Publications

Order by: publication date number of citations

• Codebase release 1.5 for PauliStrings.jl

  Nicolas Loizeau, J. Clayton Peacock, Dries Sels

  SciPost Phys. Codebases 54-r1.5 (2025) · published 24 March 2025 | Toggle abstract · src

  We present the Julia package PauliStrings.jl for quantum many-body simulations, which performs fast operations on the Pauli group by encoding Pauli strings in binary. All of the Pauli string algebra is encoded into low-level logic operations on integers, and is made efficient by various truncation methods which allow for systematic extrapolation of the results. We illustrate the effectiveness of our package by (i) performing Heisenberg time evolution through direct numerical integration and (ii) by constructing a Liouvillian Krylov space. We benchmark the results against tensor network methods, and we find our package performs favorably. In addition, we show that this representation allows for easy encoding of any geometry. We present results for chaotic and integrable spin systems in 1D as well as some examples in 2D. Currently, the main limitations are the inefficiency of representing non-trivial pure states (or other low-rank operators), as well as the need to introduce dissipation to probe long-time dynamics.

  4 citations

• Non-Hermitian quantum quenches in holography

  Sergio Morales-Tejera, Karl Landsteiner

  SciPost Phys. 14, 030 (2023) · published 10 March 2023 | Toggle abstract · pdf

  The notion of non-Hermitian $\mathcal{PT}$ symmetric quantum theory has recently been generalized to the gauge/gravity duality. We study the evolution of such non-Hermitian holographic field theories when the couplings are varied with time with particular emphasis on the question non-unitary time vs. unitary time evolution. We show that a non-unitary time evolution in the dual quantum theory corresponds to a violation of the Null Energy Condition (NEC) in the bulk of the asymptotically AdS spacetime. We find that upon varying the non-Hermitian coupling the horizon of a bulk AdS black hole shrinks. On the other hand varying the Hermitian coupling in the presence of a constant non-Hermitian coupling still violates the NEC but results in a growing horizon. We also show that by introducing a non-Hermitian gauge field the time evolution can be made unitary, e.g. the NEC in the bulk is obeyed and an exactly equivalent purely Hermitian description can be given.

  4 citations

• Quantum walks on graphs of the ordered Hamming scheme and spin networks

  Hiroshi Miki, Satoshi Tsujimoto, Luc Vinet

  SciPost Phys. 7, 001 (2019) · published 2 July 2019 | Toggle abstract · pdf

  It is shown that the hopping of a single excitation on certain triangular spin lattices with non-uniform couplings and local magnetic fields can be described as the projections of quantum walks on graphs of the ordered Hamming scheme of depth 2. For some values of the parameters the models exhibit perfect state transfer between two summits of the lattice. Fractional revival is also observed in some instances. The bivariate Krawtchouk polynomials of the Tratnik type that form the eigenvalue matrices of the ordered Hamming scheme of depth 2 give the overlaps between the energy eigenstates and the occupational basis vectors.

  3 citations

• Faraday rotation and transmittance as markers of topological phase transitions in 2D materials

  Manuel Calixto, Alberto Mayorgas, Nicolás A. Cordero, Elvira Romera, Octavio Héctor Castaños Garza

  SciPost Phys. 16, 077 (2024) · published 18 March 2024 | Toggle abstract · pdf

  We analyze the magneto-optical conductivity (and related magnitudes like transmittance and Faraday rotation of the irradiated polarized light) of some elemental two-dimensional Dirac materials of group IV (graphene analogues, buckled honeycomb lattices, like silicene, germanene, stannane, etc.), group V (phosphorene), and zincblende heterostructures (like HgTe/CdTe quantum wells) near the Dirac and gamma points, under out-of-plane magnetic and electric fields, to characterize topological-band insulator phase transitions and their critical points. We provide plots of the Faraday angle and transmittance as a function of the polarized light frequency, for different external electric and magnetic fields, chemical potential, HgTe layer thickness and temperature, to tune the material magneto-optical properties. We have shown that absortance/transmittance acquires extremal values at the critical point, where the Faraday angle changes sign, thus providing fine markers of the topological phase transition. In the case of non-topological materials as phosphorene, a minimum of the transmittance is also observed due to the energy gap closing by an external electric field.

