Realistic machine learning based ultra fast simulator for semiconductor spin qubit devices

用于半导体自旋量子位器件的基于真实机器学习的超快速模拟器

• 批准号: 10031865
• 金额: $ 49.14万
• 依托单位: QUANTROLOX LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10031865
• 关键词: Realistic; machine; learning; based; ultra

Spin qubits are one of the potential hardware platforms for quantum computing. For semiconducting spin qubits to be operated most devices must be kept at 20 mK. This low temperature operation requires very expensive cryogenic lab equipment to maintain the semiconducting spin qubit devices at operational temperatures.As quantum computing companies ramp up their production during this exciting period of growth, experimental time is a limited resource as the suppliers of cryogenic lab equipment try to keep up with demand.The development of a qubit control software for quantum computing requires access to quantum device hardware to test and improve the performance. A spin qubit machine learning based ultra fast simulator (MLUS) will enable the development of this qubit control with limited access to quantum computing hardware removing a major obstacle for QuantrolOx and other companies. The difficulty is that available quantum device simulators are significantly slower than the measurements that can be performed in this hardware. In order to develop software able to control quantum devices in real time, we require MLUSs. With this goal, our MLUS will be based on novel machine learning approaches.This represents an enormous **market opportunity** to accelerate the QuantrolOx qubit control software that will enable UK quantum computing hardware companies to scale and also for other companies to develop their qubit control algorithms using the MLUS.The proposed project will be a key step in realising automated qubit tuning, optimisation and stabilisation. The implemented automated qubit control software that will enable quantum computers to scale substantially beyond current capabilities.This is a joint project between the leading UK company producing automated control software for quantum technologies, the leading UK laboratory developing AI for quantum technologies, and the UK National Centre for Quantum Computing.

自旋量子比特是量子计算的潜在硬件平台之一。对于要操作的半导体自旋量子比特，大多数设备必须保持在20 mK。这种低温操作需要非常昂贵的低温实验室设备来维持半导体自旋量子比特器件在操作温度下。随着量子计算公司在这一令人兴奋的增长时期提高产量，由于低温实验室设备的供应商试图跟上需求，实验时间是有限的资源。开发用于量子计算的量子位控制软件需要访问量子设备硬件来测试和改进性能。基于自旋量子位机器学习的超快模拟器（MLUS）将使这种量子位控制的开发能够在有限的量子计算硬件访问权限下进行，从而为QuantrolOx和其他公司消除了一个主要障碍。困难在于，可用的量子设备模拟器比可以在这种硬件中执行的测量慢得多。为了开发能够真实的控制量子设备的软件，我们需要MLUS。基于这一目标，我们的MLUS将基于新的机器学习方法。这代表着一个巨大的 ** 市场机会 **，可以加速QuantrolOx量子位控制软件，使英国量子计算硬件公司能够扩大规模，也使其他公司能够使用MLUS开发量子位控制算法。拟议的项目将是实现自动量子位调谐，优化和稳定的关键一步。已实施的自动化量子位控制软件将使量子计算机的规模大大超过当前的能力。这是一个联合项目，由为量子技术生产自动化控制软件的英国领先公司、为量子技术开发人工智能的英国领先实验室和英国国家量子计算中心共同开发。