Computer Physics Communications最新文献

{"title":"SQUIRREL: An open-source software suite for quantum dynamics calculations on complex geometries with time-dependent electric/magnetic fields","authors":"Simon N. Sandhofer , Mahmut S. Okyay , Bryan M. Wong","doi":"10.1016/j.cpc.2025.109861","DOIUrl":"10.1016/j.cpc.2025.109861","url":null,"abstract":"<div><div>We present a general-purpose, open-source software suite, SQUIRREL (Streamlined Quantum Unified Interface for Researching Real-time Excitations with Light), for propagating the time-dependent Schrödinger equation on complex geometries in the presence of time-dependent electric and/or magnetic fields. To handle large systems that can be executed on a conventional desktop computer, the SQUIRREL software suite uses a suite of efficient propagation methods for various quantum dynamics applications, including a new perturbation-based element-dropping algorithm that improves computational performance with minimal loss of accuracy. We analyze the efficacy of these optimizations for Crank-Nicolson, scaled Taylor series approximation, and split-operator propagation methods and discuss the range of their applicability to a variety of quantum dynamics problems. In addition, we provide several examples of time-dependent dynamics calculations and extensive documentation for generating custom geometries, potentials, and time-propagation approaches. Our numerical benchmarks and results demonstrate the versatility of the SQUIRREL software suite for efficiently calculating quantum dynamics in complex nanoscale geometries, particularly in the presence of time-dependent magnetic fields, which have received less attention in previous quantum dynamics studies.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> SQUIRREL</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/kfvs5s88sj.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License 3</div><div><em>Programming language:</em> MATLAB</div><div><em>Supplementary material:</em> Additional details on calculations with an anisotropic effective mass, determination of timesteps, propagation timings, and element-dropping timings.</div><div><em>Nature of problem:</em> The SQUIRREL software suite solves the time-dependent Schrödinger equation for electronic states in the presence of time-dependent electric and/or magnetic fields. The code is well-suited for calculating electron dynamics of complex nanostructures in the effective mass approximation, using various propagation and sparse-matrix techniques to reduce the computational complexity of these problems. The program provides a user-friendly interface for testing these optimizations and generating custom geometries and potentials for systems of interest.</div><div><em>Solution method:</em> The SQUIRREL software suite uses Crank-Nicolson, split-operator, Padé approximant, and scaled-Taylor-series-based propagation methods to calculate electron dynamics on a finite-element basis. The code includes a novel perturbation-based element-dropping method, which enables sparse representations of the typically dense finite element Hamiltonian matrix. This capability enables a highly efficient and flexible approach for calculating driven dynamics in quantum systems ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109861"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GTS: A Python toolkit for building Gibbs thermodynamic surface with application to obtain high-pressure melting data","authors":"Xuan Zhao,&nbsp;Kun Yin","doi":"10.1016/j.cpc.2025.109858","DOIUrl":"10.1016/j.cpc.2025.109858","url":null,"abstract":"<div><div>Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: <span><span>https://github.com/computation-mineral-physics-group/GTS</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GTS</div><div><em>CPC Library link to program files:</em> <span><span><span>https://doi.org/10.17632/wkkkv6twgk.1</span></span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span><span>https://github.com/computation-mineral-physics-group/GTS</span></span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License, version 3</div><div><em>Programming language:</em> Python 3</div><div><em>Nature of problem:</em> A Python toolkit that efficiently obtains high-pressure melting data, including melting points and thermodynamic potentials of materials, by constructing the Gibbs thermodynamic surface using a geometrical method.