On the importance of scalability and resource estimation of quantum algorithms for domain sciences

TL;DR

This paper emphasizes the critical need for quantitative scalability and resource estimation in quantum algorithms for scientific applications to realistically assess their potential for quantum advantage.

Contribution

It highlights the importance of resource estimation and provides a case study comparing quantum and classical algorithms under simple assumptions.

Findings

Quantum algorithms require detailed resource estimates to evaluate their practical feasibility.

A case study demonstrates the potential quantum advantage in high energy physics simulations.

Standard benchmarks are necessary for credible claims of quantum advantage.

Abstract

The quantum information science community has seen a surge in new algorithmic developments across scientific domains. These developments have demonstrated polynomial or better improvements in computational and space complexity, incentivizing further research in the field. However, despite recent progress, many works fail to provide quantitative estimates on algorithmic scalability or quantum resources required -- e.g., number of logical qubits, error thresholds, etc. -- to realize the highly sought "quantum advantage." In this paper, we discuss several quantum algorithms and motivate the importance of such estimates. By example and under simple scaling assumptions, we approximate the computational expectations of a future quantum device for a high energy physics simulation algorithm and how it compares to its classical analog. We assert that a standard candle is necessary for claims of quantum advantage.