4 papers
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU() Symmetry
Han Zheng, Zimu Li, Sergii Strelchuk +2
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU sym…
Towards Symmetry-Aware Efficient Simulation of Quantum Systems and Beyond
Min Chen, Minzhao Liu, Changhun Oh +3
The efficient simulation of complex quantum systems remains a central challenge due to the exponential growth of Hilbert space with system size. Tensor network methods have long be…
Catapult Dynamics and Phase Transitions in Quadratic Nets
David Meltzer, Min Chen, Junyu Liu
Neural networks trained with gradient descent can undergo non-trivial phase transitions as a function of the learning rate. In \cite{lewkowycz2020large} it was discovered that wide…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…