Upcoming seminars

More than thirty years ago Feynman and Deutsch came up with the quantum computer. A decade later Peter Shor had shown the incredible power of quantum computers - for example, their ability to factorize large numbers, an ability whose technological consequences for the world of internet encryption and banking can be devastating.

Recently, the basic concept of quantum coherence (or superposition) has gained a lot of renewed attention, after Baumgratz et al. [PRL 113:140401 (2014)], following ˚Aberg [arXiv:quant-ph/0612146], have proposed a resource theoretic approach to quantify it. This has resulted in a large number of papers and preprints exploring various coherence monotones, and debating possible forms for the resource theory. Here we take the view that the operational foundation of coherence in a state, be it quantum or otherwise wave mechanical, lies in the observation of interference effects.

Anomaly detection is used for identifying data that does not obey the `normal' data patterns and it nds diverse applications in many important areas like fraud detection, diagnoses, data cleaning and surveillance. With the advent of quantum computing and the future of quantum computing in the cloud and over the quantum internet, anomaly detection will inevitably play an important role in revealing novel or unusual patterns in communicated quantum data. Machine learning algorithms are playing pivotal roles in classical computing, including in anomaly detection.

Tensor network states have established themselves as the natural language based on entanglement to describe and numerically simulate quantum many-body systems. In this talk I will first provide a long introduction to what tensor network states are, as well as to some of their related numerical methods. An overall perspective on the field will also be provided. Then, I will review recent progress on this topic conducted in my group.