Quantum Machine Learning

Abstract

Machine Learning (ML) is becoming a more and more popular field of knowledge, being a term known not only in the academic field due to its successful applications to many real-world problems. The advent of Deep Learning and Big Data in the last decade has contributed to make it even more popular. Many companies, both large ones and SMEs, have created specific departments for ML and data analysis, being in fact their main activity in many cases. This current exploitation of ML should not mislead us; while it is a mature field of knowledge, there is still room for many novel contributions, namely, a better understanding of the underlying Mathematics, proposal and tuning of algorithms suitable for new problems (e.g., Natural Language Processing), automation and optimization of the search of parameters, etc. Within this framework of new contributions to ML, Quantum Machine Learning (QML) has emerged strongly lately, speeding up ML calculations and providing alternative representations to existing approaches. This special session includes six high-quality papers dealing with some of the most relevant aspects of QML, including analysis of learning in quantum computing and quantum annealers, quantum versions of classical ML models –like neural networks or learning vector quantization–, and quantum learning approaches for measurement and control.