From Monte Carlo to Quantum Computation

TL;DR

This paper introduces quantum computing concepts and surveys recent advances in high-dimensional integration, highlighting potential advantages over classical methods and comparing error rates of quantum and classical algorithms.

Contribution

It provides an overview of quantum algorithms for high-dimensional integration and analyzes their potential benefits compared to classical approaches.

Findings

Quantum algorithms can potentially outperform classical methods in high-dimensional integration.

The paper compares error rates of quantum and classical algorithms.

Connections between quantum algorithms and Monte Carlo methods are discussed.

Abstract

Quantum computing was so far mainly concerned with discrete problems. Recently, E. Novak and the author studied quantum algorithms for high dimensional integration and dealt with the question, which advantages quantum computing can bring over classical deterministic or randomized methods for this type of problem. In this paper we give a short introduction to the basic ideas of quantum computing and survey recent results on high dimensional integration. We discuss connections to the Monte Carlo methology and compare the optimal error rates of quantum algorithms to those of classical deterministic and randomized algorithms.