</div><div><em>Solution method:</em> With the <em>ab initio</em> molecular dynamics (AIMD) simulation data in the NVT (N, number of atoms; V, volume; T, temperature) ensemble and the reference point, GTS consists of two steps: first building the surface, second producing the melting data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109858"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials","authors":"Chengbing Chen ,&nbsp;Yutong Li ,&nbsp;Rui Zhao ,&nbsp;Zhoulin Liu ,&nbsp;Zheyong Fan ,&nbsp;Gang Tang ,&nbsp;Zhiyong Wang","doi":"10.1016/j.cpc.2025.109859","DOIUrl":"10.1016/j.cpc.2025.109859","url":null,"abstract":"&lt;div&gt;&lt;div&gt;As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and NepTrainKit, which are dedicated to initializing and managing training datasets to generate high-quality training sets while automating NEP model training. NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physical structures from molecular dynamics trajectories, thereby ensuring high-quality training datasets. NepTrainKit is a graphical user interface (GUI) software designed specifically for NEP training datasets, providing functionalities for data editing, visualization, and interactive exploration. It integrates key features such as outlier identification, farthest-point sampling, non-physical structure detection, and configuration type selection. The combination of these tools enables users to process datasets more efficiently and conveniently. Using &lt;span&gt;&lt;math&gt;&lt;mi&gt;CsPb&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; as a case study, we demonstrate the complete workflow for training NEP models with NepTrain and further validate the models through materials property predictions. We believe this toolkit will greatly benefit researchers working with machine learning interatomic potentials.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; NepTrain and NepTrainKit&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/4s97yg7j9t.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/aboys-cb/NepTrain&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt; and &lt;span&gt;&lt;span&gt;https://github.com/aboys-cb/NepTrainKit&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; The NEP method, a novel machine learning potential model, has demonstrated broad application prospects in materials science due to its excellent computational efficiency. However, the development of accurate NEP models heavily depends on the construction of high-quality training datasets. The preparation and iterative refinement of these datasets largely rely on the researcher's expertise, which poses a significant barrier for beginners attempting to use NEP and similar machine learning potential methods.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physic","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109859"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiracBilinears.jl: A package for computing Dirac bilinears in solids","authors":"Tatsuya Miki ,&nbsp;Hsiao-Yi Chen ,&nbsp;Takashi Koretsune ,&nbsp;Yusuke Nomura","doi":"10.1016/j.cpc.2025.109857","DOIUrl":"10.1016/j.cpc.2025.109857","url":null,"abstract":"<div><div>DiracBilinears.jl is a Julia package for computing Dirac bilinears, which are fundamental physical quantities of electrons in relativistic quantum theory, using first-principles calculations for solids. In relativistic quantum theory, 16 independent bilinears can be defined using the four-component Dirac field. To focus on the low-energy physics typically considered in condensed matter physics, we consider the bilinears represented by the non-relativistic two-component Schrödinger field, obtained from the <span><math><mn>1</mn><mo>/</mo><mi>m</mi></math></span> expansion to leading order. This package can evaluate the spatial distributions and Wannier matrix elements of the Dirac bilinears in solids quantitatively by connecting to the external first-principles calculation packages, including Quantum ESPRESSO, Wannier90, and wan2respack.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> DiracBilinears.jl</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/j57y5cjkmc.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/TatsuyaMiki/DiracBilinears.jl.git</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License 3.0</div><div><em>Programming language:</em> Julia</div><div><em>External software:</em> <span>Quantum ESPRESSO</span>, <span>Wannier90</span>, <span>wan2respack</span></div><div><em>Nature of problem:</em> In relativistic quantum theory, Dirac bilinears are the fundamental physical quantities derived from the Dirac field. This package is a tool for the evaluation of the bilinears in solids quantitatively.</div><div><em>Solution method:</em> This package evaluates the non-relativistic expression of Dirac bilinears, focusing on the low-energy regime typically discussed in condensed matter physics. It uses results from first-principles calculations performed with <span>Quantum ESPRESSO</span>, <span>Wannier90</span>, and <span>wan2respack</span>. By using the Bloch wave functions and the Wannier functions obtained from these packages, this package computes spatial distributions and Wannier matrix elements of the bilinears.</div><div><em>Additional comments including restrictions and unusual features:</em> This package requires <span>Quantum ESPRESSO</span> calculations using norm-conserving pseudopotentials and supports <span>wan2respack</span> in “spinor” branch on GitHub.