Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.

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  • Machine-learning the classification of spacetimes

    YHY. HeJI Physics Letters B

    AI classifies space-time

    A neural network learns to classify different types of spacetime in general relativity according to their algebraic Petrov classification.

  • The space of functions computed by deep layered machines

    AMA. MozeikaBLDS Physical Review Letters

    Deep layered machines

    The ability of deep neural networks to generalize can be unraveled using path integral methods to compute their typical Boolean functions.

  • Quantum generalisation of feedforward neural networks

    KWODO. DahlstenHKRGMK npj Quantum Information

    Quantum neural networks

    We generalise neural networks into a quantum framework, demonstrating the possibility of quantum auto-encoders and teleportation.

  • Emergence of strongly connected components in continuum disk-spin percolation

    FCF. CaravelliMBM. BardosciaFC Journal of Statistical Mechanics

    Clusters of neurons

    Percolation theory shows that the formation of giant clusters of neurons relies on a few parameters that could be measured experimentally.

  • Journal of Physics A

    Multi-tasking in immune networks

    Associative networks with different loads model the ability of the immune system to respond simultaneously to multiple distinct antigen invasions.