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

Détails de la recherche
Numéro de l'application : RGPIN-2019-06183
Année de concours : 2019 Année financière : 2021-2022
Nom de la personne : Drouin, Dominique Institution : Université de Sherbrooke
Département : Génie électrique et génie informatique - Génie électrique et génie informatique Province : Québec
Montant : 64 000 $ Versement : 1 - 5
Type de programme : Programme de subventions à la découverte - individuelles Comité évaluateur : Génie électrique et informatique
Sujet de recherche : Matériels et composants électroniques Domaine d'application : Machinerie et équipement électriques et électroniques (y compris matériel informatique)
Chercheurs associés : Aucun associé Partenaires : Aucun partenaire
Sommaire du projet

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.