Reproduce “Theory for Equivariant Quantum Neural Networks” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Quynh T. Nguyen, Louis Schatzki, Paolo Braccia, Michael Ragone, Patrick J. Coles, Frederic Sauvage, Martin Larocca, M. Cerezo
The authors present a framework for constructing equivariant quantum neural network layers, extending quantum convolutional neural networks with equivariant convolution and pooling. The layers make symmetry transformations commute with computation and can be built by three methods. In a spin-chain phase-classification experiment, SU(2)-equivariant models benefit on odd chains with degenerate ground spaces, but not even chains with unique ground states.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.