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UpScale: Scalable quantum information enabled by integrated optics

UpScale: Scalable quantum information enabled by integrated optics
UpScale:通过集成光学实现可扩展的量子信息
批准号:
10006239
负责人:
金额:
$400.59万
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
项目状态:
未结题
起止时间:
2022 至 --

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英文摘要
Quantum information processing (QIP) will revolutionise many industries with applications ranging from drug discovery to supply chain management. However, QIP faces a technological challenge in scalability. To secure quantum advantage and a fault-tolerant general purpose quantum computer many high-fidelity qubits and sources must be controlled.UpScale brings together five commercial partners and two research organisations to address this challenge.By using a scalable integrated photonic routing and addressing platform, different QIP architectures of trapped-ions, diamond NV centres and semiconductor photon sources will be supported. The integrated photonic platform leverages decades of development in telecommunications systems and semiconductor manufacturing and is compatible with cryogenic temperature operation and multiple independent qubit systems.UpScale will develop and deploy two major and innovative integrated photonic technologies: a silicon nitride (SiN) photonic integrated chip platform and cryogenic-compatible photonic coupling and packaging.The focus of UpScale is delivery of high-TRL scalable demonstrators rather than fundamental research. It will build on several recently published results and use photonic foundry services to provide a reliable supply chain and solve technical challenges associated with scalability at the pace required for commercialisation.The project is designed to maximise return on investment by developing technological solutions for scaling of QIP systems, for the benefit of multiple commercial partners. Additional routes to market include the commercialisation of photonic systems and cryogenic packaging services.
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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis