Cryogenic memristor-based qubit control and AI autotuning for scalable quantum computing - Dominique Drouin

Quantum computing has the potential to revolutionize fields such as healthcare, materials science, and climate modeling. However, a major challenge lies in scaling quantum processors while ensuring efficient qubit control, particularly in silicon-based systems. This requires minimizing the number of input/output connections between classical control electronics and qubits inside the cryostat, especially for silicon qubits manipulated by electrostatic gates.

This seminar presents an innovative approach using TiOx-based memristors for cryogenic control electronics, enabling more efficient and scalable qubit management. Experimental validation confirmed their functionality in cryogenic environments, while circuit simulations demonstrated their potential for integration with quantum processors. Additionally, artificial neural networks were leveraged to automate quantum dot calibration, specifically to achieve the single-electron regime. These advancements pave the way for more practical and energy-efficient quantum computing systems.

The event is sponsored by Quantum City.