10 papers
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…
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…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
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…
Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
Andrea Bulgarelli, Elia Cellini, Karl Jansen +5
We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches…