Publications

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Phase transitions in quantum dot-Majorana zero mode coupling systems

Yue Mao, Qing-Feng Sun

SciPost Phys. Core 8, 031 (2025) · published 11 March 2025 | · pdf

The magnetic doublet ground state (GS) of a quantum dot (QD) could be changed to a spin-singlet GS by coupling to a superconductor. In analogy, here we study the GS phase transitions in QD-Majorana zero mode (MZM) coupling systems: GS behaves phase transition versus intra-dot energy level and QD-MZM coupling strength. The phase diagrams of GS are obtained, for cases with and without Zeeman term. Along with the phase transition, we also study the change of spin feature and density of states. The properties of the phase transition are understood via a mean-field picture. Our study not only serves as an analogue to QD-superconductor phase transitions, but also gives alternative explanations on MZM-relevant experiments.
Recipes for the digital quantum simulation of lattice spin systems

Guido Burkard

SciPost Phys. Core 8, 030 (2025) · published 10 March 2025 | · pdf

We describe methods to construct digital quantum simulation algorithms for quantum spin systems on a regular lattice with local interactions. In addition to tools such as the Trotter-Suzuki expansion and graph coloring, we also discuss the efficiency gained by parallel execution of an extensive number of commuting terms. We provide resource estimates and quantum circuit elements for the most important cases and classes of spin systems. As resource estimates we indicate the total number of gates $N$ and simulation time $T$, expressed in terms of the number $n$ of spin 1/2 lattice sites (qubits), target accuracy $\epsilon$, and simulated time $t$. We provide circuit constructions that realize the simulation time $T^{(1)}\propto nt^2/\epsilon$ and $T^{(2q)}\propto t^{1+\eta}n^\eta/\epsilon^\eta$ for arbitrarily small $\eta = 1/2q$ for the first-order and higher-order Trotter expansions. We also discuss the potential impact of scaled gates, which have not yet been fully explored.
Identifying quantum phase transitions with minimal prior knowledge by unsupervised learning

Mohamad Ali Marashli, Ho Lai Henry Lam, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki, Ho-Kin Tang, Wing Chi Yu

SciPost Phys. Core 8, 029 (2025) · published 6 March 2025 | · pdf

In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.
Characterizing far from equilibrium states of the one-dimensional nonlinear Schrödinger equation

Abhik Kumar Saha, Romain Dubessy

SciPost Phys. Core 8, 028 (2025) · published 6 March 2025 | · pdf

We use the mathematical toolbox of the inverse scattering transform to study quantita- tively the number of solitons in far from equilibrium one-dimensional systems described by the defocusing nonlinear Schrödinger equation. We present a simple method to iden- tify the discrete eigenvalues in the Lax spectrum and provide a extensive benchmark of its efficiency. Our method can be applied in principle to all physical systems described by the defocusing nonlinear Schrödinger equation and allows to identify the solitons velocity distribution in numerical simulations and possibly experiments.
Bayesian RG flow in neural network field theories

Jessica N. Howard, Marc S. Klinger, Anindita Maiti, Alexander G. Stapleton

SciPost Phys. Core 8, 027 (2025) · published 5 March 2025 | · pdf

The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme that generalizes the principles of the exact renormalization group (ERG) to arbitrarily parameterized probability distributions, including those of NNs. In BRG, coarse graining is performed in parameter space with respect to an information-theoretic distinguishability scale set by the Fisher information metric. In this paper, we unify NNFT and BRG to form a powerful new framework for exploring the space of NNs and SFTs, which we coin BRG-NNFT. With BRG-NNFT, NN training dynamics can be interpreted as inducing a flow in the space of SFTs from the information-theoretic 'IR' $→$ 'UV'. Conversely, applying an information-shell coarse graining to the trained network's parameters induces a flow in the space of SFTs from the information-theoretic 'UV' $→$ 'IR'. When the information-theoretic cutoff scale coincides with a standard momentum scale, BRG is equivalent to ERG. We demonstrate the BRG-NNFT correspondence on two analytically tractable examples. First, we construct BRG flows for trained, infinite-width NNs, of arbitrary depth, with generic activation functions. As a special case, we then restrict to architectures with a single infinitely-wide layer, scalar outputs, and generalized cos-net activations. In this case, we show that BRG coarse-graining corresponds exactly to the momentum-shell ERG flow of a free scalar SFT. Our analytic results are corroborated by a numerical experiment in which an ensemble of asymptotically wide NNs are trained and subsequently renormalized using an information-shell BRG scheme.
Jet diffusion versus JetGPT – Modern networks for the LHC

Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer, Tilman Plehn, Peter Sorrenson, Jonas Spinner

SciPost Phys. Core 8, 026 (2025) · published 3 March 2025 | · pdf

We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers.

1 citation
Mixmasters in Wonderland: Chaotic dynamics from Carroll limits of gravity

Gerben Oling, Juan F. Pedraza

SciPost Phys. Core 8, 025 (2025) · published 27 February 2025 | · pdf

We demonstrate that the Carroll limit of general relativity coupled to matter captures the chaotic mixmaster dynamics of near-singularity limits. Zooming in on the behavior of general relativity close to spacelike singularities reveals rich and solvable ultra-local Belinski-Khalatnikov-Lifshitz (BKL) dynamics, which we show to be captured by a Carroll limit. Specifically, building on recent work on geometric Carroll expansions of general relativity, we establish that leading-order Carroll gravity, with suitable matter coupling, accurately describes well-known cosmological billiards behavior. Since the Carroll limit implements the ultra-local limit off shell, this opens up the door to a wide range of possible tractable applications, including spatially inhomogeneous setups and the emergence of spikes at late times. This further suggests that Carroll gravity, along with its subleading corrections, could serve as a valuable tool for studying deep infrared physics in AdS/CFT.
Dirac spin liquid as an “unnecessary” quantum critical point on square lattice antiferromagnets

Yunchao Zhang, Xue-Yang Song, T. Senthil

SciPost Phys. Core 8, 024 (2025) · published 18 February 2025 | · pdf

Quantum spin liquids are exotic phases of quantum matter especially pertinent to many modern condensed matter systems. Dirac spin liquids (DSLs) are a class of gapless quantum spin liquids that do not have a quasi-particle description and are potentially realized in a wide variety of spin $1/2$ magnetic systems on $2d$ lattices. In particular, the DSL in square lattice spin-$1/2$ magnets is described at low energies by $(2+1)d$ quantum electrodynamics with $N_f=4$ flavors of massless Dirac fermions minimally coupled to an emergent $U(1)$ gauge field. The existence of a relevant, symmetry-allowed monopole perturbation renders the DSL on the square lattice intrinsically unstable. We argue that the DSL describes a stable continuous phase transition within the familiar Neel phase (or within the Valence Bond Solid (VBS) phase). In other words, the DSL is an "unnecessary" quantum critical point within a single phase of matter. Our result offers a novel view of the square lattice DSL in that the critical spin liquid can exist within either the Neel or VBS state itself, and does not require leaving these conventional states.
Entropy driven inductive response of topological insulators

A. Mert Bozkurt, Sofie Kölling, Alexander Brinkman, İnanç Adagideli

SciPost Phys. Core 8, 023 (2025) · published 18 February 2025 | · pdf

3D topological insulators are characterized by an insulating bulk and extended surface states exhibiting a helical spin texture. In this work, we investigate the hyperfine interaction between the spin-charge coupled transport of electrons and the nuclear spins in these surface states. Previous work has predicted that in the quantum spin Hall insulator phase, work can be extracted from a bath of polarized nuclear spins as a resource. We employ nonequilibrium Green's function analysis to show that a similar effect exists on the surface of a 3D topological insulator, albeit rescaled by the ratio between electronic mean free path and device length. The induced current due to thermal relaxation of polarized nuclear spins has an inductive nature. We emphasize the inductive response by rewriting the current-voltage relation in harmonic response as a lumped element model containing two parallel resistors and an inductor. In a low-frequency analysis, a universal inductance value emerges that is only dependent on the device's aspect ratio. This scaling offers a means of miniaturizing inductive circuit elements. An efficiency estimate follows from comparing the spin-flip induced current to the Ohmic contribution. The inductive effect is most prominent in topological insulators which have a large number of spinful nuclei per coherent segment, of which the volume is given by the mean free path length, Fermi wavelength and penetration depth of the surface state.
Necklace Ansatz for strongly repulsive spin mixtures on a ring

Gianni Aupetit-Diallo, Giovanni Pecci, Artem Volosniev, Mathias Albert, Anna Minguzzi, Patrizia Vignolo

SciPost Phys. Core 8, 022 (2025) · published 14 February 2025 | · pdf

We propose an alternative to the Bethe Ansatz method for repulsive strongly-interacting fermionic (or bosonic) mixtures on a ring. Starting from the knowledge of the solution for single-component non-interacting fermions (or strongly-interacting bosons), we explicitly impose periodic condition on the amplitudes of the spin configurations. This reduces drastically the number of independent complex amplitudes that we determine by constrained diagonalization of an effective Hamiltonian. This procedure allows us to obtain a complete basis for the exact low-energy many-body solutions for mixtures with a large number of particles, both for $SU(\kappa)$ and symmetry-breaking systems.

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Publications

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