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Thermodynamic sampling of materials using neutral-atom quantum computers

Bruno Camino, Mao Lin, John Buckeridge, Scott M. Woodley
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⚡ Quantum Brief
Researchers developed a quantum framework to simulate material thermodynamics using neutral-atom quantum computers, focusing on nitrogen-doped graphene as a test case. The method bridges classical Density Functional Theory (DFT) with quantum hardware. The team mapped DFT-derived energies onto a Rydberg-atom Hamiltonian for quantum annealing, but hardware limitations required a rescaling strategy. Current QuEra devices lack the energy scale needed for direct implementation. A single-parameter rescaling (αᵥ) aligns Boltzmann weights between hardware and material, enabling accurate sampling at an effective temperature (T′ = αᵥT). This also links laser detuning to chemical potential. Validation on a 28-site graphene nanoflake used exhaustive enumeration, while a 78-site system relied on Monte Carlo sampling. Both confirmed preferential low-energy configuration sampling. The work advances quantum simulations for material science, offering a scalable path to study complex systems beyond classical computational limits.
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Quantum Physics arXiv:2512.21142 (quant-ph) [Submitted on 24 Dec 2025] Title:Thermodynamic sampling of materials using neutral-atom quantum computers Authors:Bruno Camino, Mao Lin, John Buckeridge, Scott M. Woodley View a PDF of the paper titled Thermodynamic sampling of materials using neutral-atom quantum computers, by Bruno Camino and 2 other authors View PDF HTML (experimental) Abstract:Neutral-atom quantum hardware has emerged as a promising platform for programmable many-body physics. In this work, we develop and validate a practical framework for extracting thermodynamic properties of materials using such hardware. As a test case, we consider nitrogen-doped graphene. Starting from Density Functional Theory (DFT) formation energies, we map the material energetics onto a Rydberg-atom Hamiltonian suitable for quantum annealing by fitting an on-site term and distance-dependent pair interactions. The Hamiltonian derived from DFT cannot be implemented directly on current QuEra devices, as the largest energy scale accessible on the hardware is two orders of magnitude smaller than the target two-body interaction in the material. To overcome this limitation, we introduce a rescaling strategy based on a single parameter, $\alpha_v$, which ensures that the Boltzmann weights sampled by the hardware correspond exactly to those of the material at an effective temperature $T' = \alpha_vT$, where $T$ is the device sampling temperature. This rescaling also establishes a direct correspondence between the global laser detuning $\Delta_g$ and the grand-canonical chemical potential $\Delta\mu$. We validate the method on a 28-site graphene nanoflake using exhaustive enumeration, and on a larger 78-site system where Monte Carlo sampling confirms preferential sampling of low-energy configurations. Subjects: Quantum Physics (quant-ph); Materials Science (cond-mat.mtrl-sci); Statistical Mechanics (cond-mat.stat-mech) Cite as: arXiv:2512.21142 [quant-ph] (or arXiv:2512.21142v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.21142 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bruno Camino [view email] [v1] Wed, 24 Dec 2025 12:24:30 UTC (7,946 KB) Full-text links: Access Paper: View a PDF of the paper titled Thermodynamic sampling of materials using neutral-atom quantum computers, by Bruno Camino and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cond-mat cond-mat.mtrl-sci cond-mat.stat-mech 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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energy-climate
neutral-atom
quantum-annealing
quantum-computing
quantum-finance
quantum-hardware
quantum-materials
quera

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Source: arXiv Quantum Physics

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