5 citations · 7 across the 12 of their papers we have counts for
15 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…
Toward Hamiltonian simulations of Maxwell-Chern-Simons theory: constant modes and gauge field truncation
Andrea Bulgarelli, Maria Cristina Diamantini, Nico Dichter +6
Maxwell-Chern-Simons (MCS) theory in dimensions provides a paradigmatic example of a topological gauge theory with both dynamical and topological degrees of freedom. Its Eucl…
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…
Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
A. Singha, J. Kauffmann, E. Cellini +2
Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice…
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…
Small-scale Hamiltonian optimization of interpolating operators for Lagrangian lattice quantum field theory
Artur Avkhadiev, Lena Funcke, Karl Jansen +2
Lattice quantum field theory calculations may potentially combine the advantages of Hamiltonian formulations with the scalability and control of conventional Lagrangian frameworks.…
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…
Hamiltonian Lattice Formulation of Compact Maxwell-Chern-Simons Theory
Changnan Peng, Maria Cristina Diamantini, Lena Funcke +5
In this paper, a Hamiltonian lattice formulation for 2+1D compact Maxwell-Chern-Simons theory is derived. We analytically solve this theory and demonstrate that the mass gap in the…
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…