Generative Invertible Quantum Neural Networks
2023-02-24Code Available0· sign in to hype
Armand Rousselot, Michael Spannowsky
Code Available — Be the first to reproduce this paper.
ReproduceCode
- gitlab.com/RussellA/quantumMLOfficialIn paperpytorch★ 0
Abstract
Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.