AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Z feature map was found to be immune to phase-flip noise to a significantly high error rate, a consequence of the commutation relation R_Z, Z = 0, and practitioners are given data-driven criteria for a feature map on near-term quantum hardware.
It is explained that the efficiently preparable states, device-generated distributions, variationally learned loading, and amortized preparation are required to get advantage from quantum machine learning and close with a checklist for evaluating input-dependent advantage claims.
It is shown that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring...
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