  In the processing of quantum computation, analyzing and learning the pattern
of the quantum data are essential for many tasks. Quantum machine learning
algorithms can not only deal with the quantum states generated in the preceding
quantum procedures, but also the quantum registers encoding classical problems.
In this work, we experimentally demonstrate the anomaly detection of quantum
states encoding audio samples with a three-qubit quantum processor consisting
of solid-state spins in diamond. By training the quantum machine with a few
normal samples, the quantum machine can detect the anomaly samples with a
minimum error rate of 15.4%. These results show the power of quantum anomaly
detection in dealing with machine learning tasks and the potential to detect
abnormal output of quantum devices.
