Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms cannot only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond is experimentally demonstrated. 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. This work demonstrate a full proof-of-principle verification for quantum anomaly detection algorithm, which has logarithmic resource consumption compared to its classical counterpart. With a hybrid spin system at ambient conditions, the quantum processor learns the characteristics of training samples and then identifies whether a new sample belongs to them. image