High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR...

Area of Science:

• Physics
• Chemistry
• Engineering

Background:

• High-fidelity control of spin ensemble dynamics is crucial for quantum computing, radio-frequency (RF) engineering, NMR spectroscopy, and imaging.
• Achieving robust and high-fidelity spin operations remains a significant challenge in these fields.

Purpose of the Study:

• To design novel RF pulses using artificial intelligence (AI) and evolutionary algorithms for enhanced spin ensemble control.
• To improve robustness and tolerance to field imperfections in RF pulse design.

Main Methods:

• An evolutionary algorithm and AI were employed to design new RF pulses with customizable spatial or temporal field inhomogeneity compensation.
• The performance of AI-generated pulses was benchmarked against standard RF shapes using a spin entanglement operator for a ¹³CHCl₃ system.
• AI-generated pulses were applied to biomolecular NMR spectroscopy and magnetic resonance imaging experiments.

Main Results:

• AI-generated RF pulses demonstrated superior performance in bandwidth, robustness, and tolerance to field imperfections compared to standard RF shapes.
• High-fidelity spin transformations were achieved under multiple inhomogeneity sources using the AI-designed pulses.
• Implementation in multipulse NMR experiments significantly increased sensitivity for protein spectra, and imaging experiments showed remarkable tolerance to RF field variations.

Conclusions:

• The developed AI-generated RF pulses offer a significant advancement for high-fidelity spin control.
• These pulses have broad applicability in quantum information processing, biomolecular NMR spectroscopy, and magnetic resonance imaging for in vivo and materials science applications.