General implementation of quantum physics-informed neural networks

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS Array Pub Date : 2023-07-01 DOI:10.1016/j.array.2023.100287
Shashank Reddy Vadyala , Sai Nethra Betgeri
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引用次数: 1

Abstract

Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions are obtained. Since training modern Machine Learning (ML) systems is a computationally intensive endeavour, using Quantum Computing (QC) in the ML pipeline attracts increasing interest. Indeed, since several Quantum Machine Learning (QML) algorithms have already been implemented on present-day noisy intermediate-scale quantum devices, experts expect that ML on reliable, large-scale quantum computers will soon become a reality. However, after potential benefits from quantum speedup, QML may also entail reliability, trustworthiness, safety, and security risks. To solve the challenges of QML, we combine classical information processing, quantum manipulation, and processing with PINNs to accomplish a hybrid QML model named quantum based PINNs.

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量子物理知情神经网络的一般实现
最近,一种新型的神经网络(NNs)——物理信息神经网络(PINNs)被发现在计算物理中有许多应用。通过对偏微分方程物理规律和过程知识的整合,得到了快速收敛和有效的解。由于训练现代机器学习(ML)系统是一项计算密集型的工作,因此在ML管道中使用量子计算(QC)吸引了越来越多的兴趣。事实上,由于几种量子机器学习(QML)算法已经在当今嘈杂的中等规模量子设备上实现,专家们预计,在可靠的大规模量子计算机上实现量子机器学习将很快成为现实。然而,在量子加速的潜在好处之后,QML也可能带来可靠性、可信度、安全性和安全风险。为了解决QML面临的挑战,我们将经典的信息处理、量子操作和处理与pin n结合起来,实现了一种混合QML模型,称为基于量子的pin n。
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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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