Adiabatic Quantum Anomaly Detection and Machine Learning

Sajid Ahmed Chowdhury, Shaikh Faiyaz Karim, Md. Shahriar Islam Bhuiyan +2 more

A. Senese, Elena Esposito, Saverio De Vito +2 more

Kristen L. Pudenz, Daniel A. Lidar

Zihua Chai, Ying Liu, Mengqi Wang +5 more

Abstract 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.

Tomoyuki Yamakami

Nana Liu, Patrick Rebentrost

Anomaly detection is used for identifying data that deviate from ``normal'' data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.