118 citations · 124 across the 10 of their papers we have counts for
13 papers
Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli +2
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce indep…
Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves
Ankur Singha, Jacob Finkenrath, Karl Jansen +2
Lattice gauge theories are an important class of physical models with high-dimensional structured distributions that underpin first-principles calculations in particle, nuclear, an…
Sector-Resolved Flow Sampling for Topologically Frozen Lattice Gauge Theories
Ankur Singha, Jacob Kauffmann, Karl Jansen +3
Topological fluctuations are essential to nonperturbative gauge theories but become increasingly difficult to sample toward the continuum limit, where Markov chains can freeze in s…
Prior-Informed Adaptive Shifts for Sequential Minimal Optimization in Variational Quantum Eigensolvers
Frederik Stalschus, Samuele Pedrielli, Stefan Kühn +3
Sequential minimal optimization methods, such as the Rotosolve and the Nakanishi-Fujii-Todo algorithm (NFT), are widely used for Variational Quantum Eigensolvers (VQEs). These meth…
Bias Analysis and Regularization of Sequential Minimal Optimization in Variational Quantum Eigensolvers
Samuele Pedrielli, Frederik Stalschus, Stefan Kühn +3
The Nakanishi Fujii Todo (NFT) algorithm, also known as Rotosolve, implements Sequential Minimal Optimization for Variational Quantum Eigensolvers (SMO-VQE) by exploiting the trigo…
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…
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…
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…
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…
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…
Improvement of Heatbath Algorithm in LFT using Generative models
Ali Faraz, Ankur Singha, Dipankar Chakrabarti +2
The Heatbath Algorithm is commonly used for sampling in local lattice field theories, but performing exact updates or sampling from the local density is challenging when dealing wi…
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Kim A. Nicoli, Christopher J. Anders, Lena Funcke +5
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…