Beyond Qubit Counts: Introducing IonQ’s Application-Centric Benchmarking Framework Quantum computing needs better benchmarks. Whether evaluating quantum for today's workloads or tomorrow's, every serious decision in quantum computing eventually comes down to one question: how do you measure real progress, and at what cost? The industry produces no shortage of numbers to answer it. Qubit counts, gate fidelities, circuit depths, coherence times. These figures are published constantly, but none of them actually answers the question. That is the problem this framework is built to solve. Our Benchmarking White Paper introduces a structured, application-centric framework for evaluating quantum computing systems. The framework is designed for the industry, not just IonQ. It covers 13 benchmarks across optimization, quantum chemistry, machine learning, data loading, simulation, and foundational algorithms. While this paper reports results on IonQ hardware, the framework is built to support evaluation across any quantum system, using metrics that connect directly to the value of the obtained solution. A Framework Built on Well-Informed Lessons The framework is inspired by MLPerf, the established standard for AI benchmarking (managed by NVIDIA, Microsoft, Amazon, Meta, Qualcomm, AMD, Intel, Arm, and many more). The structure is clear yet flexible: “Closed benchmarks” fix the implementation so that cross-platform comparison is a fair test of the system, not the algorithm. “Open benchmarks” fix the success criterion and permit algorithmic innovation, allowing teams to demonstrate advances without disclosing proprietary methods. In both cases, each benchmark has to disclose critical information to give the results the necessary context. The primary metrics are Time-to-Solution (TTS), Energy-to-Solution (ETS), and solution quality. TTS is the total wall time to reach a result that meets a predefined quality threshold, encompassing pre-processing, compilation, execution, and post-processing. In TTS benchmarks, that quality threshold is the figure of merit for the problem: it defines what constitutes a valid