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A First-principles Computational Framework for Quantum Decoherence in Complex Diamond Spin Environments
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A First-principles Computational Framework for Quantum Decoherence in Complex Diamond Spin Environments

--> Quantum Physics arXiv:2608.02846 (quant-ph) [Submitted on 3 Aug 2026] Title:A First-principles Computational Framework for Quantum Decoherence in Complex Diamond Spin Environments Authors:Huijin Park, Ha-young Jeong, Hyeonsu Kim, Christoph Findler, Fedor Jelezko, Sangwon Oh, Junghyun Lee, Giulia Galli, Hosung Seo View a PDF of the paper titled A First-principles Computational Framework for Quantum Decoherence in Complex Diamond Spin Environments, by Huijin Park and 8 other authors View PDF Abstract:Quantum decoherence induced by defects remains a major limitation for solid-state quantum technologies, yet predicting decoherence in realistic materials remains computationally challenging. Complex defect populations are often approximated as homogeneous spin baths, obscuring the role of defect-specific electronic structure and spin dynamics. Here, we develop a predictive framework for decoherence in diamond by combining first-principles electronic-structure calculations, quantum many-body spin-bath simulations, and experimental validation. The framework incorporates defect-resolved spin Hamiltonians and heterogeneous spin baths containing multiple paramagnetic defect species. Using diamond nitrogen-vacancy ensembles as a model platform, we investigate mixed nitrogen-, vacancy-, and hydrogen-related defect environments. We show that decoherence depends not only on defect density but also on defect identity and bath composition, whose distinct electronic structures, hyperfine interactions, and spin dynamics produce different coherence behavior. Heterogeneous defect populations can either suppress or enhance decoherence, producing trends unexplained by homogeneous-bath models. Magnetic-field-dependent Hahn-echo measurements on samples with different defect concentrations validate the framework. The calculations reproduce the observed coherence times and stretched-exponential decay behavior across a broad magnetic-field range and identify vacancy-related defects as crit

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Quantum X Labs simulates particle transport with quantum computingquantum-computing

Quantum X Labs simulates particle transport with quantum computing

Nasdaq-listed Quantum X Labs (Nasdaq: QXL) is applying quantum computing to address longstanding computational bottlenecks in nuclear-medicine system design. Through its subsidiary, Nuclear Quantum, the company has developed technology that represents particle propagation directly within quantum computation, moving beyond traditional simulation methods by encoding and exploring gamma-photon histories. This project establishes the core quantum oracle required for future integration with Grover-inspired quantum algorithms. Nuclear Quantum intends to expand the model to more complex scenarios and evaluate its potential for faster, more precise simulations within the nuclear-medicine industry. Quantum Circuit Encoding of Nuclear Particle Transport Quantum X Labs is tackling a critical challenge in nuclear medicine: the immense computational demands of simulating particle transport, a process essential for designing effective systems. Conventional methods struggle with the complexity of modeling particle behavior, creating a bottleneck in development timelines and optimization efforts. This approach moves beyond traditional simulation techniques, potentially unlocking significant speed and precision gains. The core of this advancement lies in the ability to represent gamma-photon histories, the paths these particles take, as quantum circuits. Nuclear Quantum’s work is not simply about applying quantum computing to an existing problem; it’s about fundamentally changing how the problem is solved. The company stated that the technology aims to translate the physical transport model into a quantum circuit, highlighting the ambition to map physical processes onto the language of quantum mechanics. This encoding allows researchers to explore particle behavior in a way previously inaccessible, potentially revealing insights that improve system design. This is a crucial stepping stone toward accelerating these simulations, offering a defined pathway for future optimization. Grov

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