Fabian Hader - DR Project
Semiconductor Qubit Tuning Automatization
The focal point of our research endeavors lies in the automated tuning of qubits through the use of cryogenic electronics developed within our institute. Tuning refers to the preceding highly complex steps required for the spin-qubit operation. Currently, these steps are typically executed manually for each qubit; however, our objective is to automate these steps to enhance performance and facilitate scalability. The operation of qubits necessitates a series of preparatory processing steps, during which the quality of the data and their processing efficiency are of primary importance. Therefore, we develop algorithms that support fast processing with minimal complexity to enable hardware integration. Our methodologies demonstrate the applicability of the data originating from the aforementioned electronics.
Doctoral Researcher
We investigate processing methods ranging from traditional signal processing to deep learning methods, with the latter necessitating substantial training data, which we facilitate by developing appropriate simulation methods. During the development of our algorithms, we also prioritize the applicability of our algorithm for different spin-qubit implementations, including GaAs and SiGe heterostructures.
For a scalable qubit system, our concept involves the integration of a control module, implemented as a cryogenic IC, that directly interfaces with the qubit. This approach has the potential to enhance the quality of the measured data needed for the tuning process by eliminating the need for data transfer between different temperature stages, which can introduce noise into the measurements. Moreover, the implementation of a control module in close proximity to the qubit is imperative to address the challenge posed by the wiring bottleneck, which arises from the need to transmission tuning signals to each qubit.