Generative Invertible Quantum Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 78 references that are in the map. First posted on arXiv 24 Feb 2023.
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29 of 78 references.
References · 29
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17 more
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.