Related papers: Accurate Gauge-Invariant Tensor Network Simulations for Abelian Lattice Gauge Theory in (2+1)D: ground state and real-time dynamics

Related papers

Simulating Lattice Gauge Theories with Virtual Rishons [0.0]
We develop a novel framework which enforces gauge symmetry via a quantum-link virtual rishon representation applied at intermediate steps.<n>We benchmark this framework in a U(1) gauge theory with and without matter fields.<n>These results establish the virtual rishon framework as a scalable and robust approach for the simulation of lattice gauge theories.
arXiv Detail & Related papers (2026-03-05T13:20:37Z)
Quantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors [40.72140849821964]
We consider the massive Thirring and Gross-Neveu models with arbitrary number of fermion flavors, $N_f$, discretized on a spatial one-dimensional lattice of size $L$.<n>We prepare the ground states of both models with excellent fidelity for system sizes up to 20 qubits with $N_f = 1,2,3,4$.<n>Our work is a concrete step towards the quantum simulation of real-time dynamics of large $N_f$ fermionic quantum field theories models.
arXiv Detail & Related papers (2026-02-25T19:00:01Z)
FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with $O(N\log N)$ Complexity [52.3171766248012]
We introduce a Markov Chain Monte Carlo (MCMC) algorithm that dramatically accelerates the simulation of quantum many-body systems.<n>We validate our algorithm on benchmark quantum physics problems, accurately reproducing known theoretical results.<n>Our work provides a powerful tool for large-scale probabilistic inference and opens avenues for physics-inspired generative models.
arXiv Detail & Related papers (2025-10-13T07:57:21Z)
Accurate ground states of $SU(2)$ lattice gauge theory in 2+1D and 3+1D [0.7999703756441757]
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation.<n>We learn a parameterization of the ground state wavefunction of $SU(2)$ lattice gauge theory in 2+1 and 3+1 dimensions.<n>Our method opens up an avenue for studying lattice gauge theories beyond one dimension, with efficient scaling to larger systems.
arXiv Detail & Related papers (2025-09-15T18:00:17Z)
Field digitization scaling in a $\mathbb{Z}_N \subset U(1)$ symmetric model [0.0]
We propose to analyze field digitization by interpreting the parameter $N$ as a coupling in the renormalization group sense.<n>Using effective field theory, we derive generalized scaling hypotheses involving the FD parameter $N$.<n>We analytically prove that our calculations for the 2D classical-statistical $mathbbZ_N$ clock model are directly related to the quantum physics in the ground state of a (2+1)D $mathbbZ_N$ lattice gauge theory.
arXiv Detail & Related papers (2025-07-30T18:00:02Z)
Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures [53.51230405648361]
We study the dynamics of gradient EM and employ tensor decomposition to characterize the geometric landscape of the likelihood loss.<n>This is the first global convergence and recovery result for EM or gradient EM beyond the special case of $m=2$.
arXiv Detail & Related papers (2025-06-06T23:32:38Z)
String-Membrane-Nets from Higher-Form Gauging: An Alternate Route to $p$-String Condensation [0.12499537119440243]
We show that gauging a 1-form symmetry in 3+1D that is generated by Abelian anyons in isotropic stacks of 2+1D topological orders naturally results in a 3+1D $p$-string condensed phase.<n>This approach clarifies the symmetry principles underlying $p$-string condensation and generalizes the familiar connection between anyon condensation and one-form gauging.
arXiv Detail & Related papers (2025-05-19T18:00:04Z)
Approximation of diffeomorphisms for quantum state transfers [49.1574468325115]
We seek to combine two emerging standpoints in control theory.<n>We numerically find control laws driving state transitions in small time in a bilinear Schr"odinger PDE posed on the torus.
arXiv Detail & Related papers (2025-03-18T17:28:59Z)
Quantum Simulation of non-Abelian Lattice Gauge Theories: a variational approach to $\mathbb{D}_8$ [0.0]
We show a procedure that removes the matter and improves the efficiency of the hardware resources.<n>We map the lattice gauge theory onto qudit systems with local interactions.<n>This can serve as a way of simulating lattice gauge theories in high spatial dimensions.
arXiv Detail & Related papers (2025-01-29T18:59:59Z)
GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
arXiv Detail & Related papers (2024-12-23T18:58:17Z)
Quantum and classical spin network algorithms for $q$-deformed Kogut-Susskind gauge theories [0.0]
We introduce $q$-deformed Kogut-Susskind lattice gauge theories, obtained by deforming the defining symmetry algebra to a quantum group. Our proposal simultaneously provides a controlled regularization of the infinite-dimensional local Hilbert space while preserving essential symmetry-related properties. Our work gives a new perspective for the application of tensor network methods to high-energy physics.
