… Large machine learning models are revolutionary … -tolerant quantum computing could
possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, …

… Large machine learning models are revolutionary tech… quantum computing could possibly
provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, …

… The quantum computation time scales linearly with dataset size and quadratic for classical
post-processing. While our method scales in general exponentially in qubit number, we gain …

… ; they constitute concrete steps towards actually changing the … field of quantum computing,
unlocking a quantum advantage … more drastic in future large-scale quantum classifiers. In this …

… of quantum computation to realize superior machine learning … In the absence of large-scale
fault-tolerant quantum … Usman, Towards quantum enhanced adversarial robustness …

… been generalized to enable large-scale dynamical simulations … uses machine learning to
predict the quantum observables and … always keeps the maximal value towards the other side. …

… Since the depth of the neural network required to provably converge efficiently by gradient
descent … for improved machine learning methods amenable to quantum computing beyond the …

… The integration of machine learning with quantum computing … benchmarking of QML
models, probably leading to their … development of large-scale fault-tolerant quantum computers …

… for large-scale quantum models, the models need to be as … quantum algorithms is not a new
concept in and of itself [41… } to bias the model towards the (pretrained) equivariant XX model, …

… (DLA), we explain why quantum machine learning can generalize with few training data. …
, Towards provably efficient quantum algorithms for large-scale machine-learning models, …