48 citations · 138 across the 9 of their papers we have counts for
9 papers
End-to-End Analysis of Charge Stability Diagrams with Transformers
Rahul Marchand, Lucas Schorling, Cornelius Carlsson +8
Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to anal…
Meta-learning characteristics and dynamics of quantum systems
Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff +7
While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, ho…
QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays
Barnaby van Straaten, Joseph Hickie, Lucas Schorling +3
Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental…
Fully autonomous tuning of a spin qubit
Jonas Schuff, Miguel J. Carballido, Madeleine Kotzagiannidis +14
Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor…
Compromise-Free Scaling of Qubit Speed and Coherence
Miguel J. Carballido, Simon Svab, Rafael S. Eggli +12
Across leading qubit platforms, a common trade-off persists: increasing coherence comes at the cost of operational speed, reflecting the notion that protecting a qubit from its noi…
The Automated Bias Triangle Feature Extraction Framework
Madeleine Kotzagiannidis, Jonas Schuff, Nathan Korda
Bias triangles represent features in stability diagrams of Quantum Dot (QD) devices, whose occurrence and property analysis are crucial indicators for spin physics. Nevertheless, c…
Identifying Pauli spin blockade using deep learning
Jonas Schuff, Dominic T. Lennon, Simon Geyer +9
Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We presen…
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
Lukas J. Fiderer, Jonas Schuff, Daniel Braun
Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time…
Improving the dynamics of quantum sensors with reinforcement learning
Jonas Schuff, Lukas J. Fiderer, Daniel Braun
Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while usi…