On a synthetic financial dataset, one-class SVMs using projected quantum kernels achieved higher mean F1 scores than a classical rbf-kernel baseline at every tested anomaly ratio, both in simulation and on quantum hardware, though the statistical evidence is weak.
Learning with kernels: support vector machines, reg- ularization, optimization, and beyond
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Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors
On a synthetic financial dataset, one-class SVMs using projected quantum kernels achieved higher mean F1 scores than a classical rbf-kernel baseline at every tested anomaly ratio, both in simulation and on quantum hardware, though the statistical evidence is weak.