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Efficient quantum device tuning using machine learning

使用机器学习进行高效量子器件调谐

基本信息

批准号：
1944854
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1944854
关键词：
Efficient quantum device tuning using

项目摘要

Fault-tolerant quantum computers require hundreds to millions of physical qubits to be operated with high fidelity. Inevitable hardware imperfections must be tuned away through iterative interplay of characterization, simulation, and parameter refinement, with each data point informing the decision of what to measure next. The technology is only scalable if this task can be efficiently automated. In the language of computer science, this is a Bayesian optimization problem. Recent progress in machine learning, currently one of the most rapidly developing fields of computing, makes it possible to automate the entire process. This project will apply these new techniques experimentally, working with leaders in machine learning. Fault-tolerant quantum computers require hundreds to millions of physical qubits to be operated with high fidelity. Inevitable hardware imperfections must be tuned away through iterative interplay of characterization, simulation, and parameter refinement, with each data point informing the decision of what to measure next. The technology is only scalable if this task can be efficiently automated. In the language of computer science, this is a Bayesian optimization problem. Recent progress in machine learning, currently one of the most rapidly developing fields of computing, makes it possible to automate the entire process. This project will apply these new techniques experimentally, working with leaders in machine learning.The focus will be electron spin qubits in gate-defined GaAs quantum dots. These are an ideal testbed because the physics is known and the dot potential and tunnel barriers are conveniently optimized by tuning gate voltages. Nonetheless, tuning a simple device by hand takes days to weeks, which is clearly not scalable.The goal of this project is to develop a machine to automatically tune a singlet-triplet qubit in a double quantum dot. The machine will use electrical measurements of the quantum dot to deduce device parameters in the most efficient way, and then adjust gate voltages to optimise them. Inevitably, device imperfections lead to trade-offs in how it is tuned, and we will use simulations of small qubit clusters to identify how to optimise these to make spin qubits useful even in the presence of errors. There has been rapid progress in semiconductor spin qubit devices, but in most cases these were tuned by hand (although using a computer to control data sweeps). There has been at least one pioneering experiment, at TU Delft, using a feature recognition algorithm to tune up a double quantum dot. This project aims to be the first to use the full power of a Bayesian approach to estimate and optimise device parameters.This project makes use of new EPSRC-funded facilities in Oxford, including a facility for hardware-in-the-loop testing of quantum technology, and the NQIT computing facility. The applications of this approach will, we hope, ultimately extend to many areas of experimental science. The project aligns most closely with the EPSRC priority areas of "Quantum devices components and systems" and "Artificial intelligence technologies"EPSRC's research areas: Physical sciencesQuantum technologies

容错量子计算机需要数百万到数百万个物理量子比特才能以高保真度运行。不可避免的硬件缺陷必须通过表征、仿真和参数优化的迭代相互作用来消除，每个数据点都可以决定下一步要测量什么。只有当这项任务可以有效地自动化时，这项技术才是可扩展的。在计算机科学的语言中，这是一个贝叶斯优化问题。机器学习是目前发展最快的计算领域之一，它的最新进展使整个过程自动化成为可能。该项目将实验性地应用这些新技术，与机器学习领域的领导者合作。容错量子计算机需要数百万到数百万个物理量子比特才能以高保真度运行。不可避免的硬件缺陷必须通过表征、仿真和参数优化的迭代相互作用来消除，每个数据点都可以决定下一步要测量什么。只有当这项任务可以有效地自动化时，这项技术才是可扩展的。在计算机科学的语言中，这是一个贝叶斯优化问题。机器学习是目前发展最快的计算领域之一，它的最新进展使整个过程自动化成为可能。本项目将与机器学习领域的领导者合作，在实验上应用这些新技术。重点将是门定义GaAs量子点中的电子自旋量子位。这是一个理想的测试平台，因为物理学是已知的，并且通过调整栅极电压可以方便地优化点电位和隧道势垒。尽管如此，手工调整一个简单的设备需要几天到几周的时间，这显然是不可扩展的。该项目的目标是开发一台机器，自动调整双量子点中的单重态-三重态量子比特。该机器将使用量子点的电气测量以最有效的方式推断设备参数，然后调整栅极电压以优化它们。不可避免的是，设备的不完美性会导致如何调整它的权衡，我们将使用小量子位簇的模拟来确定如何优化这些，使自旋量子位即使在存在错误的情况下也有用。半导体自旋量子位元器件已经取得了快速的进展，但在大多数情况下，这些器件都是手工调谐的（尽管使用计算机来控制数据扫描）。至少有一个开创性的实验，在TU德尔夫特，使用特征识别算法来调整双量子点。该项目旨在成为第一个使用贝叶斯方法的全部力量来估计和优化设备参数的项目。该项目利用了牛津大学新的EPSRC资助的设施，包括量子技术的硬件在环测试设施和NQIT计算设施。我们希望，这种方法的应用最终将扩展到实验科学的许多领域。该项目与EPSRC的“量子设备组件和系统”和“人工智能技术“的优先领域最密切相关。