Bayesian Inference for Heterogeneous Integrated Photonic systems

异构集成光子系统的贝叶斯推理

• 批准号: 2595924
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2595924
• 关键词: Bayesian; Inference; Heterogeneous; Integrated; Photonic

Machine learning (ML) techniques have found employment in the quantum sciences for easier characterisation and evolution of quantum states that are difficult to describe and propagate analytically [1].Bayesian inference is one such technique that is often used as an estimator, making use of a likelihood function to update a prior distribution that represents some belief about what is being estimated in or-der to produce an updated (posterior) probability distribution [2]. This technique has been used tasks like open system dynamics of an electron spin in a diamond NV centre [3], characterisation of nuclear spins [4], and error correction [5].Photonic integrated circuits (PICs) has undergone significant development in the past decade which1has allowed benchtop quantum optics experiments to be scaled down into prototype photonic chips[6], allowing recent advances like the claim of quantum advantage from Xanadu on a programmablephotonic chip which was demonstrated with Gaussian Boson Sampling (GBS) [7]. However, PICs facean issue in that the range of tasks and operating wavelengths that will be demanded of photonic chips,mean that the properties of just one homogeneous material is unlikely to be sufficient, so research intoheterogeneous PICs and hybrid integrated quantum photonics platforms become necessary for furtheradvances in quantum information. More precisely, such platforms should be scalable and offer a highlevel of optical control.

机器学习 (ML) 技术已在量子科学中得到应用，可以更轻松地表征和演化难以分析描述和传播的量子态 [1]。贝叶斯推理就是一种经常用作估计器的技术，它利用似然函数来更新先验分布，该先验分布表示对正在估计的内容的某种信念，以产生更新的（后验）概率分布 [2]。该技术已用于诸如金刚石 NV 中心电子自旋的开放系统动力学 [3]、核自旋表征 [4] 和误差校正 [5] 等任务。光子集成电路 (PIC) 在过去十年中经历了重大发展，这使得台式量子光学实验能够缩小到原型光子芯片 [6]，从而实现了最新的进展，例如声称量子优势 Xanadu 位于可编程光子芯片上，通过高斯玻色子采样 (GBS) 进行了演示 [7]。然而，PIC面临着一个问题，即光子芯片所需的任务范围和工作波长意味着仅一种均质材料的特性不太可能足够，因此对异质PIC和混合集成量子光子平台的研究成为量子信息进一步发展的必要条件。更准确地说，此类平台应该是可扩展的并提供高水平的光学控制。