![]() These routines search the RF phase space and generate an Optimal Phase Surface (OPS) library through an iterative evolution of the solutions. The core module of the algorithm consists of a network of interconnected optimization routines, including an iterative forward search (IFSA) and iterative self-correcting algorithms (ISCA) ( Figure S2). The overall architecture of GENETICS-AI is reported in Figure S1. To design high-fidelity RF pulses, we combined an evolutionary algorithm with AI into a modular software, GENErator of TrIply Compensated RF pulSes via Artificial Intelligence (GENETICS-AI). ![]() The architecture of the GENETICS-AI algorithm Our versatile approach enables the design of several spin operations for various applications, including quantum computing, biomolecular NMR spectroscopy, and MRI techniques. Searching the phase space enabled the algorithm to achieve optimal solutions and reach an operational fidelity of 0.9999. We then trained an artificial intelligence (AI) algorithm with this library to generate the optimal solution for a given problem. Instead of tuning RF amplitude and duration for a given pulse shape, we search the phase space and let an evolutionary algorithm generate a library of ∼2,00,000 phase shapes with constant amplitude. Here, we introduce a novel strategy to achieve high-fidelity control of spin ensemble dynamics with a high-level compensation for inhomogeneity and offset effects, reaching a fidelity for several spin operations up to 0.99999. Although advanced computational techniques have been instrumental for designing compensated RF pulses such as composite, adiabatic, and numerically optimized pulses ( 6, 12–18, 19, 20–29), high- and ultra-high–field NMR and MRI spectroscopy require RF pulses with larger bandwidths, higher fidelity, and compensation for instrumental inhomogeneities. Moreover, these imperfections accumulate in multipulse experiments, leading to low-fidelity operations and sizable signal losses ( 11). These experimental errors also affect NMR and MRI at high and ultra-high magnetic fields as they require high-fidelity levels for coherent and high-efficiency control of heterogeneous spin ensembles ( 9, 10). In the experimental implementation of quantum computing, inhomogeneities affect the experimental fidelity of quantum gates, on-demand entangled state generation, and coherent control ( 1, 7, 8). However, the RF and external field inhomogeneities and finite pulse length effects make the coherent manipulation of spin ensemble dynamics challenging ( 6). Spin operations such as excitation, inversion, refocusing, etc., are central to these techniques and are achieved by applying radio-frequency (RF) pulses of finite length and amplitude. High-fidelity control of quantum spin systems is at the foundation of many applications such as quantum computing, coherent and optical spectroscopies, NMR, and MRI ( 1–5). ![]() 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. Finally, we applied the new pulses to typical imaging experiments, showing a remarkable tolerance to changes in the RF field. 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. We then generated band-selective and ultra-broadband RF pulses typical of biomolecular NMR spectroscopy. As a benchmark, we constructed a spin entanglement operator for the weakly coupled two-spin-1/2 system of 13CHCl 3, achieving high-fidelity transformations under multiple inhomogeneity sources. Compared with the standard RF shapes, the new AI-generated pulses show superior performance for bandwidth, robustness, and tolerance to field imperfections. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. However, attaining robust and high-fidelity spin operations remains an unmet challenge. 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.
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