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.

翻译：可逆神经网络已成为模拟和生成高复杂度数据的成熟工具。我们提出了一种基于量子门的量子可逆神经网络算法，并将其应用于大型强子对撞机中喷注伴随Z玻色子（衰变为轻子）产生的数据——这是粒子对撞机精密测量的标准烛光过程。我们比较了QINN在不同损失函数和训练场景下的性能。针对该任务，我们发现混合QINN在学习与生成复杂数据方面能够达到规模显著更大的纯经典INN的性能水平。