arXiv Detail & Related papers (2023-04-05T15:49:20Z)
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles [55.41644538483948]
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the transformations symmetry and the corresponding generators. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.
arXiv Detail & Related papers (2023-01-13T16:25:25Z)
General quantum algorithms for Hamiltonian simulation with applications to a non-Abelian lattice gauge theory [44.99833362998488]
We introduce quantum algorithms that can efficiently simulate certain classes of interactions consisting of correlated changes in multiple quantum numbers. The lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions coupled to one flavor of staggered fermions. The algorithms are shown to be applicable to higher-dimensional theories as well as to other Abelian and non-Abelian gauge theories.
arXiv Detail & Related papers (2022-12-28T18:56:25Z)
Discrete Abelian lattice gauge theories on a ladder and their dualities with quantum clock models [0.0]
We study a duality transformation from the gauge-invariant subspace of a $mathbbZ_N$ lattice gauge theory to an $N$-clock model on a single chain. The main feature of this mapping is the emergence of a longitudinal field in the clock model, whose value depends on the superselection sector of the gauge model.
arXiv Detail & Related papers (2022-08-05T15:14:18Z)
Quantum Error Correction with Gauge Symmetries [69.02115180674885]
Quantum simulations of Lattice Gauge Theories (LGTs) are often formulated on an enlarged Hilbert space containing both physical and unphysical sectors. We provide simple fault-tolerant procedures that exploit such redundancy by combining a phase flip error correction code with the Gauss' law constraint.
arXiv Detail & Related papers (2021-12-09T19:29:34Z)
Primitive Quantum Gates for Dihedral Gauge Theories [0.0]
We describe the simulation of dihedral gauge theories on digital quantum computers. The nonabelian discrete gauge group $D_N$ serves as an approximation to $U(1)timesbbZ$ lattice gauge theory.
arXiv Detail & Related papers (2021-08-30T15:16:47Z)
Quantum double aspects of surface code models [77.34726150561087]
We revisit the Kitaev model for fault tolerant quantum computing on a square lattice with underlying quantum double $D(G)$ symmetry. We show how our constructions generalise to $D(H)$ models based on a finite-dimensional Hopf algebra $H$.
arXiv Detail & Related papers (2021-06-25T17:03:38Z)
Investigating a (3+1)D Topological $\ heta$-Term in the Hamiltonian Formulation of Lattice Gauge Theories for Quantum and Classical Simulations [0.0]
We derive the (3+1)D topological $theta$-term for Abelian and non-Abelian lattice gauge theories. We study numerically the zero-temperature phase structure of a (3+1)D U(1) lattice gauge theory.
arXiv Detail & Related papers (2021-05-13T01:10:42Z)
Cold Atom Quantum Simulator for String and Hadron Dynamics in Non-Abelian Lattice Gauge Theory [0.0]
Scheme calls for the realization of a two-state ultracold fermionic system in a 1-dimensional bipartite lattice. Being based on novel loop string hadron formalism of SU(2) lattice gauge theory, this simulation technique is completely SU(2) invariant.
arXiv Detail & Related papers (2020-09-29T12:39:14Z)
Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
We study two different methods to prepare excited states on a quantum computer. We benchmark these techniques on emulated and real quantum devices. These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
arXiv Detail & Related papers (2020-09-28T17:21:25Z)
Variational Monte Carlo simulation with tensor networks of a pure $\mathbb{Z}_3$ gauge theory in (2+1)d [0.688204255655161]
Variational minimization of tensor network states enables the exploration of low energy states of lattice gauge theories. It is possible to efficiently compute physical observables using a variational Monte Carlo procedure. This is a first proof of principle to the method, which provides an inherent way to increase the number of variational parameters.
arXiv Detail & Related papers (2020-08-03T13:59:42Z)
Stochastic Flows and Geometric Optimization on the Orthogonal Group [52.50121190744979]
We present a new class of geometrically-driven optimization algorithms on the orthogonal group $O(d)$. We show that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, flows and metric learning.
arXiv Detail & Related papers (2020-03-30T15:37:50Z)
Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
arXiv Detail & Related papers (2020-02-25T19:18:36Z)
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling [56.394284787780364]
This paper provides the first theoretical convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning. Under general nonlinear function approximation, PG-AMSGrad with a constant stepsize converges to a neighborhood of a stationary point at the rate of $mathcalO(log T/sqrtT)$. Under linear function approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood of the global optimum at the rate of $mathcalO(log T/sqrtT
arXiv Detail & Related papers (2020-02-15T00:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.