In order to characterize and benchmark computational hardware, software, and algorithms, it is essential to have many problem instances on-hand. This is no less true for quantum computation, where a large collection of real-world problem instances would allow for benchmarking studies that in turn help to improve both algorithms and hardware designs. To this end, here we present a large dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib (for Hamiltonian Library), is freely available online and contains problem sizes ranging from 2 to 1000 qubits. HamLib includes problem instances of the Heisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronic structure, molecular vibrational structure, MaxCut, Max-$k$-SAT, Max-$k$-Cut, QMaxCut, and the traveling salesperson problem. The goals of this effort are (a) to save researchers time by eliminating the need to prepare problem instances and map them to qubit representations, (b) to allow for more thorough tests of new algorithms and hardware, and (c) to allow for reproducibility and standardization across research studies.

中文翻译：

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HamLib：用于对量子算法和硬件进行基准测试的哈密顿量库


为了对计算硬件、软件和算法进行表征和基准测试，手头必须有许多问题实例。对于量子计算来说也是如此，其中大量实际问题实例将允许进行基准测试研究，这反过来又有助于改进算法和硬件设计。为此，我们在这里提供了一个基于量子比特的量子哈密顿量的大型数据集。该数据集名为 HamLib（用于哈密顿库），可在线免费获得，包含从 2 到 1000 个量子比特的问题大小。HamLib 包括 Heisenberg 模型、Fermi-Hubbard 模型、Bose-Hubbard 模型、分子电子结构、分子振动结构、MaxCut、Max-$k$-SAT、Max-$k$-Cut、QMaxCut 和旅行推销员问题的问题实例。这项工作的目标是 （a） 无需准备问题实例并将其映射到量子比特表示形式，从而为研究人员节省时间，（b） 允许对新算法和硬件进行更彻底的测试，以及 （c） 允许跨研究的可重复性和标准化。