Machine Learning as an Enabler for Qubit Scalability, Quantum Computing

机器学习作为量子位可扩展性和量子计算的推动者

• 批准号: 2891528
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891528
• 关键词: Machine; Learning; Enabler; Qubit; Scalability

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.

量子计算是一个相对当代的研究领域，具有丰富的潜在应用。这些范围跨越了广泛的部门，例如用于治疗和预防性医疗解决方案的药物发现，以加强数字时代的安全性。支持这些应用的物理实现同样多样化，包括利用被捕获的离子，位于量子点内的自旋量值以及利用量子量子电动力学的超导量子的系统。尽管这些实现的多样性，但它们还是受到相同的挑战的统一影响，尤其是在相同的挑战中，尤其是与配置时间和级数相同。最佳性能要求快速速度与较长的连贯时间合作。在上述每个实现中都创建了在门速度和相干时间之间显示理想平衡的单个量子系统。另一方面，这些参数通常是通过人类操作员进行的手动微调和表征来实现的。这种方法虽然有效，但既是资源密集型，又不适合大规模操作，尤其是那些要求数百万量子器来描述任何深刻的量子优势的人，这是在这个项目的背景下，该项目提出了自动调整和校准过程。这个过程通常称为量子控制，将由机器学习的原理指导。该项目将重点关注的特定机器学习方法是贝叶斯优化。该项目有望利用这项技术来开发用于量子设备的广谱调谐算法 - 在科学文献中严重不受影响的领域。Bayesian优化在能够详细详细说明不确定性的概率框架内运行。此功能可用于告知决策算法。该方法与替代方法具有不同的优势 - 与经典的机器学习算法和神经网络相比，它可以更轻松地归因于实验数据。其他好处在于它对尚待看到或经历的设备的更大概括性，并且通常会带来更快的预测能力。该项目将特别关注半导体设备，例如由Josephson Chincions形成的互补金属氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物 - 氧化物。这些设备具有明显的身体相似性，使其成为合适的候选人进行联合研究。通过与奥地利IST，巴塞尔大学，Chalmers和公司定量软件的合作伙伴关系，可以访问这些设备。为该项目提供另一层支持，是我们与Quantrolox的合作，这是我们与一家专门通过机器学习创建控制软件的公司的公司，该公司专门为超导Qubits创建控制软件。他们将在机器学习技术和仪器集成方面提供专业知识，无论需要提供必要的帮助。主要目标是开发用于调整半导体设备的定制的贝叶斯优化算法。这将使我们能够获得这些设备的最新性能，同时减少在微调每个设备上花费的时间。该项目与EPSRC量子技术研究领域非常合并。