Towards provably efficient quantum algorithms for large-scale machine-learning models

6 March 2023

Papers citing "Towards provably efficient quantum algorithms for large-scale machine-learning models"

6 / 6 papers shown

Title
Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent Pingzhi Li Junyu Liu Hanrui Wang Tianlong Chen 76 1 0 30 Apr 2024
sQUlearn -- A Python Library for Quantum Machine Learning D. Kreplin Moritz Willmann Jan Schnabel Frederic Rapp Manuel Hagelüken M. Roth GP 22 9 0 15 Nov 2023
Carbon Emissions and Large Neural Network Training David A. Patterson Joseph E. Gonzalez Quoc V. Le Chen Liang Lluís-Miquel Munguía D. Rothchild David R. So Maud Texier J. Dean AI4CE 239 626 0 21 Apr 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 136 679 0 31 Jan 2021
Information-theoretic bounds on quantum advantage in machine learning Hsin-Yuan Huang R. Kueng J. Preskill 63 211 0 07 Jan 2021
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 153 232 0 04 Mar 2020