<span><span>¹</span></span></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109857"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PARSE: Physical attribute representativity and stationarity evaluator open-source library for 3D images using scalar and vector metrics","authors":"Andrey S. Zubov ,&nbsp;Yan A. Malyavko ,&nbsp;Marina V. Karsanina ,&nbsp;Nikolay D. Kondratyuk ,&nbsp;Kirill M. Gerke","doi":"10.1016/j.cpc.2025.109860","DOIUrl":"10.1016/j.cpc.2025.109860","url":null,"abstract":"<div><div>The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present <figure><img></figure> library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of <figure><img></figure> library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109860"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ants3 toolkit: Front-end for Geant4 with interactive GUI and Python scripting","authors":"A. Morozov,&nbsp;L.M.S. Margato,&nbsp;G. Canezin,&nbsp;J. Gonzalez","doi":"10.1016/j.cpc.2025.109869","DOIUrl":"10.1016/j.cpc.2025.109869","url":null,"abstract":"<div><div>Ants3 is a toolkit that serves as a front-end for particle simulations in Geant4 and offers a custom simulator for optical photons. It features a fully interactive Graphical User Interface and an extensive scripting system based on general-purpose scripting languages (Python and JavaScript). Ants3 covers the entire detector simulation/optimization cycle, providing an intuitive approach for configuration of the geometry and simulation conditions, the possibility to automatically distribute workload over local and network resources, and giving a suite of versatile tools based on CERN ROOT for the analysis of the results. The intended application area is the development of new detectors and readout methods. The toolkit has been designed to be user-friendly for those with little experience in simulations and programming.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109869"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tropical sampling from Feynman measures","authors":"Michael Borinsky ,&nbsp;Mathijs Fraaije","doi":"10.1016/j.cpc.2025.109846","DOIUrl":"10.1016/j.cpc.2025.109846","url":null,"abstract":"<div><div>We introduce an algorithm that samples a set of loop momenta distributed as a given Feynman integrand. The algorithm uses the tropical sampling method and can be applied to evaluate phase-space-type integrals efficiently. We provide an implementation, <span>momtrop</span>, and apply it to a series of relevant integrals from the loop-tree duality framework. Compared to naive sampling methods, we observe convergence speedups by factors of more than 10⁶.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>momtrop</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/v9mxr9dw2z.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/alphal00p/momtrop</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> <span>Rust</span></div><div><em>Nature of problem:</em> Efficient numerical evaluation of Feynman-type integrals (e.g. phase space or loop-tree duality integrals).</div><div><em>Solution method:</em> Efficient sampling of loop momenta distributed as the Feynman measure (i.e. the integrand of a scalar Euclidean Feynman integral) using tropical sampling [1]. The input to the library is the graph associated to the Feynman measure. From the graph a sampler is produced that takes as input a set of uniformly distributed random numbers and returns a (weighted) set of loop momenta.</div><div><em>Additional comments including restrictions and unusual features:</em> Memory usage is exponential in the number of propagators of the Feynman integral. There can be numerical instabilities if the parameters are close to a divergent configuration.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>M. Borinsky, Tropical Monte Carlo quadrature for Feynman integrals, Ann. Inst. H. Poincare D Comb. Phys. Interact. 10 (4) (2023) 635–685. <span><span>arXiv:2008.12310</span><svg><path></path></svg></span>, <span><span>https://doi.org/10.4171/aihpd/158</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109846"},"PeriodicalIF":3.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large scale stress-aware epitaxial growth simulations using a hybrid Molecular Dynamics-Monte Carlo method","authors":"Anthony Payet ,&nbsp;SangJun Yun ,&nbsp;JeongHwan Han ,&nbsp;Alexander Schmidt ,&nbsp;Inkook Jang ,&nbsp;Dae Sin Kim","doi":"10.1016/j.cpc.2025.109849","DOIUrl":"10.1016/j.cpc.2025.109849","url":null,"abstract":"<div><div>A large scale hybrid method combining Molecular Dynamics with Monte Carlo is implemented for the simulation of Silicon Germanium heteroepitaxy and the prediction of dislocation apparition. On one hand, using the Tersoff potential, the Molecular Dynamics part allows for realistic structure relaxation as well as the creation of a highly discretized potential energy surface. On the other hand, the Monte Carlo part allows for a fast deposition simulation. The combination is furthermore improved to apply the method in two different large scale domains. First, with structures holding millions of atoms and second in a supercomputer environment with thousands of processing cores. The method results show a very close agreement regarding the critical thickness of heteroepitaxied structures grown at their stable states.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109849"},"PeriodicalIF":3.