In this work, variational circuits on a continuous-variable (CV) quantum computer are simulated using the PennyLane Framework. Utilizing the CV approach, it is possible to directly encode real numbers into each mode, which is an advantage for more complicated architectures. The necessary background theory in quantum optics and CV quantum computing is presented and used to deduce how neural network inspired circuits can be realized. Several toy machine learning tasks are solved using these circuits including regression, classification and solving an ODE.

Keywords: continuous-variable quantum computing, quantum machine learning, quantum neural networks