Identification of Light Sources using Machine Learning

量子机器学习中的偏差识别与缓解

Identification and Mitigating Bias in Quantum Machine Learning

本文围绕量子机器学习中的偏差问题，系统探讨其表现形式、产生原因及识别、诊断与缓解策略，为该领域偏差治理提供研究框架。

Interpretable machine-learning identification of the crossover from subradiance to superradiance in an atomic array

Light-matter interacting quantum systems manifest strong correlations that lead to distinct cooperative spontaneous emissions of subradiance or superradiance. To demonstrate the essence of long-range correlations in such systems, we consider an atomic array under the resonant dipole-dipole interactions (RDDI) and apply an interpretable machine learning with the integrated gradients to identify the crossover between the subradiant and superradiant sectors. The machine shows that the next nearest-neighbor couplings in RDDI play as much as the roles of nearest-neighbor ones in determining the who…

Quantum Machine Learning for $b$-jet charge identification

Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computation could be used to exploit particles correlations for improving the jet classification performance. In this paper, we present a brand new approach to identify if a jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of p…

Machine learning identification of symmetrized base states of Rydberg atoms

Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and…

Towards Building A Facial Identification System Using Quantum Machine Learning Techniques

In the modern world, facial identification is an extremely important task in which many applications rely on high performing algorithms to detect faces efficiently. Whilst classical methods of SVM and k-NN commonly used may perform to a good standard, they are often highly complex and take substantial computing power to run effectively. With the rise of quantum computing boasting large speedups without sacrificing large amounts of much needed performance, we aim to explore the benefits that quantum machine learning techniques can bring when specifically targeted towards facial identification a…

基于机器学习的量子开放系统谱密度识别方法

A machine learning based approach to the identification of spectral densities in quantum open systems

本文提出基于机器学习的方法，用于识别量子开放系统中的谱密度，通过神经网络实现分类与参数估计，展示了其在环境特征探测中的应用潜力。