76 citations · 305 across the 15 of their papers we have counts for
15 papers
Data-Driven Qubit Characterization and Optimal Control using Deep Learning
Paul Surrey, Julian D. Teske, Tobias Hangleiter +2
Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of eval…
Flopping-mode electron dipole spin resonance in the strong-driving regime
Julian D. Teske, Friederike Butt, Pascal Cerfontaine +2
Achieving high fidelity control of spin qubits with conventional electron dipole spin resonance (EDSR) requires large magnetic field gradients of about 1 mT/nm, which also couple t…
Minimising statistical errors in calibration of quantum-gate sets
Yaiza Aragonés-Soria, René Otten, Tobias Hangleiter +2
Calibration of quantum gates is a necessary hurdle to overcome on the way to a reliable quantum computer. In a recent paper, a protocol called Gate Set Calibration protocol (GSC) h…
Analytic Filter Function Derivatives for Quantum Optimal Control
Isabel Nha Minh Le, Julian D. Teske, Tobias Hangleiter +2
Auto-correlated noise appears in many solid state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However…
Filter Function Formalism and Software Package to Compute Quantum Processes of Gate Sequences for Classical Non-Markovian Noise
Tobias Hangleiter, Pascal Cerfontaine, Hendrik Bluhm
Correlated, non-Markovian noise is present in many solid-state systems employed as hosts for quantum information technologies, significantly complicating the realistic theoretical…
Filter Functions for Quantum Processes under Correlated Noise
Pascal Cerfontaine, Tobias Hangleiter, Hendrik Bluhm
Many qubit implementations are afflicted by correlated noise not captured by standard theoretical tools that are based on Markov approximations. While independent gate operations a…