In this work, we propose a generalization of the current most widely used quantum computing hardware metric known as the quantum volume [1, 2]. The quantum volume specifies a family of random test circuits defined such that the logical circuit depth is equal to the total number of qubits used in the computation. However, such square circuit shapes do not directly relate to many specific applications for which one may wish to use a quantum computer. Based on surveying available resource estimates for known quantum algorithms, we generalize the quantum volume to a handful of representative circuit shapes, which we call Quantum Volumetric Classes, based on the scaling behavior of the logical circuit depth (time) with the problem size (qubit number).As a technology, quantum computing is in its infancy but developing rapidly. In the near term, noisy and intermediate-scale quantum (NISQ) systems may become useful for specific niche applications [3]. In the long term, with the development of fault-tolerant (FT) systems, this technology is expected be extremely disruptive and transformative. Clear metrics to evaluate this technology are
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