  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.
