118 citations · 280 across the 17 of their papers we have counts for
8 papers · 1 filter
Computing quantum entanglement with machine learning
Andrea Bulgarelli, Elia Cellini, Karl Jansen +5
Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demo…
Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the e…
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…
Adaptive Observation Cost Control for Variational Quantum Eigensolvers
Christopher J. Anders, Kim A. Nicoli, Bingting Wu +6
The objective to be minimized in the variational quantum eigensolver (VQE) has a restricted form, which allows a specialized sequential minimal optimization (SMO) that requires onl…
Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers
Kim A. Nicoli, Luca J. Wagner, Lena Funcke
Variational Quantum Eigensolvers (VQEs) are a powerful class of hybrid quantum-classical algorithms designed to approximate the ground state of a quantum system described by its Ha…
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…
Simulating the Hubbard Model with Equivariant Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
Generative models, particularly normalizing flows, have shown exceptional performance in learning probability distributions across various domains of physics, including statistical…