Grant: Alliance Grants
Université de Sherbrooke
$231,000.00
CMOS-OxRAM integrated circuits for low-power AI and cryogenic quantum dot auto-tuning
FY 2022-23
Summary
The latest major breakthrough in quantum computing (QC) has been the demonstration of quantum systems with more than 50 superconducting qubits allowing quantum supremacy for the first time. Other very promising qubit technologies include spin qubits based on Si, SiGe or III-V quantum dots (QDs). They leverage the great maturity of CMOS technologies and fabrication processes to offer low cost and highly scalable quantum systems at the chip level. Major research centers and industrials like CEA and Intel have started to report high quality spin qubits based on semiconductor technologies. However, the calibration and control of spin qubits are still performed mostly by hand with bulky classical electronics located outside the cryostat. The absence of fully integrated cryogenic electronics makes it impossible to build a large-scale quantum system due to the "wiring bottleneck" between the spin qubits and the control electronics. Additionally, QDs are prone to device-to-device variability inherent to manufacturing imperfections and environment perturbations. One of the next major breakthroughs in QC is thus to automate the control of very large numbers of spin qubits based on QDs using integrated in-situ cryogenic electronics.The partnership between 3IT and 1QBit aims to (i) demonstrate real time auto-tuning of solid-state QDs using low-power machine learning and In-Memory Computing paradigm based on the co-integration of CMOS circuits and emerging TiOx-based resistive memories (OxRAM) and (ii) design and fabricate ultra-low power artificial intelligence hardware specifically designed to operate at cryogenic temperature for in-situ tuning. This unique proof-of-concept will pave the way for large-scale quantum systems enabled by fast and energy efficient AI solutions co-located with quantum circuits inside a cryostat.
Program
Alliance Grants
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
580722-2022
Name nom
Drouin, Dominique D
Department département
Génie électrique et génie informatique
Organization id
68
Institution établissement
Université de Sherbrooke
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
231000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
513
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Electrical and electronic machinery and equipment (including computer hardware)
Area of application fr
Machinerie et équipement électriques et électroniques (y compris matériel informatique)
Research subject code
2515
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Electronic materials and components
Research subject fr
Matériels et composants électroniques
Installment
inst:1
Partie
1
Nb partie
1
Application title
CMOS-OxRAM integrated circuits for low-power AI and cryogenic quantum dot auto-tuning
Application summary
The latest major breakthrough in quantum computing (QC) has been the demonstration of quantum systems with more than 50 superconducting qubits allowing quantum supremacy for the first time. Other very promising qubit technologies include spin qubits based on Si, SiGe or III-V quantum dots (QDs). They leverage the great maturity of CMOS technologies and fabrication processes to offer low cost and highly scalable quantum systems at the chip level. Major research centers and industrials like CEA and Intel have started to report high quality spin qubits based on semiconductor technologies. However, the calibration and control of spin qubits are still performed mostly by hand with bulky classical electronics located outside the cryostat. The absence of fully integrated cryogenic electronics makes it impossible to build a large-scale quantum system due to the "wiring bottleneck" between the spin qubits and the control electronics. Additionally, QDs are prone to device-to-device variability inherent to manufacturing imperfections and environment perturbations. One of the next major breakthroughs in QC is thus to automate the control of very large numbers of spin qubits based on QDs using integrated in-situ cryogenic electronics.The partnership between 3IT and 1QBit aims to (i) demonstrate real time auto-tuning of solid-state QDs using low-power machine learning and In-Memory Computing paradigm based on the co-integration of CMOS circuits and emerging TiOx-based resistive memories (OxRAM) and (ii) design and fabricate ultra-low power artificial intelligence hardware specifically designed to operate at cryogenic temperature for in-situ tuning. This unique proof-of-concept will pave the way for large-scale quantum systems enabled by fast and energy efficient AI solutions co-located with quantum circuits inside a cryostat.