Bayesian Optimal Control of Greenberger-Horne-Zeilinger States in Rydberg Lattices

Optimizing Quantum Control Pulses with Gaussian Process Priors: The Spectral Way

This study presents the Gaussian Process Prior Optimization for Pulse Shaping (GPPOPS) methodology, a novel approach to pulse shaping engineering. Its main objective is to efficiently identify laser pulse shapes that can achieve a desired task encoded in a cost function while being experimentally implementable. The AlH⁺ molecule was utilized as a test case to find pulse shapes that maximized vibronic transitions. The results demonstrate that optimal pulses can be readily implemented using current laser technology and that their control capabilities can withstand noise. The study emphasizes the benefits of constructing a surrogate approach to the control landscape during the optimization process. This approach is expected to be versatile, efficient and readily implementable in the laboratory. Its demonstrated robustness to noise sets it apart from other numerical pulse shaping engineering methods. By reducing the required experimental labor, this method has the potential to facilitate breakthroughs in quantum engineering.

Deep learning enhanced Rydberg multifrequency microwave recognition

Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.

Bayesian optimal control of the ultrashort circularly polarized attosecond pulse generation by two-color polarization gating

We present ab initio simulations of optimal control of high-order-harmonic generation spectra that enable the synthesis of a circularly polarized 53-attosecond pulse in a single Helium atom response. The Bayesian optimization is used to achieve control of a two-color polarization gating laser waveform such that a series of harmonics in the plateau region are phase-matched, which can be used for attosecond pulse synthesis. To find the underlying mechanisms for generating these harmonics, we perform a wavelet analysis for the induced dipole moment in acceleration form, and compare the time-energy representation with the quantum paths extracted from the semiclassical calculation. We found that these coherent harmonics are excited along the short trajectories. The proposed method has the potential to migrate to laboratories for generation of isolated circularly polarized ultrashort attosecond pulses.