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linearized quantum lattice-Boltzmann method for the advection-diffusion equation using dynamic circuits","authors":"David Wawrzyniak ,&nbsp;Josef Winter ,&nbsp;Steffen Schmidt ,&nbsp;Thomas Indinger ,&nbsp;Christian F. Janßen ,&nbsp;Uwe Schramm ,&nbsp;Nikolaus A. Adams","doi":"10.1016/j.cpc.2025.109856","DOIUrl":"10.1016/j.cpc.2025.109856","url":null,"abstract":"<div><div>We propose a quantum algorithm for the linear advection-diffusion equation (ADE) Lattice-Boltzmann method (LBM) that leverages dynamic circuits. Dynamic quantum circuits allow for an optimized quantum collision operator algorithm by incorporating partial measurements as an integral step. The quantum circuit efficiently adapts during execution based on digital information obtained via mid-circuit measurements.</div><div>The proposed new collision algorithm is implemented as a fully unitary operator, which facilitates the computation of multiple time steps without state reinitialization. Unlike previous quantum collision operators that rely on linear combinations of unitaries, the proposed algorithm does not exhibit a probabilistic failure rate. Our proposed algorithm embeds no more than two distribution functions simultaneously within the quantum state, irrespective of the velocity set. Compared to previous quantum algorithms, this approach reduces both the qubit overhead and circuit complexity required to execute the collision operator and encode the distributions.</div><div>The quantum collision algorithm is validated against classical LBM simulations in 1D and 2D, showing excellent agreement. Performance analysis over multiple time steps highlights advantages of the proposed method compared to previous methods.</div><div>As an additional variant, a hybrid quantum-digital approach is proposed, which reduces the number of mid-circuit measurements, thus improving the efficiency of the quantum collision algorithm.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109856"},"PeriodicalIF":3.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SWtools: A Python package implementing iterative solvers for soliton solutions of nonlinear Schrödinger-type equations","authors":"O. Melchert ,&nbsp;A. Demircan","doi":"10.1016/j.cpc.2025.109851","DOIUrl":"10.1016/j.cpc.2025.109851","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Solitons are ubiquitous in nature and play a pivotal role in the structure and dynamics of solutions of nonlinear propagation equations. In many instances where solitons are expected to exist, analytical expressions of these special objects are not available. The presented software fills this gap, allowing users to numerically calculate soliton solutions for a generic nonlinear Schrödinger-type equation by iteratively solving an associated nonlinear eigenvalue problem. The package implements a range of methods, including the spectral renormalization method, and a relaxation method for the problem with additional normalization constraint. We verify the implemented methods in terms of a problem for which an analytical soliton expression is available, and demonstrate the implemented functionality by numerical experiments for example problems in nonlinear optics and matter-wave solitons in quantum mechanics. For common variants of the considered equation, &lt;span&gt;SWtools&lt;/span&gt; also implements functions retrieving linear stability eigenvalues and modes for its solitons. The presented Python package is open-source and released under the MIT License in a publicly available software repository.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; Solitary wave tools (&lt;span&gt;SWtools&lt;/span&gt;)&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/y55t9chcz6.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/omelchert/SWtools&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; MIT&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; Online documentation and usage examples are hosted on GitHub under &lt;span&gt;&lt;span&gt;https://github.com/omelchert/SWtools&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;. A Code Ocean compute capsule demonstrating the calculation of a soliton solution for a higher-order nonlinear Schrödinger equation is available under &lt;span&gt;&lt;span&gt;https://doi.org/10.24433/CO.5557616.v1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Numerical computation of solitary wave solutions for nonlinear Schrödinger-type equations. Two variants of the corresponding nonlinear eigenvalue problem (NEVP) are considered: a “bare” NEVP, where a solution with prescribed eigenvalue is computed, and a “constrained” NEVP with &lt;em&gt;a priori&lt;/em&gt; unknown eigenvalue, where a solution with prescribed norm is computed.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; &lt;span&gt;SWtools&lt;/span&gt; implements iterative solvers for both problem variants. While the bare NEVP is solved using the spectral renormalization method (SRM) [1], the constrained NEVP is solved using a custom nonlinear successive overrelaxation method (NSOM).&lt;/div&gt;&lt;div&gt;&lt;em&gt;Additional comments including restrictions and unusual features:&lt;/em&gt; This document serves as a reference for &lt;span&gt;SWtools&lt;/span&gt;. For a concise presen","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109851"},"PeriodicalIF":3.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}