TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.16.6.146
TI  - Generative invertible quantum neural networks
PY  - 2024/06/04
UR  - https://scipost.org/SciPostPhys.16.6.146
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 16
IS  - 6
SP  - 146
A1  - Rousselot, Armand
AU  - Spannowsky, Michael
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.16.6.146,
	title={{Generative invertible quantum neural networks}},
	author={Armand Rousselot and Michael Spannowsky},
	journal={SciPost Phys.},
	volume={16},
	pages={146},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.16.6.146},
	url={https://scipost.org/10.21468/SciPostPhys.16.6.146},
}