  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 is the topic of this work. Our approach -
Quantum Variational Rewinding, or, QVR - trains a family of parameterized
unitary time-devolution operators to cluster normal time series instances
encoded within quantum states. Unseen time series are assigned an anomaly score
based upon their distance from the cluster center, which, beyond a given
threshold, classifies anomalous behavior. After a first demonstration with a
simple and didactic case, QVR is used to study the real problem of identifying
anomalous behavior in cryptocurrency market data. Finally, multivariate time
series from the cryptocurrency use case are studied using IBM's Falcon r5.11H
family of superconducting transmon QPUs, where anomaly score errors resulting
from hardware noise are shown to be reducible by as much as 20% using advanced
error mitigation techniques.
