32 citations · 51 across the 6 of their papers we have counts for
6 papers
Neural network backflow for ab-initio solid calculations
An-Jun Liu, Bryan K. Clark
Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but fac…
Enhancing Neural Network Backflow
Kieran Loehr, Bryan K. Clark
Accurately describing the ground state of strongly correlated systems is essential for understanding their emergent properties. Neural Network Backflow (NNBF) is a powerful variati…
Efficient optimization of neural network backflow for ab-initio quantum chemistry
An-Jun Liu, Bryan K. Clark
The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQS) offer flexible and expressive wavefuncti…
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse +42
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet th…
Neural network backflow for ab-initio quantum chemistry
An-Jun Liu, Bryan K. Clark
The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wavefunctions based on ML architectures have shown p…
The effect of quantization on the FCIQMC sign problem
Michael H. Kolodrubetz, James S. Spencer, Bryan K. Clark +1
The sign problem in Full Configuration Interaction Quantum Monte Carlo (FCIQMC) without annihilation can be understood as an instability of the psi-particle population to the groun…