Publications

Order by: publication date number of citations

• Sequential adiabatic generation of chiral topological states

  Xie Chen, Michael Hermele, David T. Stephen

  SciPost Phys. 19, 107 (2025) · published 23 October 2025 | Toggle abstract · pdf

  In previous work, it was shown that non-trivial gapped states can be generated from a product state using a sequential quantum circuit. Explicit circuit constructions were given for a variety of gapped states at exactly solvable fixed points. In this paper, we show that a similar generation procedure can be established for chiral topological states as well, despite the fact that they lack a zero-correlation-length exactly solvable form. Instead of sequentially applying local unitary gates, we sequentially evolve the Hamiltonian by changing local terms in one subregion and then the next. The Hamiltonian remains gapped throughout the process, giving rise to an adiabatic evolution mapping the ground state from a product state to a chiral topological state. We demonstrate such a sequential adiabatic generation process for free fermion chiral states like the Chern Insulator and the $p+ip$ superconductor. Moreover, we show that coupling a quantum state to a discrete gauge group can be achieved through a sequential quantum circuit, thereby generating interacting chiral topological states from the free fermion ones.

• Simplified derivations for high-dimensional convex learning problems

  David G. Clark, Haim Sompolinsky

  SciPost Phys. Lect. Notes 105 (2025) · published 23 October 2025 | Toggle abstract · pdf

  Statistical-physics calculations in machine learning and theoretical neuroscience often involve lengthy derivations that obscure physical interpretation. Here, we give concise, non-replica derivations of several key results and highlight their underlying similarities. In particular, using a cavity approach, we analyze three high-dimensional learning problems: perceptron classification of points, perceptron classification of manifolds, and kernel ridge regression. These problems share a common structure—a bipartite system of interacting feature and datum variables—enabling a unified analysis. Furthermore, for perceptron-capacity problems, we identify a symmetry that allows derivation of correct capacities through a naïve method.

• An introduction to large deviations with applications in physics

  Ivan N. Burenev, Daniël W. H. Cloete, Vansh Kharbanda, Hugo Touchette

  SciPost Phys. Lect. Notes 104 (2025) · published 23 October 2025 | Toggle abstract · pdf

  These notes are based on the lectures that one of us (HT) gave at the Summer School on the "Theory of Large Deviations and Applications," held in July 2024 at Les Houches in France. They present the basic definitions and mathematical results that form the theory of large deviations, as well as many simple motivating examples of applications in statistical physics, which serve as a basis for the many other lectures given at the school that covered more specific applications in biophysics, random matrix theory, nonequilibrium systems, geophysics, and the simulation of rare events, among other topics. These notes extend the lectures, which can be accessed online, by presenting exercises and pointer references for further reading.

• TorchGDM: A GPU-accelerated Python toolkit for multi-scale electromagnetic scattering with automatic differentiation

  Sofia Ponomareva, Adelin Patoux, Clément Majorel, Antoine Azéma, Aurélien Cuche, Christian Girard, Arnaud Arbouet, Peter R. Wiecha

  SciPost Phys. Codebases 60 (2025) · published 22 October 2025 | Toggle abstract · pdf

  We present "torchGDM", a numerical framework for nano-optical simulations based on the Green's Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green's tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical.

  1 citation

• Codebase release 0.56 for TorchGDM

  Sofia Ponomareva, Adelin Patoux, Clément Majorel, Antoine Azéma, Aurélien Cuche, Christian Girard, Arnaud Arbouet, Peter R. Wiecha

  SciPost Phys. Codebases 60-r0.56 (2025) · published 22 October 2025 | Toggle abstract · src

  We present "torchGDM", a numerical framework for nano-optical simulations based on the Green's Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green's tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical.

  1 citation

• Codebase release 2.0 for EERAD3

  Benjamin Campillo Aveleira, Aude Gehrmann-De Ridder, Thomas Gehrmann, Nigel Glover, Gudrun Heinrich, Christian T. Preuss

  SciPost Phys. Codebases 59-r2.0 (2025) · published 22 October 2025 | Toggle abstract · src

  We present a major update of the publicly available EERAD3 package to calculate perturbative corrections in the strong coupling in hadronic Higgs and $Z$-boson decays. We describe the theoretical framework underlying the numerical implementation and provide a guide to the usage of the program.

