High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging. Issue 4 (5th August 2022)

• Record Type: Journal Article
• Authors: Subrahmanian, Manu Veliparambil; Pavuluri, KowsalyaDevi; Olivieri, Cristina; Veglia, Gianluigi
• Editors: Wilson, Ian
• Is Part Of: PNAS nexus. Volume 1:Issue 4(2022)
• Publication Date: 2022-08-05
• Subjects: artificial intelligence -- evolutionary algorithm -- RF pulse design -- NMR -- MRI; Science -- Periodicals; 505
• DOI: 10.1093/pnasnexus/pgac133
• Languages: English
• ISSNs: 2752-6542

Abstract

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 13 CHCl3, achieving high-fidelity transformations under multiple inhomogeneity sources. We then generated band-selective and ultra-broadband RF pulses typical of biomolecular NMR spectroscopy. When implemented in multipulse NMR experiments, the AI-generated pulses significantly increased the sensitivity of medium-size and large protein spectra relative to standard pulse sequences. Finally, we applied the new pulses to typical imaging experiments, showing a remarkable tolerance to changes in the RF field. These AI-generated RF pulses can be directly implemented in quantum information, NMR spectroscopy of biomolecules, magnetic resonance imaging techniques for in vivo and materials sciences.