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Efficient time-evolution of matrix product states using average Hamiltonians

arXiv Quantum Physics
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⚡ Quantum Brief
Researchers led by Belal Abouraya and Fedor Jelezko introduced a method to improve matrix product state (MPS) simulations of quantum many-body systems, achieving second-order convergence—double the accuracy of standard first-order techniques. The approach uses average Hamiltonians to enhance time-evolution simulations of time-dependent quantum systems, significantly reducing computational errors while maintaining efficiency in one-dimensional models. Testing on nitrogen-vacancy (NV) center spin chains in diamonds showed a 1,000-fold error reduction for moderate step sizes, demonstrating potential for scalable quantum technologies. This advancement addresses the long-standing challenge of simulating exponentially complex quantum systems, leveraging tensor networks to capture entanglement properties more effectively. The work opens pathways for more accurate and practical simulations of dynamic quantum systems, critical for condensed matter physics and quantum information science.
Efficient time-evolution of matrix product states using average Hamiltonians

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Quantum Physics arXiv:2602.04955 (quant-ph) [Submitted on 4 Feb 2026] Title:Efficient time-evolution of matrix product states using average Hamiltonians Authors:Belal Abouraya, Jirawat Saiphet, Fedor Jelezko, Ressa S. Said View a PDF of the paper titled Efficient time-evolution of matrix product states using average Hamiltonians, by Belal Abouraya and 3 other authors View PDF HTML (experimental) Abstract:Simulating quantum many-body systems (QMBS) is one of the long-standing, highly non-trivial challenges in condensed matter physics and quantum information due to the exponentially growing size of the system's Hilbert space. To date, tensor networks have been an essential tool for studying such quantum systems, owing to their ability to efficiently capture the entanglement properties of the systems they represent. One of the well-known tensor network architectures, namely matrix product states (MPS), is the standard method for simulating one-dimensional QMBS. Here, we propose a simple, yet efficient, method to augment the already available MPS algorithms to simulate the dynamics of time-dependent Hamiltonians with better accuracy and a faster convergence rate, giving a second-order convergence compared to the first-order convergence of the standard method. We apply our proposed method to simulate the dynamics of a chain of single spins associated with nitrogen-vacancy color centers in diamonds, which has potential applications for practical and scalable quantum technologies, and find that our method improves the average error for a system of few NV centers by a factor of about 1000 for moderate step sizes. Our work paves the way for efficient simulation of QMBS under the influence of time-dependent Hamiltonians. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.04955 [quant-ph] (or arXiv:2602.04955v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.04955 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ressa Said [view email] [v1] Wed, 4 Feb 2026 19:00:01 UTC (272 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient time-evolution of matrix product states using average Hamiltonians, by Belal Abouraya and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-02 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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Source: arXiv Quantum Physics