Optimization Applications as Quantum Performance Benchmarks

Understand

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years.

• The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions.
• Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems using gate-model quantum devices and a quantum annealing device.
• This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications.