Scalable and Automated Tuning of Spin-based Quantum Computer Architectures

基于自旋的量子计算机架构的可扩展和自动调整

• 批准号: 2887634
• 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: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887634
• 关键词: Scalable; Automated; Tuning; Spin; based

Scalable and Automated Tuning of Spin-based Quantum Computer ArchitecturesBrief description of the context of the research including potential impact: - Quantum computing is an emergent technology that has the potential to solve classically intractable problems using a new paradigm of computation. The basis for such computation uses the quantum states of a system to encode information, unlike the binary states of modern-day transistors (bits). Of the many candidate platforms for realising these quantum bits (qubits), spins confined in semiconductor structures are attractive for their long coherence times as well as small size and ease of integration with control electronics, giving them an edge in terms of scalability. The challenge lies in moving away from proof-of-concept devices (containing just a handful of qubits) to microchips that contain dozens of such qubits and beyond. This is currently hindered by device variability and noise associated with the host material, which, in the first case, makes device tuning very difficult, and in the second case, can lead to a reduction in the fidelity of quantum operations, especially if these noise sources have spatio-temporal correlations. If clever algorithms and protocols can be devised to, firstly, tune multiple devices efficiently, and secondly, compensate for noise in real-time, this would mark an enormous step forward in the scalability of spin qubit architectures. Aims and objectives:- The aim of this project is to explore machine learning methods capable of tuning and stabilizing spin qubits over large length-scales. In doing so, the hope is to inspire the development of spin qubit architectures that are most resilient to variability and noise. Until now, spin qubit architectures have been pioneered with a physics mindset, i.e. how spin qubits can most easily interact with one another and how they can be most easily addressed using magnetic and electric fields. The novelty of this project approach lies in the software and hardware co-design, which has previously not been considered at the device design stage. This will be an interdisciplinary effort, drawing on both experimental physics and software methodologies, such as accelerated Bayesian optimization methods and time series analysis - a new effort within spin-based quantum computing. Some milestones for this project would include the characterization of material disorder at the wafer scale, expansion of tuning algorithms beyond a handful of qubits, and new methods for compensating noise, as would be evidenced by an enhancement in the fidelity of quantum operations. Some material platforms that could be investigated include Ge/SiGe heterostructures, Si-MOS, and Si Fin-FET devices, with the goal to isolate the most promising candidate. EPSRC alignment:- This project falls within the EPSRC Quantum Technologies research area.Any companies or collaborators involved:- An existing collaboration with Katsaros Group at the Institute of Science and Technology Austria (ISTA) will be extended, from whom we will receive quantum devices on which our algorithms and experiments will be tested. In addition, devices could be sourced from the Quantum Coherence Laboratory at the University of Basel or the Quantum Technologies division at the IBM Research Laboratory in Zurich or Scappucci Lab at QuTech. Moreover, to characterize quantum devices on a wafer scale, we will reach out to the teams at IBM or at CEA-Leti in Grenoble to use their state-of-the-art cryo-prober system. Lastly, we will continue collaborations with groups at the University of Oxford from departments of Engineering Science as well as Statistics, who provide invaluable algorithmic input.

基于自旋的量子计算机架构的可扩展和自动调整简要描述了研究背景，包括潜在影响： - 量子计算是一种新兴技术，有潜力使用新的计算范式解决经典的棘手问题。这种计算的基础是使用系统的量子态来编码信息，这与现代晶体管（位）的二进制态不同。在实现这些量子位（qubit）的众多候选平台中，限制在半导体结构中的自旋因其长相干时间、小尺寸以及易于与控制电子设备集成而具有吸引力，从而在可扩展性方面具有优势。挑战在于从概念验证设备（仅包含少量量子位）转向包含数十个此类量子位及更多的微芯片。目前，这受到器件可变性和与主体材料相关的噪声的阻碍，在第一种情况下，这使得器件调谐非常困难，而在第二种情况下，可能会导致量子操作的保真度降低，特别是如果这些噪声来源具有时空相关性。如果可以设计出巧妙的算法和协议，首先可以有效地调整多个设备，其次可以实时补偿噪声，这将标志着自旋量子位架构的可扩展性向前迈出了一大步。目的和目标：该项目的目的是探索能够在大长度范围内调整和稳定自旋量子位的机器学习方法。这样做的目的是激发对可变性和噪声最具弹性的自旋量子位架构的开发。到目前为止，自旋量子位架构一直以物理思维方式开创，即自旋量子位如何最容易地相互交互以及如何使用磁场和电场最容易地解决它们。该项目方法的新颖之处在于软件和硬件协同设计，这是以前在设备设计阶段未考虑到的。这将是一项跨学科的工作，借鉴了实验物理学和软件方法，例如加速贝叶斯优化方法和时间序列分析——基于自旋的量子计算领域的一项新工作。该项目的一些里程碑包括晶圆级材料无序的表征、将调谐算法扩展到少数量子位之外，以及补偿噪声的新方法，量子操作保真度的增强将证明这一点。一些可以研究的材料平台包括 Ge/SiGe 异质结构、Si-MOS 和 Si Fin-FET 器件，目的是分离出最有希望的候选材料。 EPSRC 联盟：- 该项目属于 EPSRC 量子技术研究领域。参与的任何公司或合作者：- 与奥地利科学技术研究所 (ISTA) Katsaros Group 的现有合作将得到扩展，我们将从他们那里获得量子设备我们的算法和实验将在其上进行测试。此外，设备还可以来自巴塞尔大学的量子相干实验室、苏黎世 IBM 研究实验室的量子技术部门或 QuTech 的 Scappucci 实验室。此外，为了表征晶圆级量子器件，我们将联系 IBM 或格勒诺布尔 CEA-Leti 的团队，使用他们最先进的低温探针系统。最后，我们将继续与牛津大学工程科学系和统计学系的团队合作，他们提供了宝贵的算法输入。