Machine Learning as an Enabler for Qubit Scalability, Quantum Computing

量子计算是一个相对现代的研究领域，具有丰富的潜在应用。这些扩展到广泛的领域，例如用于治疗和预防性医疗解决方案的药物发现，以及用于加强数字时代安全性的密码学。支持这些应用的物理实现同样多种多样，包括利用捕获离子的系统，位于量子点内部的自旋量子比特，以及利用电路量子电动力学的超导量子比特。尽管这些实现的性质不同，但它们都受到相同的挑战，特别是在相干时间，门速度和可扩展性方面。最佳性能要求门速度快，相干时间长。在上述每个实现中，已经创建了在门速度和相干时间之间显示理想平衡的单量子位系统。另一方面，这些参数通常是通过人工微调和人工操作员进行表征来实现的。这种方法虽然有效，但既资源密集，又不适合大规模操作，特别是那些需要数百万量子位来描绘任何深刻的量子优势的操作。正是在这种背景下，该项目提出了自动化调谐和校准过程。这个过程通常被称为量子控制，将由机器学习原理指导。本项目重点关注的特定机器学习方法是贝叶斯优化。该项目准备利用这种技术开发量子设备的广谱调谐算法--这是科学文献中严重未充分探索的领域。贝叶斯优化在能够详细描述不确定性的概率框架内运行。此功能可用于通知决策算法。与其他方法相比，该方法具有明显的优势-与经典的机器学习算法和神经网络相比，它可以更容易地将因果关系归因于实验数据。其他好处在于它对尚未看到或体验过的设备具有更大的通用性，通常会导致更快的预测能力。该项目将特别关注半导体器件，如互补金属氧化物半导体（CMOS）和由约瑟夫森结形成的超导电路。这些装置具有很大的物理相似性，使它们成为联合调查的合适候选者。通过与IST Austria、巴塞尔大学、查尔默斯大学和QuantWare公司的合作，这些设备将成为可能。我们与QuantrolOx公司的合作为该项目增加了另一层支持，QuantrolOx公司专门通过机器学习为超导量子比特创建控制软件。他们将在必要时提供机器学习技术和仪器集成方面的专业知识，并提供必要的援助。主要目标是开发贝叶斯优化算法，为调整半导体器件定制。这将使我们能够为这些设备获得最先进的性能，同时减少微调每个设备所花费的时间。该项目与EPSRC量子技术研究领域保持一致。

Quantum computing, a relatively contemporary field of study, boasts of abundant potential applications. These extend across broad sectors such as drug discovery for curative and preventative medical solutions, to cryptography for bolstering security in the digital age. The physical implementations supporting these applications are equally diverse, including systems utilising trapped ions, spin qubits situated inside quantum dots, and superconducting qubits that leverage circuit quantum electrodynamics.Despite the diverse nature of these implementations, they are uniformly affected by the same set of challenges, particularly in relation to coherence times, gate speeds and scalability in their operations. Optimal performance demands fast gate speeds partnered with long coherence times. Single qubit systems that display an ideal balance between gate speed and coherence time have been created in each of the aforementioned implementations. On the flip side, these parameters have often been realised through the manual fine-tuning and characterisation undertaken by human operators. This approach, while effective, is both resource-intensive and arduously unsuitable for large scale operations, particularly those demanding millions of qubits to delineate any profound quantum advantage.It is against this backdrop that this project proposes to automate the tuning and calibration process. This process, typically referred to as quantum control, will be guided by the principles of machine learning. The specific machine learning method that will be in focus for this project is Bayesian Optimisation. The project is poised to utilise this technique for the development of a broad-spectrum tuning algorithm for quantum devices - a domain severely underexplored in scientific literature.Bayesian optimisation operates within a probabilistic framework capable of detailing uncertainty. This feature can be used to inform decision-making algorithms. The method holds distinct advantages over alternative methods - it enables easier attribution of cause-and-effect relationships to experimental data, as compared to classical machine learning algorithms and neural networks. Additional benefits lie in its greater generalisability to devices that are yet to be seen or experienced and often results in quicker predictive capabilities.This project will specifically focus on semiconductor devices, like the complementary metal-oxide-semiconductor (CMOS) and superconducting circuits formed from Josephson Junctions. These devices possess significant physical similarity, making them suitable candidates for joint investigation. Access to these devices will be made possible via partnerships with IST Austria, University of Basel, University of Chalmers and the company QuantWare.Adding another layer of support to the project is our collaboration with QuantrolOx, a company specialising in the creation of control software for superconducting qubits via machine learning. They will lend their expertise in machine learning techniques and instrument integration, wherever necessary, providing requisite assistance. The principal goal is the development of Bayesian optimisation algorithms customised for tuning semiconductor devices. This will enable us to obtain state-of-the-art performance for these devices, while simultaneously reducing the time spent on fine-tuning each device. This project is well-aligned with the EPSRC Quantum technologies research area.