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Found 8 papers, 2 papers with code
How quantum computing can enhance biomarker discovery for multi-factorial diseases
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions.
Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
We introduce a novel technique to numerically calculate R\'enyi entanglement entropies in lattice quantum field theory using generative models.
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.
Symmetry breaking in geometric quantum machine learning in the presence of noise
This work studies the behavior of EQNN models in the presence of noise.
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.
Multilevel Monte Carlo for quantum mechanics on a lattice
This paper discusses hierarchical sampling methods to tame this growth in autocorrelations.
High Energy Physics - Lattice Numerical Analysis Numerical Analysis Computational Physics 81-08, 81T25, 65Y20, 60J22 F.2; J.2
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.
