Colloquium- Junyu Liu (University of Chicago)- Quantum machine learning: from near-term to fault-tolerance

Abstract: Quantum technologies, such as quantum computing, are poised to revolutionize next-generation digital technologies by leveraging the principles of quantum mechanics, and are widely regarded as some of the most significant technologies of our era. Quantum machine learning, which involves running machine learning algorithms on quantum devices, is seen as a flagship application in this field. In my talk, I will explore two aspects of quantum machine learning: near-term algorithms and fault-tolerant algorithms. For near-term applications, I will delve into the use of variational quantum circuits in machine learning problems and discuss the quantum neural tangent kernel theory as an analytical tool for understanding and optimizing quantum neural networks. Regarding fault-tolerant applications that incorporate quantum error correction, I will present an end-to-end application of the HHL algorithm. This algorithm offers a provable, generic, and efficient approach to a range of machine learning challenges. Lastly, I will give various examples and insights on how my research is related to physics applications and how quantum machine learning could serve as a natural combination of physics and AI.