Optimization Applications as Quantum Performance Benchmarks
- URL: http://arxiv.org/abs/2302.02278v2
- Date: Thu, 1 Feb 2024 21:01:47 GMT
- Title: Optimization Applications as Quantum Performance Benchmarks
- Authors: Thomas Lubinski, Carleton Coffrin, Catherine McGeoch, Pratik Sathe,
Joshua Apanavicius, David E. Bernal Neira
- Abstract summary: Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years.
Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems.
This is used to guide the development of an advanced benchmarking framework for quantum computers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The framework generates performance
profiles through compelling visualizations that show performance progression as
a function of time for various problem sizes and illustrates algorithm
limitations uncovered by the benchmarking approach. As an illustration, we
explore the factors that influence quantum computing system throughput, using
results obtained through execution on various quantum simulators and quantum
hardware systems.
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