标题： 从实验中学习开放量子系统的物理知识 显示英文标题

标题： Learning the physics of open quantum systems from experiments

摘要： 本论文探讨了自适应推断作为一种工具，用于利用实验数据表征量子系统，在传感、校准、控制和计量学中有应用。 我提出了并测试了使用贝叶斯实验设计和先进的蒙特卡洛技术（包括顺序蒙特卡洛和哈密顿蒙特卡洛）来学习哈密顿量和克劳斯算子的算法。 应用于从IBMQ获得的量子设备表征显示了稳健的性能，在相同测量次数下超越了Qiskit的内置表征功能。 提供了贝叶斯统计、实验设计和数值积分的介绍，以及现有文献的概述。 显示英文摘要

摘要： This thesis explores adaptive inference as a tool to characterize quantum systems using experimental data, with applications in sensing, calibration, control, and metrology. I propose and test algorithms for learning Hamiltonian and Kraus operators using Bayesian experimental design and advanced Monte Carlo techniques, including Sequential and Hamiltonian Monte Carlo. Application to the characterization of quantum devices from IBMQ shows a robust performance, surpassing the built-in characterization functions of Qiskit for the same number of measurements. Introductions to Bayesian statistics, experimental design, and numerical integration are provided, as well as an overview of existing literature.