Enabling large-scale silicon spin qubit platform using memristor-based neuromorphic circuits for quantum dots auto-tuning

使用基于忆阻器的神经形态电路实现量子点自动调节的大规模硅自旋量子位平台

• 批准号: RGPIN-2019-06183
• 负责人: Drouin, Dominique
• 金额: $ 4.66万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715961
• 关键词: Enabling; large; scale; silicon; spin

Since the first demonstrations of quantum computing based on nuclear magnetic resonance spectroscopy in 1997, tremendous progress has been made in the field and multiple technologies are now available to obtain high quality quantum bits (qubits). Great efforts are now channeled toward large-scale integration of qubits. In that regards, the first demonstration of spin manipulation in silicon in 2007 has identified the use of silicon technologies for spin-based quantum computing as one of the most seducing approaches. Silicon is indeed the foundation of modern electronics, from which more than 50 years of high-yield manufacturing of CMOS-based very large-scale integrated circuits (VLSI) can be leveraged towards logical qubits and large-scale quantum computing. Moreover, exceptional quantum coherence has been demonstrated with single electron spin in isotopically-enriched 28Si device. However, to make the step to large-scale quantum computation, an extensible integrated qubit system has yet to be developed. Using currently available room-temperature instrumentation to operate quantum devices in the cryogenic environment is only practical for current few-qubit systems. Knowing that nowadays the tuning of a dozen of qubits through several control gates is a laborious but feasible task, it becomes clear that a drastically higher number of qubits and I/Os is impossible to manage in these conditions. Scaling of interconnections and control lines with the number of qubits is thus considered as one of the main bottleneck preventing the creation of an actual quantum computer. The proposed research program seeks to enable large-scale silicon spin qubits platform by investigating the use of memristors and memristor-based neuromorphic circuits, co-integrated with quantum dots to greatly ease their formation and control while lowering the number of necessary I/Os. Such integration of memory and machine learning technologies in close vicinity of the quantum system would address at the same time the physical size, control and connection issues hindering the advent of mainstream quantum computing by i) offering scalable high-density and high-quality CMOS-based quantum dot integration, ii) storing in memristors the gate voltage values required to electrostatically form the quantum dots, iii) embedding memristor-based neuromorphic auto-tuning system and iv) dramatically reducing the number of required physical connections between the inside and the outside of the cryostat.

自1997年基于核磁共振光谱的量子计算的首次演示以来，该田间已经取得了巨大进展，现在可以使用多种技术来获得高质量的量子位（Qubits）。现在，为大规模的Qubits整合而进行了巨大的努力。在这方面，2007年硅旋转操作的首次演示证明了将硅技术用于基于自旋的量子计算是最引人入胜的方法之一。硅确实是现代电子产品的基础，从中，基于CMOS的非常大规模集成电路（VLSI）的高收益制造可以利用逻辑量子和大型量子计算。此外，在富含同位素增强的28SI设备中，已经证明了与单电子自旋的异常量子相干性。但是，为了迈出大规模量子计算的步骤，尚未开发可扩展的集成量子系统。使用当前可用的室温仪器在低温环境中操作量子设备，仅对于当前的几量量系统才是实用的。知道如今，通过几个控制门调整了十几个Qubit是一项费力但可行的任务，很明显，在这些条件下，无法管理的量子数量大大增加。因此，与量子数数量的互连和控制线的缩放被认为是阻止创建实际量子计算机的主要瓶颈之一。拟议的研究计划旨在通过调查使用候选人和基于备忘录的神经形态电路来实现大规模硅自旋量子平台，并与量子点共同集成以极大地减少其形成和控制，同时降低必要I/OS的数量。在量子系统附近附近的存储器和机器学习技术的这种集成将同时解决物理大小，控制和连接问题，从而阻碍了主流量子计算的出现，i）提供可扩展的高密度和高质量的CMOS-基于量子点积分，ii）存储在备注剂中静电形成量子点所需的栅极电压值，iii）嵌入基于备忘录的神经形态自动调节系统和iv）大大减少了内部和外部之间所需物理连接的数量低温恒温器。