Lawrence Berkeley National Laboratory

Quantum computing technologies are undergoing rapid development. The different qubit modalities being considered for quantum computing each have their strengths and weaknesses, making it challenging to compare their performance relative to each other and the state-of-the-art in classical high-performance computing. To better understand the utility of a given quantum processor and to assess when and how it will be able to advance the frontiers of computational science, researchers need a robust approach to quantum benchmarking. A variety of approaches have been proposed, many of which characterize the presence of noise in current quantum devices. These efforts include component-level performance metrics, such as randomized benchmarking and gate set tomography; high-level application-dependent metrics; and devicelevel metrics, such as the Quantum Volume. However, it remains unclear how low-level metrics, such as fidelities and decoherence times, and global device metrics, such as Quantum Volume, relate to the computational utility and practical limitations of quantum processors to solve useful problems. In this paper, we describe our Hamiltonian-oriented approach to quantum benchmarking called HamPerf. Where previous application-dependent approaches specify a suite of benchmarking circuits inspired by applications, we place the problem Hamiltonian at the center. Our strategy allows us to probe the computational performance of a quantum processor on standardized and relevant problem sets, agnostic of the algorithms and hardware used to solve them; it also provides fundamental insights into how device characteristics correlate with computational utility.