AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Together, these contributions show how learned representations, statistical control, and hardware constraints shape the exchange between quantum computing and machine learning.
Multi-shift SR (MS-SR), which averages independent ridge solves at data-adaptive shifts to form a richer, lower-variance spectral filter, is shown, which lowers validation risk and update variance relative to the fixed-shift SR baseline.
This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations.
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