  In this paper, we introduce HamilToniQ, an open-source, and
application-oriented benchmarking toolkit for the comprehensive evaluation of
Quantum Processing Units (QPUs). Designed to navigate the complexities of
quantum computations, HamilToniQ incorporates a methodological framework
assessing QPU types, topologies, and multi-QPU systems. The toolkit facilitates
the evaluation of QPUs' performance through multiple steps including quantum
circuit compilation and quantum error mitigation (QEM), integrating strategies
that are unique to each stage. HamilToniQ's standardized score, H-Score,
quantifies the fidelity and reliability of QPUs, providing a multidimensional
perspective of QPU performance. With a focus on the Quantum Approximate
Optimization Algorithm (QAOA), the toolkit enables direct, comparable analysis
of QPUs, enhancing transparency and equity in benchmarking. Demonstrated in
this paper, HamilToniQ has been validated on various IBM QPUs, affirming its
effectiveness and robustness. Overall, HamilToniQ significantly contributes to
the advancement of the quantum computing field by offering precise and
equitable benchmarking metrics.
