Generative Invertible Quantum Neural Networks
- URL: http://arxiv.org/abs/2302.12906v3
- Date: Thu, 22 Feb 2024 15:55:13 GMT
- Title: Generative Invertible Quantum Neural Networks
- Authors: Armand Rousselot and Michael Spannowsky
- Abstract summary: Invertible Neural Networks (INNs) 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.
We find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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