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.
Support Vector Machines on Noisy Intermediate Scale Quantum Computers
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abstract
Support vector machine algorithms are considered essential for the implementation of automation in a radio access network. Specifically, they are critical in the prediction of the quality of user experience for video streaming based on device and network-level metrics. Quantum SVM is the quantum analogue of the classical SVM algorithm, which utilizes the properties of quantum computers to speed up the algorithm exponentially. In this work, we derive an optimized preprocessing unit for a quantum SVM that allows classifying any two-dimensional datasets that are linearly separable. We further provide a result readout method of the kernel matrix generation circuit to avoid quantum tomography that, in turn, reduces the quantum circuit depth. We also derive a quantum SVM system based on an optimized HHL quantum circuit with reduced circuit depth.
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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.