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Combinatorial optimization is anticipated to be one of the primary use casesfor quantum computation in the coming years. The Quantum ApproximateOptimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential todemonstrate significant run-time performance benefits over currentstate-of-the-art solutions. Using existing methods for characterizing classicaloptimization algorithms, we analyze solution quality obtained by solvingMax-Cut problems using a quantum annealing device and gate-model quantumsimulators and devices. This is used to guide the development of an advancedbenchmarking framework for quantum computers designed to evaluate the trade-offbetween run-time execution performance and the solution quality for iterativehybrid quantum-classical applications. The framework generates performanceprofiles through effective visualizations that show performance progression asa function of time for various problem sizes and illustrates algorithmlimitations uncovered by the benchmarking approach. The framework is anenhancement to the existing open-source QED-C Application-Oriented Benchmarksuite and can connect to the open-source analysis libraries. The suite can beexecuted on various quantum simulators and quantum hardware systems.

Optimization Applications as Quantum Performance Benchmarks