16 citations · 26 across the 17 of their papers we have counts for
5 papers · 1 filter
NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
Yangjun Wu, Chu Guo, Yi Fan +2
Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampl…
Towards practical and massively parallel quantum computing emulation for quantum chemistry
Honghui Shang, Yi Fan, Li Shen +5
Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum comp…
Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems
Xiongzhi Zeng, Yi Fan, Jie Liu +2
The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-d…
Quantum circuit matrix product state ansatz for large-scale simulations of molecules
Yi Fan, Jie Liu, Zhenyu Li +1
As in the density matrix renormalization group (DMRG) method, approximating many-body wave function of electrons using a matrix product state (MPS) is a promising way to solve elec…
A real neural network state for quantum chemistry
Yangjun Wu, Xiansong Xu, Dario Poletti +3
The restricted Boltzmann machine (RBM) has been successfully applied to solve the many-electron Schrdinger equation. In this work we propose a single-layer fully c…