Bayesian Optimization for Wavefront Sensing and Error Correction

Algorithms for wavefront sensing and error correction from intensity attract great concern in many fields.Here we propose Bayesian optimization to retrieve phase and demonstrate its performance in simulation and experiment.For small aberration,this method demonstrates a convergence process with high accuracy of phase sensing,which is also verified experimentally.For large aberration,Bayesian optimization is shown to be insensitive to the initial phase while maintaining high accuracy.The approach’s merits of high accuracy and robustness make it promising in being applied in optical systems with static aberration such as AMO experiments,optical testing shops,and electron or optical microscopes.

Experimental demonstration of suppressing residual geometric dephasing

The geometric phase is regarded as a promising strategy in fault tolerance quantum information processing(QIP) domain due to its phase only depending on the geometry of the path executed. However, decoherence caused by environmental noise will destroy the geometric phase. Traditional dynamic decoupling sequences can eliminate dynamic dephasing but can not reduce residual geometric dephasing, which is still vital for high-precision quantum manipulation. In this work, we experimentally demonstrate effective suppression of residual geometric dephasing with modified dynamic decoupling schemes,using a single trapped171 Ybtion. The experimental results show that the modified schemes can reduce dephasing rate up to more than one order of magnitude compared with traditional dynamic decoupling schemes, where residual geometric dephasing dominates. Besides, we also investigate the impact of intensity and correlation time of the low-frequency noise on coherence of the quantum system. And we confirm these methods can be used in many cases.

Experimental realization of nonadiabatic holonomic single‐qubit quantum gates with two dark paths in a trapped ion

For circuit-based quantum computation,experimental implementation of a universal set of quantum logic gates with high-fidelity and strong robustness is essential and central.Quantum gates induced by geometric phases,which depend only on global properties of the evolution paths,have built-in noise-resilience features.Here,we propose and experimentally demonstrate nonadiabatic holonomic single-qubit quantum gates on two dark paths in a trapped ^(171)γδ^(+)ion based on four-level systems with resonant drives.We confirm the implementation with measured gate fidelity through both quantum process tomography and randomized benchmarking methods.Meanwhile,we find that nontrivial holonomic two-qubit quantum gates can also be realized within current experimental technologies.Compared with previous implementations,our experiments share both the advantages of fast nonadiabatic evolution and robustness against systematic errors.Therefore,our experiments confirm a promising method for fast and robust holonomic quantum computation.

Experimentally realizing efficient quantum control with reinforcement learning

We experimentally investigate deep reinforcement learning(DRL)as an artificial intelligence approach to control a quantum system.We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity.In particular,the protocol’s robustness against both over-rotations and off-resonance errors can still be achieved simultaneously without any priori input.For the thorough comparison,we choose the task as single-qubit flipping,in which various analytical methods are well-developed as the benchmark,ensuring their feasibility in the quantum system as well.Consequently,a gate operation is demonstrated on a trapped^(171) Yb^(+)ion,significantly outperforming analytical pulses in the gate time and energy cost with hybrid robustness,as well as the fidelity after repetitive operations under time-varying stochastic errors.Our experiments reveal a framework of computer-inspired quantum control,which can be extended to other complicated tasks without loss of generality.