Hi everyone, This summer, we will be hosting a biweekly Quantum Machine Learning (QML) journal club. The first meeting is this Wednesday (June 7th) at 2:30 PM, with invited speaker Sona Najafi from IBM. Hope to see you there! Best, Iria Wang, Rodrigo Araiza Bravo, Andi Gu, Hong-Ye Hu -- *QML Journal Club* *Wednesday, June 7th* *2:30 PM in LISE 303* *Zoom: * https://harvard.zoom.us/j/92778541116?pwd=SUk1RWRkcE9jdUczQy9DcDdIaXM0QT09 (password: 750575) *Sona Najafi*, Scientific Researcher at IBM *Overview of QML* Quantum machine learning has become one of the most progressing fields of quantum technology with applications in quantum optimization, quantum chemistry as well as quantum simulation. In this talk first I will review three distinct domains of quantum machine learning. Consequently, I will introduce novel quantum generative/variational algorithms based on quantum many-body localized (MBL) dynamics and show that it is capable of learning a toy dataset consisting of patterns of MNIST handwritten digits, quantum data obtained from quantum many-body states, and non-local parity data. I will theoretically prove that the MBL generative model possesses more expressive power than classical models, and the introduction of hidden units boosts its learning power. Finally, I will discuss quantum neuromorphic computing that capitalizes on inherent system dynamics and introduce the universal quantum perceptron (QP) based on interacting qubits with tunable coupling constants. By adding tunable single-qubit rotations to the QP, I will demonstrate that a QP can realize universal quantum computation, which contrasts sharply with the limited computational complexity of a single classical perceptron.