum fluctuations to optimize complex energy functions, providing a framework for analog quantum computation.

Contribution

It offers a comprehensive overview of quantum annealing, including problem mapping and the annealing process, highlighting its potential for analog quantum computation.

Findings

Quantum annealing effectively optimizes complex energy functions.

Mapping problems to quantum spin glasses is crucial for understanding annealing.

Quantum fluctuations play a key role in the annealing process.

Abstract

We review here the recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations. The concept is introduced in successive steps through the studies of mapping of such computationally hard problems to the classical spin glass problems. The quantum spin glass problems arise with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.