  1 citation

• EERAD3 version 2: QCD corrections in hadronic colour-singlet decays

  Benjamin Campillo Aveleira, Aude Gehrmann-De Ridder, Thomas Gehrmann, Nigel Glover, Gudrun Heinrich, Christian T. Preuss

  SciPost Phys. Codebases 59 (2025) · published 22 October 2025 | Toggle abstract · pdf

  We present a major update of the publicly available EERAD3 package to calculate perturbative corrections in the strong coupling in hadronic Higgs and $Z$-boson decays. We describe the theoretical framework underlying the numerical implementation and provide a guide to the usage of the program.

  1 citation

• The 3d $A$-model and generalised symmetries, part I: Bosonic Chern-Simons theories

  Cyril Closset, Elias Furrer, Adam Keyes, Osama Khlaif

  SciPost Phys. 19, 106 (2025) · published 21 October 2025 | Toggle abstract · pdf

  The 3d $A$-model is a two-dimensional approach to the computation of supersymmetric observables of three-dimensional $\mathcal{N}=2$ supersymmetric gauge theories. In principle, it allows us to compute half-BPS partition functions on any compact Seifert three-manifold (as well as of expectation values of half-BPS lines thereon), but previous results focussed on the case where the gauge group $\widetilde G$ is a product of simply-connected and/or unitary gauge groups. We are interested in the more general case of a compact gauge group $G=\widetilde G/\Gamma$, which is obtained from the $\widetilde G$ theory by gauging a discrete one-form symmetry. In this paper, we discuss in detail the case of pure $\mathcal{N}=2$ Chern-Simons theories (without matter) for simple groups $G$. When $G=\widetilde G$ is simply-connected, we demonstrate the exact matching between the supersymmetric approach in terms of Seifert fibering operators and the 3d TQFT approach based on topological surgery in the infrared Chern-Simons theory $\widetilde G_k$, including through the identification of subtle counterterms that relate the two approaches. We then extend this discussion to the case where the Chern-Simons theory $G_k$ can be obtained from $\widetilde G_k$ by the condensation of abelian anyons which are bosonic. Along the way, we revisit the 3d $A$-model formalism by emphasising its 2d TQFT underpinning.

• Higgs phases and boundary criticality

  Kristian Tyn Kai Chung, Rafael Flores-Calderón, Rafael C. Torres, Pedro Ribeiro, Sergej Moroz, Paul McClarty

  SciPost Phys. 19, 105 (2025) · published 21 October 2025 | Toggle abstract · pdf

  Motivated by recent work connecting Higgs phases to symmetry protected topological (SPT) phases, we investigate the interplay of gauge redundancy and global symmetry in lattice gauge theories with Higgs fields in the presence of a boundary. The core conceptual point is that a global symmetry associated to a Higgs field, which is pure-gauge in a closed system, acts physically at the boundary under boundary conditions which allow electric flux to escape the system. We demonstrate in both Abelian and non-Abelian models that this symmetry is spontaneously broken in the Higgs regime, implying the presence of gapless edge modes. Starting with the U(1) Abelian Higgs model in 4D, we demonstrate a boundary phase transition in the 3D XY universality class separating the bulk Higgs and confining regimes. Varying the boundary coupling while preserving the symmetries shifts the location of the boundary phase transition. We then consider non-Abelian gauge theories with fundamental and group-valued Higgs matter, and identify the analogous non-Abelian global symmetry acting on the boundary generated by the total color charge. For $\mathrm{SU}{N}$ gauge theory with fundamental Higgs matter we argue for a boundary phase transition in the $\mathrm{O}{2N}$ universality class, verified numerically for $N=2,3$. For group-valued Higgs matter, the boundary theory is a principal chiral model exhibiting chiral symmetry breaking. We further demonstrate this mechanism in theories with higher-form Higgs fields. We show how the higher-form matter symmetry acts at the boundary and can spontaneously break, exhibiting a boundary confinement-deconfinement transition. We also study the electric-magnetic dual theory, demonstrating a dual magnetic defect condensation transition at the boundary. We discuss some implications and extensions of these findings and what they may imply for the relation between Higgs and SPT phases.

  1 citation

• Post-hoc reweighting of hadron production in the Lund string model

  Benoît Assi, Christan Bierlich, Philip Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan

  SciPost Phys. 19, 104 (2025) · published 20 October 2025 | Toggle abstract · pdf

  We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both coverage considerations and timing benefits. For the purpose of this paper, our goal is to develop and demonstrate the the formalism, and we therefore exclude several model variations for baryon production (popcorn model, junction production) needed for proton collisions. These will be the topic of a future paper.