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:反演神经网络(INN)已成为模拟和生成高度复杂数据的成熟工具。我们提出一种用于量子反演神经网络(QINN)的量子门算法,并将其应用于LHC数据的带关联产生Z玻色子的过程,该过程衰变为轻子,该过程是粒子对撞机精密测量的标准过程。我们比较不同损失函数和训练方案下QINN的性能。对于这个任务,我们发现混合QINN在学习和生成复杂数据方面与显着更大的纯经典INN的性能相匹配。