Efficiently measuring a quantum device using machine learning. - Dominic T. Lennon; Hyungil Moon; Leon C. Camenzind; Liuqi Yu; Dominik M. Zumbühl; G. Andrew D. Briggs; Michael A. Osborne; Edward A. Laird; Natalia Ares (CoRR, 2018)

Machine learning enables completely automatic tuning of a quantum device faster than human experts. - Hyungil Moon; Dominic T. Lennon; James Kirkpatrick; Nina M. van Esbroeck; Leon C. Camenzind; Liuqi Yu; Florian Vigneau; Dominik M. Zumbühl; G. Andrew D. Briggs; Michael A. Osborne; Dino Sejdinovic; Edward A. Laird; et al. (CoRR, 2020)

Quantum device fine-tuning using unsupervised embedding learning. - Nina M. van Esbroeck; Dominic T. Lennon; Hyungil Moon; V. Nguyen; Florian Vigneau; Leon C. Camenzind; Liuqi Yu; Dominik M. Zumbühl; G. Andrew D. Briggs; Dino Sejdinovic; Natalia Ares (CoRR, 2020)

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. - V. Nguyen; S. B. Orbell; Dominic T. Lennon; Hyungil Moon; Florian Vigneau; Leon C. Camenzind; Liuqi Yu; Dominik M. Zumbühl; G. Andrew D. Briggs; Michael A. Osborne; Dino Sejdinovic; Natalia Ares (CoRR, 2020)

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. - Brandon Severin; Dominic T. Lennon; Leon C. Camenzind; Florian Vigneau; Federico Fedele; D. Jirovec; A. Ballabio; D. Chrastina; G. Isella; M. de Kruijf; Miguel J. Carballido; Simon Svab; et al. (CoRR, 2021)

Bridging the reality gap in quantum devices with physics-aware machine learning. - David L. Craig; H. Moon; Federico Fedele; Dominic T. Lennon; Barnaby van Straaten; Florian Vigneau; Leon C. Camenzind; Dominik M. Zumbühl; G. Andrew D. Briggs; Michael A. Osborne; Dino Sejdinovic; Natalia Ares (CoRR, 2021)

Identifying Pauli spin blockade using deep learning. - Jonas Schuff; Dominic T. Lennon; Simon Geyer; David L. Craig; Federico Fedele; Florian Vigneau; Leon C. Camenzind; Andreas V. Kuhlmann; G. Andrew D. Briggs; Dominik M. Zumbühl; Dino Sejdinovic; Natalia Ares (Quantum, 2023)

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Michael A. Osborne; Sebastian Orbell; Natalia Ares; Krikamol Muandet; Siu Lun Chau (AISTATS, 2024)

Fully autonomous tuning of a spin qubit. - Jonas Schuff; Miguel J. Carballido; Madeleine Kotzagiannidis; Juan Carlos Calvo; Marco Caselli; Jacob Rawling; David L. Craig; Barnaby van Straaten; Brandon Severin; Federico Fedele; Simon Svab; Pierre Chevalier Kwon; et al. (CoRR, 2024)

Quantum feedback control with a transformer neural network architecture. - Pranav Vaidhyanathan; Florian Marquardt; Mark T. Mitchison; Natalia Ares (CoRR, 2024)

MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence. - Pranav Vaidhyanathan; Aristotelis Papatheodorou; Mark T. Mitchison; Natalia Ares; Ioannis Havoutis (CoRR, 2025)

Meta-learning characteristics and dynamics of quantum systems. - Lucas Schorling; Pranav Vaidhyanathan; Jonas Schuff; Miguel J. Carballido; Dominik M. Zumbühl; Gerard Milburn; Florian Marquardt; Jakob N. Foerster; Michael A. Osborne; Natalia Ares (CoRR, 2025)

A Physics-Inspired Optimizer: Velocity Regularized Adam. - Pranav Vaidhyanathan; Lucas Schorling; Natalia Ares; Michael A. Osborne (CoRR, 2025)

Quantum computing and artificial intelligence: status and perspectives. - Giovanni Acampora; Andris Ambainis; Natalia Ares; Leonardo Banchi; Pallavi Bhardwaj; Daniele Binosi; G. Andrew D. Briggs; Tommaso Calarco; Vedran Dunjko; Jens Eisert; Olivier Ezratty; Paul Erker; et al. (CoRR, 2025)

Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures. - Aristotelis Papatheodorou; Pranav Vaidhyanathan; Natalia Ares; Ioannis Havoutis (CoRR, 2025)