Learning to learn with quantum neural networks via classical neural networks. - Guillaume Verdon; Michael Broughton; Jarrod R. McClean; Kevin J. Sung; Ryan Babbush; Zhang Jiang; Hartmut Neven; Masoud Mohseni (CoRR, 2019)

Quantum Graph Neural Networks. - Guillaume Verdon; Trevor McCourt; Enxhell Luzhnica; Vikash Singh; Stefan Leichenauer; Jack Hidary (CoRR, 2019)

Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm. - Guillaume Verdon; Jacob Marks; Sasha Nanda; Stefan Leichenauer; Jack Hidary (CoRR, 2019)

TensorFlow Quantum: A Software Framework for Quantum Machine Learning. - Michael Broughton; Guillaume Verdon; Trevor McCourt; Antonio J. Martinez; Jae Hyeon Yoo; Sergei V. Isakov; Philip Massey; Murphy Yuezhen Niu; Ramin Halavati; Evan Peters; Martin Leib; Andrea Skolik; et al. (CoRR, 2020)

A semi-agnostic ansatz with variable structure for quantum machine learning. - Matías Bilkis; Marco Cerezo; Guillaume Verdon; Patrick J. Coles; Lukasz Cincio (CoRR, 2021)

Group-Invariant Quantum Machine Learning. - Martin Larocca; Frederic Sauvage; Faris M. Sbahi; Guillaume Verdon; Patrick J. Coles; Marco Cerezo (CoRR, 2022)

Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry. - Faris M. Sbahi; Antonio J. Martinez; Sahil Patel; Dmitri Saberi; Jae Hyeon Yoo; Geoffrey Roeder; Guillaume Verdon (CoRR, 2022)

Challenges and opportunities in quantum machine learning. - Marco Cerezo; Guillaume Verdon; Hsin-Yuan Huang; Lukasz Cincio; Patrick J. Coles (Nat. Comput. Sci., 2022)

A semi-agnostic ansatz with variable structure for variational quantum algorithms. - Matías Bilkis; Marco Cerezo; Guillaume Verdon; Patrick J. Coles; Lukasz Cincio (Quantum Mach. Intell., 2023)