118 citations · 280 across the 17 of their papers we have counts for
6 papers · 1 filter
SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
Janik Kreit, Dominic Schuh, Kim A. Nicoli +1
Deep generative models have recently garnered significant attention across various fields, from physics to chemistry, where sampling from unnormalized Boltzmann-like distributions…
Multilevel Generative Samplers for Investigating Critical Phenomena
Ankur Singha, Elia Cellini, Kim A. Nicoli +3
Investigating critical phenomena or phase transitions is of high interest in physics and chemistry, for which Monte Carlo (MC) simulations, a crucial tool for numerically analyzing…
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers
Samuele Pedrielli, Christopher J. Anders, Lena Funcke +3
Parameter shift rules (PSRs) are key techniques for efficient gradient estimation in variational quantum eigensolvers (VQEs). In this paper, we propose its Bayesian variant, where…
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Kim A. Nicoli, Christopher J. Anders, Lena Funcke +7
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to ap…
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima +1
We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback-Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resul…
Path-Gradient Estimators for Continuous Normalizing Flows
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima +1
Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime i…