Generative Invertible Quantum Neural Networks
This work addresses the challenge of efficiently modeling high-energy physics data for particle collider precision measurements, representing an incremental advancement by integrating quantum computing with classical neural networks.
The authors tackled the problem of simulating and generating complex particle collider data by proposing a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and applying it to LHC data of Z-boson decay. They found that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating this data.
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.