3 citations · 3 across the 6 of their papers we have counts for
8 papers
Parametric Neural Quantum States for Nuclear Physics
Yukari Yamauchi, Ryan Curry, Garrett B. King +3
We introduce a new approach, based on neural quantum states (NQSs), to rapidly compute nuclear observables when couplings in nuclear Hamiltonians are varied. A single interaction-d…
Light nuclear scattering from neural quantum states
Scott Lawrence, Yukari Yamauchi
We present a method of studying few-body nuclear scattering by means of neural quantum states, without requiring time-evolution. A recently developed family of stable minimum princ…
Stable minimum principles for scattering states
Scott Lawrence, Yukari Yamauchi
Quantum-mechanical scattering states are energy eigenstates obeying particular boundary conditions, whose behavior at infinity encodes the S-matrix which defines the outcoming of s…
Efficient Learning of Lattice Gauge Theories with Fermions
Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov +2
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the S…
Quantum states from normalizing flows
Scott Lawrence, Arlee Shelby, Yukari Yamauchi
We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows. The use of normalizing flows enables efficient uncorrel…
Contour deformations for non-holomorphic actions
Scott Lawrence, Semeon Valgushev, Jianan Xiao +1
We show how contour deformations may be used to control the sign problem of lattice Monte Carlo calculations with non-holomorphic actions. Such actions arise naturally in quantum m…
Mitigating a discrete sign problem with extreme learning machines
Scott Lawrence, Yukari Yamauchi
An extreme learning machine is a neural network in which only the weights in the last layer are changed during training; for such networks training can be performed efficiently and…
Convex optimization and contour deformations
Scott Lawrence, Yukari Yamauchi
We discuss various formal aspects of contour deformations used to alleviate sign problems; most importantly, relating these contour deformations to a certain convex optimization pr…