Solving Markov Chains with Analog Quantum Computing: The Fine Print

TL;DR

This paper examines the practical limitations of an analog quantum algorithm for computing Markov chain stationary distributions, emphasizing the need for more focus on real-world applicability of quantum algorithms.

Contribution

It extends the 'fine print' analysis to an analog quantum algorithm for Markov chains, highlighting practical challenges and applicability issues.

Findings

Identifies practical constraints of the analog quantum algorithm

Highlights the importance of applicability considerations in quantum algorithm development

Calls for increased focus on real-world implementation challenges

Abstract

With a growing interest in quantum computing, the number of proposed quantum algorithms grows as well. The practical applicability of these algorithms differs: Some can be applied out-of-the-box, while others require black box oracles, which can not always be easily implemented. One of the first works to explicitly discuss these practical applicability aspects is by Aaronson discussing the \textit{fine print} of the HHL quantum algorithm that solves linear systems of equations. We extend this line of research by providing a similar fine print for the first analog quantum algorithm that computes the stationary distribution of Markov chains. We conclude that more focus should be put on this practical applicability of quantum algorithms, either through a separate line of research, or through more attention when introducing the algorithm.