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In order to characterize and benchmark computational hardware, software, andalgorithms, it is essential to have many problem instances on-hand. This is noless true for quantum computation, where a large collection of real-worldproblem instances would allow for benchmarking studies that in turn help toimprove both algorithms and hardware designs. To this end, here we present alarge dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib(for Hamiltonian Library), is freely available online and contains problemsizes ranging from 2 to 1000 qubits. HamLib includes problem instances of theHeisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronicstructure, 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 probleminstances and map them to qubit representations, (b) to allow for more thoroughtests of new algorithms and hardware, and (c) to allow for reproducibility andstandardization across research studies.

HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware