Benchmarking quantum computers helps to quantify them and bringing the technology to the market. Various application-level metrics exist to benchmark a quantum device at an application level. This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS) incorporating strong points of previous metrics, such as QPack and the Q-score. We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers. We evaluate the new metric on different hardware platforms such as D- Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.

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Currently, quantum computing is making large steps to becoming a mature technology. Many companies are developing their own quantum hardware for both research and preparing for practical deployment. The main challenges in the past years have been an abundance of practical applications for the few-qubit Noisy Intermediate-Scale Quantum (NISQ) quantum hardware and scaling the qubits and depth of the quantum hardware. For these reasons, quantum computing was not yet at the maturity level needed to create a useful application level quantumbenchmark. Many quantum benchmarks have been developed, but these either aim at qubit level assessment or are lacking in practical usability. For a benchmark to be useful, the practical application of the quantum hardware needs to be reflected. For this a practical application is required and this was not yet achievable with current quantum hardware scales. Benchmarks at the component level (individual qubits, quantum logic gates) have been developed widely and are useful for the development of the hardware. This is, however, more aimed at basic quantum research rather than application [21]. This also serves a fundamentally different goal than a performance measure of quantum computers. Many different forms of quantum hardware are being used, without a clear dominating implementation. Each of these have different dynamics and metrics, and can therefore not be generalized [97]. For this reason, making a benchmark for low-level hardware aspectswill not properly reflect its performance compared to other implementations of the quantum hardware. Furthermore, while benchmarks for single qubit operations have been developed [32, 36, 70, 102], these performances do not accurately reflect quantum operations on larger scales. Noise levels, for example, are an important metric in single-gate performance. The noise, however, varies per gate and due to qubit entanglement will propagate unpredictably throughout the system [38, 43, 97]. This makes it impossible to use single gate noise performance to extrapolate for the entire system, while for classical computers this would have been possible. These difficulties in determining the performance will require abstraction from the low level performance and an application level benchmark is needed to properly verify the performance.