Learning in and about the quantum world

Talk

Quantum information science has made remarkable progress over recent decades, marked by advances in both the experimental realization of medium-scale quantum computers and theoretical breakthroughs in quantum information processing. Despite these achievements, major open questions persist: How can we scale quantum computations to tackle problems beyond the reach of classical computers? And where, in science and industry, can quantum technologies deliver a genuine quantum advantage?

In this talk, I will examine how the fields of machine learning and learning theory are shaping our understanding of these pivotal questions. I will highlight recent developments that shed light onto when and how quantum algorithms can outperform classical approaches in machine learning tasks, and outline the key challenges yet to overcome for this line of research to become practically impactful. I will also examine how concepts from learning theory and machine learning techniques can deepen our understanding of the quantum world and our ability to probe and control quantum systems.

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