Reproduce “Predicting Good Quantum Circuit Compilation Options” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Nils Quetschlich, Lukas Burgholzer, Robert Wille
Researchers at Technical University of Munich and Johannes Kepler University Linz developed a machine learning framework that predicts optimal quantum circuit compilation options for specific hardware and compilers, achieving 77% accuracy in selecting the single best option and reducing compilation time by over an order of magnitude compared to brute-force methods. The framework helps non-experts efficiently navigate the complex choices of qubit technology, devices, compilers, and settings.
Key insights
- Supervised machine learning, especially Random Forest, can accurately predict the best quantum circuit compilation options, achieving 77% accuracy and 96% top-3 accuracy.
- The selection of appropriate compilation options (device, compiler, settings) drastically impacts a circuit's expected fidelity, validating the need for automated guidance.
- Circuit features like qubit count and composite metrics (e.g., entanglement ratio, parallelism) are crucial predictors for optimal compilation choices, offering insights for future research.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.