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Found 4 papers, 1 papers with code
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
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 approximate the ground state of a quantum Hamiltonian.
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
In this work, we first point out that the tunneling problem is also present for normalizing flows but is shifted from the sampling to the training phase of the algorithm.
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Crucially, these models allow for the direct estimation of the free energy at a given point in parameter space.
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic.
