论文标题
关于量子计算机的物理信息神经网络
On Physics-Informed Neural Networks for Quantum Computers
论文作者
论文摘要
物理知识的神经网络（PINN）成为解决科学计算问题的强大工具，范围从偏微分方程的解决方案到数据同化任务。使用PINN的优点之一是利用依靠CPU和诸如ACCELERATORS等CPU和协同处理器的合并使用的机器学习计算框架的使用来实现最高性能。这项工作使用量子处理单元（QPU）协作者研究了PINN的设计，实现和性能。我们设计了一个简单的量子PINN，以使用连续变量（CV）量子计算框架解决一维泊松问题。我们讨论了不同优化器，PINN残留公式和量子神经网络深度对量子PINN精度的影响。我们表明，在量子PINN的情况下，训练景观的优化器探索不像经典Pinn中的有效性，而基本的随机梯度下降（SGD）优化器的表现优于自适应和高阶优化器。最后，我们强调了量子和经典Pinn之间方法和算法的差异，并概述了量子Pinn开发的未来研究挑战。
Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks. One of the advantages of using PINN is to leverage the usage of Machine Learning computational frameworks relying on the combined usage of CPUs and co-processors, such as accelerators, to achieve maximum performance. This work investigates the design, implementation, and performance of PINNs, using the Quantum Processing Unit (QPU) co-processor. We design a simple Quantum PINN to solve the one-dimensional Poisson problem using a Continuous Variable (CV) quantum computing framework. We discuss the impact of different optimizers, PINN residual formulation, and quantum neural network depth on the quantum PINN accuracy. We show that the optimizer exploration of the training landscape in the case of quantum PINN is not as effective as in classical PINN, and basic Stochastic Gradient Descent (SGD) optimizers outperform adaptive and high-order optimizers. Finally, we highlight the difference in methods and algorithms between quantum and classical PINNs and outline future research challenges for quantum PINN development.
