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About this paper
Gavin S. Hartnett, Aaron Barbosa, Pranav S. Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum
A data-driven ranking method identifies optimal qubit layouts for quantum circuits, enhancing execution fidelity by accounting for context-dependent hardware errors. This machine learning approach, validated on IBM ibmq_guadalupe, reduced selection error by 1.8x compared to a common heuristic, achieving substantially higher circuit performance.
Key insights
- Context-dependent and coherent gate errors are the dominant factors influencing real-world circuit performance, which are not adequately addressed by standard hardware characterization methods.
- Optimizing for rank agreement using loss functions like Rank MSE is more effective for selecting the best performing layout than traditional approaches that aim to predict absolute fidelities.
- The data-driven approach implicitly learns complex hardware error landscapes from actual circuit executions, bypassing the need for explicit and costly tomographic characterization of all error sources.
Summary by alphaXiv.
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