Tensor lattice field theory for renormalization and quantum computing

Y Meurice, R Sakai, J Unmuth-Yockey - Reviews of modern physics, 2022 - APS
The successes and limitations of statistical sampling for a sequence of models studied in the
context of lattice QCD are discussed and the need for new methods to deal with finite …

Preparation of the SU (3) lattice Yang-Mills vacuum with variational quantum methods

AN Ciavarella, IA Chernyshev - Physical Review D, 2022 - APS
Studying QCD and other gauge theories on quantum hardware requires the preparation of
physically interesting states. The variational quantum eigensolver provides a way of …

Efficient representation for simulating U (1) gauge theories on digital quantum computers at all values of the coupling

CW Bauer, DM Grabowska - Physical Review D, 2023 - APS
We derive a representation for a lattice U (1) gauge theory with exponential convergence in
the number of states used to represent each lattice site that is applicable at all values of the …

Simulating Heisenberg interactions in the Ising model with strong drive fields

AN Ciavarella, S Caspar, H Singh, MJ Savage… - Physical Review A, 2023 - APS
The time evolution of an Ising model with large driving fields over discrete time intervals is
shown to be reproduced by an effective XXZ-Heisenberg model at leading order in the …

Phase diagram of generalized XY model using the tensor renormalization group

A Samlodia, V Longia, RG Jha, A Joseph - Physical Review D, 2024 - APS
We use the higher-order tensor renormalization group method to study the two-dimensional
generalized XY model that admits integer and half-integer vortices. This model is the …

Simulating effective QED on quantum computers

TF Stetina, A Ciavarella, X Li, N Wiebe - Quantum, 2022 - quantum-journal.org
In recent years simulations of chemistry and condensed materials has emerged as one of
the preeminent applications of quantum computing, offering an exponential speedup for the …

Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter

W Guo, L He - New Journal of Physics, 2023 - iopscience.iop.org
For performing regression tasks involved in various physics problems, enhancing the
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …

Reanalysis of critical exponents for the model via a hydrodynamic approach to the functional renormalization group

F Murgana, A Koenigstein, DH Rischke - Physical Review D, 2023 - APS
We reanalyze some critical exponents of the O (N) model within the functional
renormalization group (FRG) approach in the local potential approximation (LPA). We use …

Initial tensor construction and dependence of the tensor renormalization group on initial tensors

K Nakayama, M Schneider - Physical Review D, 2024 - APS
We propose a method to construct a tensor network representation of partition functions
without singular value decompositions nor series expansions. The approach is …

Floquet engineering heisenberg from ising using constant drive fields for quantum simulation

AN Ciavarella, S Caspar, H Singh, MJ Savage… - arXiv preprint arXiv …, 2022 - arxiv.org
The time-evolution of an Ising model with large driving fields over discrete time intervals is
shown to be reproduced by an effective XXZ-Heisenberg model at leading order in the …