Qubit-Simulationen & Datenverarbeitung

  T-junction Urheberrecht: © Ran Xue

B.Sc.-Arbeit Impacts of charged defects on T-junction shuttling devices in Si/SiGe

In this project, you will learn about the concept of conveyor-mode shuttling of spin qubits for large scale quantum computing and implementing the perturbation theory by modelling a realistic quantum device. It is a project that closely cooperates with our experimental team to design and optimize the next generation of shuttling devices.

Projektbeschreibung (PDF)

  Machine Learning based Charge Stability Diagram Model and Feature Extractor Urheberrecht: © Ran Xue

M.Sc.-Arbeit Machine Learning based Charge Stability Diagram Model and Feature Extractor

In this project, you will train a neural network to learn a parametrized representation of charge stability diagram measurements. Then you will build methods to utilize this model as a parametrized simulation, as a feature extractor, and as an out-of-distribution detector.

Projektbeschreibung (PDF)

  T-junction Urheberrecht: © Lars Schreiber

M.Sc.-Arbeit High level simulation of a QuBus-based architecture for quantum ​computation

In this project you will carry out a detailed study of the operation and performance of the novel QuBus architecture for spin-based quantum computing, which is designed to overcome the problem of scalability that often limits the number of qubits in a quantum computer.

Projektbeschreibung (PDF)

  Thermische Lösungen für das Quantencomputing im großen Maßstab Urheberrecht: © Otten/Surrey

B.Sc.-Arbeit Thermal Solutions for Large Scale Quantum Computing

In this project, you will perform thermal simulations of our experiments in order to gain insights on the performance and constraints for large scale quantum computing. You will devise an experiment to gather the necessary low temperature data needed for a cryo-toolkit for spin qubits. This knowledge is essential for future quantum processors.

Projektbeschreibung (PDF)



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