Generative Invertible Quantum Neural Networks

Understand

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.