Efficiently tuning quantum devices using machine learning

在纳米尺度上控制材料已经足够精细，因此需要新的方法来制造这些结构并调整它们的性能。在许多情况下，我们已经达到了人工控制流程的能力极限，当关键的考虑因素是技术应用程序的可扩展性时，更是如此。该项目确定了量子设备，机器学习将为加速、扩展和开放新技术提供关键。目标是优化量子器件的调谐。量子设备的可变性是一个需要解决的挑战，当关键考虑因素是量子计算等技术应用的可扩展性时，更是如此。为此，学生将开发机器学习技术，如深度学习和贝叶斯优化。人类专家需要数小时才能找到最佳的器件参数，阻碍了大型量子电路的实现。单个量子器件的参数空间至少是十维的。为了探索这个大的参数空间，将建立允许有效搜索最优参数的算法。实验将在低温下用专用的低噪声电子设备实现。量子器件将是半导体（如砷化镓）中静电定义的纳米结构。虽然机器学习在材料科学的数据挖掘方面已经很好地建立起来，并且开始用于材料设计，但是机器学习在控制纳米尺度实验方面的全部潜力尚未开发，并且显然需要先进的机器学习方法。使用机器学习技术来推进设备物理学，目前还处于早期阶段，它已经被证明在量子设备的制造、表征和调谐中至关重要。这个项目是释放量子技术潜力的关键，它允许对量子设备进行快速测量，从而有可能控制技术上相关的量子电路。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从致力于人工智能研究的计算机科学家的专业知识中受益，特别是贝叶斯优化和其他机器学习技术，这些技术不需要大量数据，而量子设备则无法获得这些数据。该项目属于EPSRC量子技术研究领域。学生正在与Graphcore公司进行案例转换

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. The objective is to optimise the tuning of quantum devices. Quantum device variability is a challenge to tackle, all the more so when a key consideration is scalability for technological applications such as quantum computing. For this, the student will develop machine learning techniques such as deep learning and Bayesian optimisation. Human expert takes hours to find the optimal device parameters, impeding the realisation of large quantum circuits. The parameter space of a single quantum device is at least ten dimensional. To explore this large parameter space, algorithms that allow for an efficient search of optimal parameters will be established. The experiments will be realized at cryogenic temperatures with dedicated low-noise electronics. The quantum devices will be nanostructures defined electrostatically in semiconductors such as gallium arsenide. Although machine learning is well established for data mining in materials science, and is starting to be used for materials design, the full potential of machine learning for controlling nanoscale experiments is untapped, and there is a clear need for advanced machine learning approaches. The use of machine learning techniques to advance device physics, is in its early stages and it is already proving essential in the fabrication, characterisation and tuning of quantum devices. 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.Student is case conversion with company Graphcore