Workshop on Quantum Machine Learning 2018

Machine learning has fundamentally changed the way humans interact with and relate to data, and achieved remarkable successes in many application areas. However, this revolution faces increasing challenges due to limited computational power and the growing size of datasets. Information is fundamentally governed by the laws of physics, and quantum mechanics can enhance our information-processing abilities. Unprecedented quantum advantages have been identified, such as Shor’s polynomial-time quantum algorithm for factorization, which compromises the widely-used RSA cryptosystem. Motivated by this and other possible applications of quantum information, the study of the impact of quantum mechanics on information processing has become a major research area over the past two decades.

 

Considering these developments, the time is ripe to build bridges between machine learning and quantum information. The interaction between these areas naturally goes both ways: machine learning algorithms find application in understanding and controlling quantum systems and, on the other hand, quantum computational devices promise enhancement of the performance of machine learning algorithms for problems beyond the reach of classical computing. The intersection of these two areas offers great potential for both fields.