Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

The last few decades have seen significant breakthroughs in the fields of deep learning and
quantum computing. Research at the junction of the two fields has garnered an increasing …

Quantum computing is computing beyond classical computing based on quantum
phenomena such as superposition and entanglement. While quantum computing is still …

We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …

The permutational invariance of identical two-level systems allows for an exponential
reduction in the computational resources required to study the Lindblad dynamics of …

Optical cavity QED provides a platform with which to explore quantum many-body physics in
driven-dissipative systems. Single-mode cavities provide strong, infinite-range photon …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

We describe simulations of the quantum dynamics of a confocal cavity QED system that
realizes an intrinsically driven-dissipative spin glass. A close connection between open …

Quantum computing allows for the potential of significant advancements in both the speed
and the capacity of widely used machine learning techniques. Here we employ quantum …

We observe the joint spin-spatial (spinor) self-organization of a two-component Bose-
Einstein condensate (BEC) strongly coupled to an optical cavity. This unusual …