2026
OrthoRF: Exploring Orthogonality in Object-Centric Representations
Despoina Touska, Bastiaan Onne Fagginger Auer, Alexandru Onose, Tejaswi Kasarla, Luis Armando Pérez Rey, Maximilian Lipp, Lyubov Amitonova, Martin R. Oswald, Pascal Cerfontaine
ICLR 2026
Paper
Introduces competitive binding and orthogonality loss for discrete object representations in synchrony-based object-centric learning, eliminating the need for post-hoc clustering and enabling occlusion recovery in semiconductor metrology.
Data-Driven Qubit Characterization and Optimal Control using Deep Learning
Paul Surrey, Julian D. Teske, Tobias Hangleiter, Hendrik Bluhm, Pascal Cerfontaine
arXiv:2601.18704, 2026
Paper
Presents deep learning methods for data-driven extraction of qubit parameters and optimal control of quantum devices.
Earlier Publications by Year
2023
Flopping-mode electron dipole spin resonance in the strong-driving regime
J. Teske, F. Butt, P. Cerfontaine, G. Burkard, H. Bluhm
Physical Review B 107, 035302 (2023)
Paper
2022
Minimising statistical errors in calibration of quantum-gate sets
Y. Aragonés-Soria, R. Otten, T. Hangleiter, P. Cerfontaine, D. Gross
arXiv:2206.03417 (2022)
Paper
qopt: An Experimentally-Oriented Qubit Simulation and Quantum Optimal Control Package
J. Teske, P. Cerfontaine, H. Bluhm
Physical Review Applied 17, 034036 (2022)
Paper
Analytic Filter Function Derivatives for Quantum Optimal Control
I. Le, J. Teske, T. Hangleiter, P. Cerfontaine, H. Bluhm
Physical Review Applied 17, 024006 (2022)
Paper
2021
Filter Functions for Quantum Processes under Correlated Noise
P. Cerfontaine, T. Hangleiter, H. Bluhm
Physical Review Letters 127, 170403 (2021)
Paper
Filter Function Formalism and Software Package to Compute Quantum Processes of Gate Sequences for Classical Non-Markovian Noise
T. Hangleiter, P. Cerfontaine, H. Bluhm
Physical Review Research 3, 043047 (2021)
Paper
2020
Closed-loop control of a GaAs-based singlet-triplet spin qubit with 99.5% gate fidelity and low leakage
P. Cerfontaine, T. Botzem, J. Ritzmann, S. S. Humpohl, A. Ludwig, D. Schuh, D. Bougeard, A. D. Wieck & H. Bluhm
Nature Communications 11, 4144 (2020)
Paper
Machine learning-based feedback control achieving a fidelity record of 99.5% in GaAs spin qubits.
Towards a realistic GaAs spin qubit device for a classical error-corrected quantum memory
M. Rispler, P. Cerfontaine, V. Langrock, B. Terhal
Physical Review A 102, 022416 (2020)
Paper
Self-Consistent Calibration of Quantum-Gate Sets
P. Cerfontaine, R. Otten, H. Bluhm
Physical Review Applied 13, 044071 (2020)
Paper
High-fidelity gate set for exchange-coupled singlet-triplet qubits
P. Cerfontaine, R. Otten, M. A. Wolfe, P. Bethke, H. Bluhm
Physical Review B 101, 155311 (2020)
Paper
2019
Transfer of a quantum state from a photonic qubit to a gate-defined quantum dot
B. Joecker, P. Cerfontaine, F. Haupt, L. R. Schreiber, B. E. Kardynal, H. Bluhm
Physical Review B 99, 205415 (2019)
Paper
A machine learning approach for automated fine-tuning of semiconductor spin qubits
J. D. Teske, S. S. Humpohl, R. Otten, P. Bethke, P. Cerfontaine, J. Dedden, A. Ludwig, A. D. Wieck, H. Bluhm
Applied Physics Letters 114, 133102 (2019)
Paper
2018
Tuning Methods for Semiconductor Spin Qubits
T. Botzem, M. D. Shulman, S. Foletti, S. P. Harvey, O. E. Dial, P. Bethke, P. Cerfontaine, R. P. G. McNeil, D. Mahalu, V. Umansky, A. Ludwig, A. Wieck, D. Schuh, D. Bougeard, A. Yacoby, H. Bluhm
Physical Review Applied 10, 054026 (2018)
Paper
Calculation of tunnel couplings in open gate-defined disordered quantum dot systems
J. Klos, F. Hassler, P. Cerfontaine, H. Bluhm, L. R. Schreiber
Physical Review B 98, 155320 (2018)
Paper
2016
Feedback-tuned noise-resilient gates for encoded spin qubits
P. Cerfontaine, T. Botzem, S. S. Humpohl, D. Schuh, D. Bougeard, H. Bluhm
arXiv:1606.01897 (2016)
Paper
2014
High-fidelity single-qubit gates for two-electron spin qubits in GaAs
Pascal Cerfontaine et al.
