Elvet - a neural network-based differential equation and variational problem solver. - Jack Y. Araz; Juan Carlos Criado; Michael Spannowsky (CoRR, 2021)

Quantum Optimisation of Complex Systems with a Quantum Annealer. - Steve Abel; Andrew Blance; Michael Spannowsky (CoRR, 2021)

Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States. - Jack Y. Araz; Michael Spannowsky (CoRR, 2021)

Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data. - Jack Y. Araz; Michael Spannowsky (CoRR, 2022)

Completely Quantum Neural Networks. - Steve Abel; Juan Carlos Criado; Michael Spannowsky (CoRR, 2022)

Qade: Solving Differential Equations on Quantum Annealers. - Juan Carlos Criado; Michael Spannowsky (CoRR, 2022)

IRC-safe Graph Autoencoder for an unsupervised anomaly detection. - Oliver Atkinson; Akanksha Bhardwaj; Christoph Englert; Partha Konar; Vishal S. Ngairangbam; Michael Spannowsky (CoRR, 2022)

Identifying (anti-)skyrmions while they form. - Jack Y. Araz; Juan Carlos Criado; Michael Spannowsky (CoRR, 2022)

A Genetic Quantum Annealing Algorithm. - Steven Abel; Luca A. Nutricati; Michael Spannowsky (CoRR, 2022)

Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection. - Jack Y. Araz; Michael Spannowsky (CoRR, 2022)

Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models. - Ezequiel Alvarez; Michael Spannowsky; Manuel Szewc (Frontiers Artif. Intell., 2022)

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection. - Oliver Atkinson; Akanksha Bhardwaj; Christoph Englert; Partha Konar; Vishal S. Ngairangbam; Michael Spannowsky (Frontiers Artif. Intell., 2022)

A duality connecting neural network and cosmological dynamics. - Sven Krippendorf; Michael Spannowsky (Mach. Learn. Sci. Technol., 2022)

Generative Invertible Quantum Neural Networks. - Armand Rousselot; Michael Spannowsky (CoRR, 2023)

Generating Reservoir State Descriptions with Random Matrices. - Samuel Tovey; Christian Holm; Michael Spannowsky (CoRR, 2024)

Optimal Symmetries in Binary Classification. - Vishal S. Ngairangbam; Michael Spannowsky (CoRR, 2024)

The role of data embedding in quantum autoencoders for improved anomaly detection. - Jack Y. Araz; Michael Spannowsky (CoRR, 2024)

Collective variables of neural networks: empirical time evolution and scaling laws. - Samuel Tovey; Sven Krippendorf; Michael Spannowsky; Konstantin Nikolaou; Christian Holm (CoRR, 2024)

FeynMG: A FeynRules extension for scalar-tensor theories of gravity. - Sergio Sevillano Muñoz; Edmund J. Copeland; Peter Millington; Michael Spannowsky (Comput. Phys. Commun., 2024)

Training neural networks with universal adiabatic quantum computing. - Steve Abel; Juan Carlos Criado; Michael Spannowsky (Frontiers Artif. Intell., 2024)

Quantum pathways for charged track finding in high-energy collisions. - Christopher Brown; Michael Spannowsky; Alexander Tapper; Simon Williams; Ioannis Xiotidis (Frontiers Artif. Intell., 2024)

Optimal equivariant architectures from the symmetries of matrix-element likelihoods. - Daniel Maître; Vishal S. Ngairangbam; Michael Spannowsky (Mach. Learn. Sci. Technol., 2025)

Generating quantum reservoir state representations with random matrices. - Samuel Tovey; Tobias Fellner; Christian Holm; Michael Spannowsky (Mach. Learn. Sci. Technol., 2025)

Communicating Likelihoods with Normalising Flows. - Jack Y. Araz; Anja Beck; Méril Reboud; Michael Spannowsky; Danny van Dyk (CoRR, 2025)

Enhancing anomaly detection with topology-aware autoencoders. - Vishal S. Ngairangbam; Blazej Rozwoda; Kazuki Sakurai; Michael Spannowsky (CoRR, 2025)

Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States. - Vishal S. Ngairangbam; Michael Spannowsky; Timur Sypchenko (CoRR, 2025)