ArticlePNAS nexus2022
High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging.
Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Seedless: on-the-fly pulse calculation for NMR experiments.Nature communications · 2025Article
- Detection ofThe Analyst · 2025Article
- Exchange-selective excitation pulses for dynamic magnetic resonance.Science advances · 2025Article
- AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields.Nature communications · 2023Article
- Water irradiation devoid pulses enhance the sensitivity ofJournal of biomolecular NMR · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
High-fidelity control of spin ensemble dynamics is essential for many research areas, spanning from quantum computing and radio-frequency (RF) engineering to NMR spectroscopy and imaging. However, attaining robust and high-fidelity spin operations remains an unmet challenge. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. Compared with the standard RF shapes, the new AI-generated pulses show superior performance for bandwidth, robustness, and tolerance to field imperfections. As a benchmark, we constructed a spin entanglement operator for the weakly coupled two-spin-1/2 system of
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Registered trials
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