.png){kind=icon}
返回
Gradient-Ascent Pulse Engineering with Feedback
.png){kind=icon}
OA
AI
DOI：10.1103/PRXQuantum.4.030305.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully solved using optimization techniques, including methods such as gradient-ascent pulse engineering (GRAPE) , relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. In this work, we introduce feedback GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power considering various scenarios based on cavity-QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multiqubit devices to linear-optics quantum computation strategies, quantum enhanced sensing with adaptive measurements, and quantum error correction.
Keyword:
QUANTUM
PHOTON
QUBIT
DESIGN
期刊
P
IF:
11
论文数:
919
被引数:
9.0K
机构
引用论文
分享收藏Optimal control of circuit quantum electrodynamics in one and two dimensions.png)
PHYSICAL REVIEW B
OAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic DrivingPHYSICAL REVIEW XOAAI
PHYSICAL REVIEW XIF15.7
OAAI分享收藏Implementing a universal gate set on a logical qubit encoded in an oscillator在振荡器中编码的逻辑量子位上实现通用门集NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏
查看更多内容