Quantum utility -- definition and assessment of a practical quantum advantage

TL;DR

This paper introduces the concept of quantum utility, integrating physical and performance metrics to evaluate practical quantum advantage across various applications, and proposes a classification scheme for application readiness.

Contribution

It defines quantum utility considering physical footprint and performance, and develops a classification scheme for application readiness levels in quantum computing.

Findings

Quantum utility effectively measures practical quantum advantage.

Application readiness levels help classify quantum application maturity.

Demonstrated across quantum chemistry, simulation, machine learning, and data analysis.

Abstract

Several benchmarks have been proposed to holistically measure quantum computing performance. While some have focused on the end user's perspective (e.g., in application-oriented benchmarks), the real industrial value taking into account the physical footprint of the quantum processor are not discussed. Different use-cases come with different requirements for size, weight, power consumption, or data privacy while demanding to surpass certain thresholds of fidelity, speed, problem size, or precision. This paper aims to incorporate these characteristics into a concept coined quantum utility, which demonstrates the effectiveness and practicality of quantum computers for various applications where quantum advantage -- defined as either being faster, more accurate, or demanding less energy -- is achieved over a classical machine of similar size, weight, and cost. To successively pursue quantum utility, a level-based classification scheme -- constituted as application readiness levels (ARLs) -- as well as extended classification labels are introduced. These are demonstratively applied to different quantum applications from the fields of quantum chemistry, quantum simulation, quantum machine learning, and data analysis followed by a brief discussion.