… 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, …

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

… ; 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 …

… 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 …

… 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. …

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

… Going past HHL, here we survey several quantum algorithms … not rely on large-scale qRAM
is the quantum algorithm for … , and clear paths exist towards overcoming them. They must …

… For large-scale circuits with ideal expectation values of … can train the model to mitigate
expectation values towards values … for practitioners of quantum computation with noisy devices to …

… In Section 2.4, we examine the question of provable correctness of deep learning con… tum
algorithms depend on large-scale quantum computations to execute machine learning tasks. …