30 citations · 41 across the 2 of their papers we have counts for
6 papers
Scalable variational Monte Carlo with graph neural ansatz
Li Yang, Wenjun Hu, Li Li
Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this directi…
Quantum Optimization with a Novel Gibbs Objective Function and Ansatz Architecture Search
Li Li, Minjie Fan, Marc Coram +2
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansa…
Decoding Molecular Graph Embeddings with Reinforcement Learning
Steven Kearnes, Li Li, Patrick Riley
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previous…
Neural-Guided Symbolic Regression with Asymptotic Constraints
Li Li, Minjie Fan, Rishabh Singh +1
Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about t…
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou, Steven Kearnes, Li Li +2
We present a framework, which we call Molecule Deep -Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement l…
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes +4
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes…