Hybrid between biologically inspired and quantum inspired many-body states
Miha SRDINSEK (CEA Grenoble)
Deep neural networks can represent very different sorts of functions, including complex quantum many-body states. Tensor networks can also represent these states, have more structure and are easier to optimize. However, they can be prohibitively costly computationally in two or higher dimensions. In this seminar I will propose a hybrid network [1] which borrows features from the two different formalisms. I will showcase the ansatz by obtaining the representation of a transverse field quantum Ising model with a long range 1/r^6 antiferromagnetic interaction on a 10×10 square lattice. The model corresponds to the Rydberg (cold) atoms platform proposed for quantum annealing.
[1] Srdinsek, Waintal, arXiv:2506.05050 (2025)