Abstract:
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 like 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, is expected to 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 kernel principal component analysis and 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 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.
Biography:
Nana Liu is currently a Postdoctoral Research Fellow at the Centre for Quantum Technologies in the National University of Singapore and the Singapore University for Technology and Design. She received her doctorate in Atomic and Laser Physics in 2016 from the University of Oxford as a Clarendon Scholar. Her research focus is on the advantages of using quantum resources for computation, in particular using light. Applications include using quantum computers for machine learning and quantum computation in the cloud. Her research also lies at the interface between quantum computation and security which will be useful in building a future quantum internet.
Seminar by the NYU-ECNU Institute of Physics at NYU Shanghai