Where Does a Variational Quantum Classifier Lose Accuracy? Angle Encoding, Readout and the Fidelity-Kernel Reference in Cyberattack Detection

arXiv:2611.00885v1 Announce Type: new Abstract: When a variational quantum classifier performs poorly at detecting network attacks, it is hard to tell which part is to blame: the encoding, the trained circuit or the readout, which turns measurements into a class. In this work, we build upon a recent amplitude-encoded classifier, and we try to separate their contributions to the NSL-KDD benchmark in noiseless simulation. We compare angle and amplitude encoding, vary one component at a time, and measure each circuit against a classical yardstick: a support-vector machine on the encoded states, whose accuracy estimates what a well-trained circuit can reach. Most of the shortfall lies in the readout.