Learning to Program Variational Quantum Circuits with Fast Weights

arXiv:2402.17760v1 Announce Type: cross
Abstract: Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages notably within domains such as Reinforcement Learning (RL) and time-series prediction. A significant advancement lies in Quantum Recurrent Neural Networks (QRNNs), specifically tailored for memory-intensive tasks encompassing partially observable environments and non-linear time-series prediction. Nevertheless, QRNN-based models encounter challenges, notably prolonged training duration stemming from the necessity to compute quantum gradients using …

abstract advancement advantages arxiv control cs.ai cs.et cs.lg cs.ne domains framework lies machine machine learning modeling networks neural networks prediction qml quant-ph quantum recurrent neural networks reinforcement reinforcement learning series tasks type