2 code implementations • 8 Apr 2024 • Zihan Pengmei, Zimu Li
Graph Transformers have emerged as a powerful alternative to Message-Passing Graph Neural Networks (MP-GNNs) to address limitations such as over-squashing of information exchange.
1 code implementation • 2 Oct 2023 • Zihan Pengmei, Zimu Li, Chih-chan Tien, Risi Kondor, Aaron R. Dinner
We demonstrate SubFormer on benchmarks for predicting molecular properties from chemical structures and show that it is competitive with state-of-the-art graph transformers at a fraction of the computational cost, with training times on the order of minutes on a consumer-grade graphics card.
no code implementations • 5 Jun 2023 • Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.
no code implementations • 14 Nov 2022 • Zimu Li, Zihan Pengmei, Han Zheng, Erik Thiede, Junyu Liu, Risi Kondor
Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group.
no code implementations • 15 Jul 2022 • Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, Risi Kondor
We introduce a framework of the equivariant convolutional algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU($d$) symmetries.
1 code implementation • 14 Dec 2021 • Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, Risi Kondor
We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits.
no code implementations • 12 Sep 2021 • Junyu Liu, Zimu Li, Han Zheng, Xiao Yuan, Jinzhao Sun
Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices.