Benchmarking fault-tolerant quantum computing hardware via QLOPS

TL;DR

This paper introduces QLOPS, a benchmarking metric for evaluating fault-tolerant quantum computing hardware, considering error correction, accuracy, throughput, and latency to assess practical quantum algorithm execution.

Contribution

It proposes a comprehensive benchmarking framework using QLOPS to evaluate and compare fault-tolerant quantum computing schemes across different hardware platforms.

Findings

QLOPS reflects practical requirements for quantum algorithm execution.

The framework helps identify bottlenecks in quantum hardware.

It provides a basis for estimating resources needed for quantum algorithms.

Abstract

It is widely recognized that quantum computing has profound impacts on multiple fields, including but not limited to cryptography, machine learning, materials science, etc. To run quantum algorithms, it is essential to develop scalable quantum hardware with low noise levels and to design efficient fault-tolerant quantum computing (FTQC) schemes. Currently, various FTQC schemes have been developed for different hardware platforms. However, a comprehensive framework for the analysis and evaluation of these schemes is still lacking. In this work, we propose Quantum Logical Operations Per Second (QLOPS) as a metric for assessing the performance of FTQC schemes on quantum hardware platforms. This benchmarking framework will integrate essential relevant factors, e.g., the code rates of quantum error-correcting codes, the accuracy, throughput, and latency of the decoder. Through a resource analysis of factoring RSA-2048, we demonstrate that QLOPS reflects the practical requirements of quantum algorithm execution. This framework will enable the identification of bottlenecks in quantum hardware, providing potential directions for their development. Moreover, our results will help establish a comparative framework for evaluating FTQC designs. As this benchmarking approach considers practical applications, it may assist in estimating the hardware resources needed to implement quantum algorithms and offers preliminary insights into potential timelines.