  3 citations

• Predicting quantum potentials by deep neural network and metropolis sampling

  Rui Hong, Peng-Fei Zhou, Bin Xi, Jie Hu, An-Chun Ji, Shi-Ju Ran

  SciPost Phys. Core 4, 022 (2021) · published 13 September 2021 | Toggle abstract · pdf

  The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields. Inspired by quantum potential neural network, we here propose to solve the potential in the Schrodinger equation provided the eigenstate, by combining Metropolis sampling with deep neural network, which we dub as Metropolis potential neural network (MPNN). A loss function is proposed to explicitly involve the energy in the optimization for its accurate evaluation. Benchmarking on the harmonic oscillator and hydrogen atom, MPNN shows excellent accuracy and stability on predicting not just the potential to satisfy the Schrodinger equation, but also the eigen-energy. Our proposal could be potentially applied to the ab-initio simulations, and to inversely solving other partial differential equations in physics and beyond.

  3 citations

• Unconventional superconductivity in a strongly correlated band-insulator without doping

  Anwesha Chattopadhyay, H. R. Krishnamurthy, Arti Garg

  SciPost Phys. Core 4, 009 (2021) · published 28 April 2021 | Toggle abstract · pdf

  We present a novel route for attaining unconventional superconductivity in a strongly correlated system without doping. In a simple model of a correlated band insulator at half-filling we demonstrate, based on a generalization of the projected wavefunctions method, that superconductivity emerges for a broad range of model parameters when e-e interactions and the bare band-gap are both much larger than the kinetic energy, provided the system has sufficient frustration against the magnetic order. As the interactions are tuned, the superconducting phase appears sandwiched between the correlated band insulator followed by a paramagnetic metal on one side, and a ferrimagnetic metal, antiferromagnetic half-metal, and Mott insulator phases on the other side

  3 citations

• Random features and polynomial rules

  Fabián Aguirre-López, Silvio Franz, Mauro Pastore

  SciPost Phys. 18, 039 (2025) · published 31 January 2025 | Toggle abstract · pdf

  Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit. In this work, we present a thorough analysis of the generalization performance of random features models for generic supervised learning problems with Gaussian data. Our approach, built with tools from the statistical mechanics of disordered systems, maps the random features model to an equivalent polynomial model, and allows us to plot average generalization curves as functions of the two main control parameters of the problem: the number of random features $N$ and the size $P$ of the training set, both assumed to scale as powers in the input dimension $D$. Our results extend the case of proportional scaling between $N$, $P$ and $D$. They are in accordance with rigorous bounds known for certain particular learning tasks and are in quantitative agreement with numerical experiments performed over many order of magnitudes of $N$ and $P$. We find good agreement also far from the asymptotic limits where $D\to ∞$ and at least one between $P/D^K$, $N/D^L$ remains finite.

  3 citations

• Particle representation for $NN{\bar K}$ system

  Branislav Vlahovic, Igor Filikhin, Roman Ya. Kezarashvili

  SciPost Phys. Proc. 3, 044 (2020) · published 27 February 2020 | Toggle abstract · pdf

  In the framework of the Faddeev equations in configuration space we perform an analysis of quasi-bound state of the $NN{\bar K}$ system within a particle model. In our approach, the system $NN{\bar K}(s_{NN}=0)$ ($NN{\bar K}(s_{NN}=1))$ is described as a superposition of $ppK^{-}$ and $pn{\bar K}^0$ ($nn{\bar K}^{0}$ and $pn{ K}^-$) states, which is possible due to a particle transition. The relation of the particle model to the theory of a two-state quantum system is addressed and discussed taking into account the possibilities of deep and shallow $NN{\bar K}(s_{NN}=0)$ quasi-bound states.

  3 citations

• Comparative analysis of tight-binding models for transition metal dichalcogenides

  Bert Jorissen, Lucian Covaci, Bart Partoens

  SciPost Phys. Core 7, 004 (2024) · published 6 February 2024 | Toggle abstract · pdf

  We provide a comprehensive analysis of the prominent tight-binding (TB) models for transition metal dichalcogenides (TMDs) available in the literature. We inspect the construction of these TB models, discuss their parameterization used and conduct a thorough comparison of their effectiveness in capturing important electronic properties. Based on these insights, we propose a novel TB model for TMDs designed for enhanced computational efficiency. Utilizing $MoS_2$ as a representative case, we explain why specific models offer a more accurate description. Our primary aim is to assist researchers in choosing the most appropriate TB model for their calculations on TMDs.

  3 citations

• A general learning scheme for classical and quantum Ising machines

  Ludwig Schmid, Enrico Zardini, Davide Pastorello

  SciPost Phys. Core 7, 013 (2024) · published 14 March 2024 | Toggle abstract · pdf

  An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent. We provide a mathematical characterization of the training process, which is based upon optimizing a loss function whose partial derivatives are not explicitly calculated but estimated by the Ising machine itself. Moreover, we present some experimental results on the training and execution of the proposed learning model. These results point out new possibilities offered by Ising machines for different learning tasks. In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning.

  3 citations