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度复杂数据的成熟工具。我们提出了一种用于量子可逆神经网络的量子门算法，并将其应用于大型强子对撞机中Z玻色子喷注关联产生（衰变至轻子）的数据——这是粒子对撞机精密测量的标准烛光过程。我们比较了不同损失函数和训练场景下量子可逆神经网络的性能。针对这一任务，我们发现混合型量子可逆神经网络在学习和生成复杂数据方面，能够达到规模显著更大的纯经典可逆神经网络相媲美的性能。