# Quantum Variational Rewinding for Time Series Anomaly Detection

@inproceedings{Baker2022QuantumVR, title={Quantum Variational Rewinding for Time Series Anomaly Detection}, author={Jack S Baker and Haim Horowitz and Santosh Kumar Radha and Stenio F. L. Fernandes and Colin Jones and Noor Fadiya Mohd Noor Ishak Hashim Mohd Salmi Noorani and V. Anatolievich Skavysh and Philippe Lamontangne and Barry C. Sanders}, year={2022} }

Electron dynamics, ﬁnancial markets and nuclear ﬁssion reactors, though seemingly unrelated, all produce observable characteristics evolving with time. Within this broad scope, departures from normal temporal behavior range from academically interesting to potentially catastrophic. New algorithms for time series anomaly detection (TAD) are therefore certainly in demand. With the advent of newly accessible quantum processing units (QPUs), exploring a quantum approach to TAD is now relevant and…

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