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

自1997年基于核磁共振波谱的量子计算首次演示以来，该领域取得了巨大进展，现在有多种技术可以获得高质量的量子比特。目前，人们正在努力实现量子比特的大规模集成。在这方面，2007年首次在硅中进行自旋操纵的演示已经确定了将硅技术用于基于自旋的量子计算是最诱人的方法之一。硅确实是现代电子产品的基础，50多年来基于cmos的超大规模集成电路（VLSI）的高产量制造可以用于逻辑量子位和大规模量子计算。此外，在同位素富集的28Si器件中，用单电子自旋证明了特殊的量子相干性。然而，为了实现大规模量子计算，还需要开发可扩展的集成量子比特系统。使用目前可用的室温仪器在低温环境中操作量子器件仅适用于当前的少量量子比特系统。知道现在通过几个控制门调整十几个量子位是一项艰苦但可行的任务，很明显，在这些条件下，大量的量子位和I/ o是不可能管理的。因此，互连和控制线的量子位数被认为是阻碍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研究记忆电阻器和基于记忆电阻器的神经形态电路的使用来实现大规模硅自旋量子比特平台，与量子点共集成，以大大简化其形成和控制，同时降低必要的I/ o数量。这种在量子系统附近的存储器和机器学习技术的集成将同时解决阻碍主流量子计算出现的物理尺寸，控制和连接问题，i)提供可扩展的高密度和高质量的基于cmos的量子点集成，ii)在记忆电阻器中存储静电形成量子点所需的门电压值。Iii)嵌入基于忆阻器的神经形态自调谐系统iv)显著减少低温恒温器内外之间所需的物理连接数量。

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.