.png){kind=icon}
返回
Preparing quantum states by measurement-feedback control with Bayesian optimization
.png){kind=icon}
OA
AI
DOI：10.1007/s11467-023-1311-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The preparation of quantum states is crucial for enabling quantum computations and simulations. In this work, we present a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) with machine learning techniques. Specifically, we employ Bayesian optimization (BO) to enhance the efficiency of determining the measurement and feedback operators within the MFCP. As an illustration, we study the ground state preparation of the one-dimensional Bose-Hubbard model. Through BO, we are able to identify optimal parameters that can effectively drive the system towards low-energy states with a high probability across various quantum trajectories. Our results open up new directions for further exploration and development of advanced control strategies for quantum computations and simulations.
Keyword:
state preparation
measurement-feedback control
Bayesian optimization
期刊
IF:
5.3
论文数:
1.4K
被引数:
3.7K
机构
引用论文
The effect of grain angle and species on thermal conductivity of some selected wood species.png)
BioResourcesOAAI
BioResourcesIF0
OAAI分享收藏Emergent Schrodinger equation in an introspective machine learning architectureSCIENCE BULLETIN
OAAI
SCIENCE BULLETINIF21.1
OAAI分享收藏Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum ComputerPRX QUANTUMOAAI
PRX QUANTUMIF11
OAAI分享收藏
查看更多内容