11 citations · 24 across the 5 of their papers we have counts for
5 papers
Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
Alperen Aksoy, Ilja Bekman, Vesselin Dimitrov +6
This paper examines the use of Quantized Neural Networks (QNNs) for two resource-constrained scientific applications: automated calibration of semiconductor quantum bits (qubits) a…
Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams
Fabian Hader, Fabian Fuchs, Sarah Fleitmann +5
Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability dia…
Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)
Fabian Hader, Sarah Fleitmann, Jan Vogelbruch +2
Quantum dots must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tun…
On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots
Fabian Hader, Jan Vogelbruch, Simon Humpohl +5
In gate-defined quantum dot systems, the conductance change of electrostatically coupled sensor dots allows the observation of the quantum dots' charge and spin states. Therefore,…
Data needs and challenges for quantum dot devices automation
Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews +17
Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However,…