标题： ResQ：一种在模拟 Rydberg 原子量子计算机上实现残差神经网络的新框架 显示英文标题

标题： ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers

摘要： 量子机器学习的研究最近由于量子计算加速机器学习的潜力而迅速增加。 尚未被探索的机器学习领域是基于神经微分方程（神经ODE）的残差神经网络（ResNets），这些网络旨在利用常微分方程的原理来提高神经网络的有效性。 在本工作中，我们提出了关于模拟里德伯原子量子计算机为何特别适合ResNets的见解。 我们还介绍了ResQ，这是一种新的框架，用于优化里德伯原子量子计算机的动力学，以使用模拟量子神经ODE解决机器学习中的分类问题。 显示英文摘要

摘要： Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.