Machine learning for quantum device tuning and simulation

用于量子器件调谐和模拟的机器学习

• 批准号: 2266701
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2266701
• 关键词: Machine; learning; quantum; device; tuning

Controlling materials on the nanoscale is now sufficiently refined that new methods are needed for fabricating the structures and tuning their performance. In many cases we are reaching the limits of our ability to do this using human control of the process, all the more so when a key consideration is scalability for technological applications. The project identifies quantum devices where machine learning will provide the key to speeding up, scaling up, and opening up new technologies. Bayesian optimisation is state-of-the-art for expensive (in time or money) problems. Given the available data, Bayesian optimisation methods builds a low-cost machine learning model (an inexpensive probabilistic approximation of the actual experiment) whose predictions are cheap to compute. The model leads to the optimal location for the next measurement. The machine uses the model to find an objective in the most cost-effective way.The objective is to combine Bayesian optimisation with other machine learning methods, such as neural networks, to control quantum hardware-in-the-loop systems. Hardware-in-the-loop testing allows actual hardware under test to be interfaced with a software model in real time. Swapping out individual quantum devices one by one, the test results can be used to simulate realistic (and therefore imperfect) signals passing between components of the integrated device. The experiments will be realized at cryogenic temperatures with dedicated radio-frequency electronics. The novelty of the research methodology resides in the use of hardware-in-the-loop simulations to enable the machine to learn tuning techniques in multi quantum device circuits. Our extensible simulation and test platform provides a hardware-in-the-loop facility for applying machine learning to subsystems in a simulated environment, with a view to accelerating the development and prototyping of advanced complex quantum technologies.This project is key to unleash the potential of quantum technologies by allowing fast measurement of quantum devices, and thus the possibility to control technologically relevant quantum circuits. A collaboration with University of Basel provides us with the quantum devices we require for our experiments, and a collaboration with the Department of Engineering at University of Oxford allows us to benefit from the expertise of computer scientist dedicated to the study of artificial intelligence, in particular Bayesian optimisation and other machine learning techniques which do not require large amounts of data, which is not available for quantum devices. This project falls within the EPSRC Quantum technologies research area.

在纳米尺度上控制材料现在已经足够精细，需要新的方法来制造结构和调整它们的性能。在许多情况下，我们通过人工控制流程来实现这一目标的能力已经达到了极限，当关键考虑因素是技术应用的可扩展性时，情况就更是如此。该项目确定了量子设备，机器学习将为加速，扩大和开放新技术提供关键。贝叶斯优化是最先进的昂贵（在时间或金钱）的问题。给定可用数据，贝叶斯优化方法构建了一个低成本的机器学习模型（实际实验的廉价概率近似），其预测计算成本很低。该模型导致下一次测量的最佳位置。机器使用模型以最具成本效益的方式找到目标。目标是将联合收割机贝叶斯优化与其他机器学习方法（如神经网络）结合起来，以控制量子硬件在环系统。硬件在环测试允许被测实际硬件与软件模型在真实的时间内接口。通过一个接一个地交换单个量子器件，测试结果可以用来模拟集成器件组件之间传递的真实（因此是不完美的）信号。这些实验将在低温下用专用的射频电子设备进行。研究方法的新奇在于使用硬件在环仿真，使机器能够学习多量子器件电路中的调谐技术。我们的可扩展仿真和测试平台提供了一个硬件在环设施，用于在模拟环境中将机器学习应用于子系统，以加速先进复杂量子技术的开发和原型设计。该项目是释放量子技术潜力的关键，允许快速测量量子设备，从而有可能控制技术相关的量子电路。与巴塞尔大学的合作为我们提供了实验所需的量子设备，与牛津大学工程系的合作使我们能够受益于致力于人工智能研究的计算机科学家的专业知识，特别是贝叶斯优化和其他不需要大量数据的机器学习技术，这对于量子设备来说是不可用的。该项目属于EPSRC量子技术研究领域的福尔斯。