Physical Review Letters 113, 150501 (2014)
Paper
Demonstration of high-fidelity single-qubit gates using ML-optimized pulse sequences.
Patents
2025
Method and system for predicting metrology data using a neural network
Alexandru Onose, Tiago Botari, Timo Matthias Deist, Pascal Cerfontaine, Mariya Vyacheslavivna Medvedyeva, Zili Zhou, Markus Gerardus Martinus Maria van Kraaij
ASML Netherlands B.V., WO2025247596A1 (2025)
A method for generating predicted metrology data of a patterned portion using a neural network comprising an input network and an output network, with a common input model and specific input model architecture.
Translation prior for image analysis
Pascal Cerfontaine, S. M. Masudur Rahman Al Arif, Nick Verheul, Remco Dirks
ASML Netherlands B.V., WO2025233109A1 (2025)
An apparatus for training a machine learning model to determine a loss function for use in image analysis, encoding images of batches to incorporate translation priors.
Inference model training
Alexandru Onose, Scott Anderson Middlebrooks, Nick Verheul, Markus Gerardus Martinus Maria van Kraaij, Bart Jacobus Martinus Tiemersma, Pascal Cerfontaine
ASML Netherlands B.V., US20250355365A1 (2025)
A method of training an inference model to determine one or more parameters of a product of a fabrication process from measurements of the product.
Device and method for operating a semiconductor spin qubit quantum computer
Jan Klos, Matthias Künne, Pascal Cerfontaine
Forschungszentrum Jülich GmbH, EP4573490A1 (2025)
A method for operating a quantum chip with semiconductor heterostructure and gate electrodes providing shuttling lanes for moving single electrons in quantum computing applications.
Quantum computer design
Matthias Künne, Pascal Cerfontaine
RWTH Aachen, US20250131177A1 (2025)
A method for designing a quantum computing element for performing a quantum algorithm, configured to be operated with spin qubits and shuttling lanes with building blocks.
2023
Methods related to an autoencoder model or similar for manufacturing process parameter estimation
Alexandru Onose, Nick Verheul, Bart Jacobus Martinus Tiemersma, Pascal Cerfontaine, Davide Barbieri
ASML Netherlands B.V., WO2023186446A1 (2023)
A method for ordering and selection of latent elements for modeling low dimensional data within a latent space representation, with reduced dimensionality representation of input data.
Latent space synchronization of machine learning models for in-device metrology inference
Davide Barbieri, Pascal Cerfontaine
ASML Netherlands B.V., WO2023083564A1 (2023)
Autoencoder models for lithography to estimate, infer or predict parameters of interest (e.g., metrology metrics) trained with measurement data from substrates.
Operation of a quantum computing element
Matthias Künne, Pascal Cerfontaine
RWTH Aachen, WO2023117064A1 (2023)
Method for operating a quantum computing element with a network of shuttling lanes having multiple junctions and manipulation zones for spin qubits.
Code Repositories
Our code is available on GitHub under open-source licenses.