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Article

Quantum Physics-Informed Neural Networks

U.S. Army Engineer Research and Development Center, Information and Technology Laboratory, 3909 Halls Ferry Rd., Vicksburg, MS 39180, USA
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Entropy 2024, 26(8), 649; https://doi.org/10.3390/e26080649
Submission received: 25 April 2024 / Revised: 15 July 2024 / Accepted: 23 July 2024 / Published: 30 July 2024

Abstract

In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can increase model accuracy with less total network parameters for noiseless models.
Keywords: quantum computing; quantum variational algorithm; quantum machine learning; physics informed neural networks; quantum data-derived methods; quantum algorithms quantum computing; quantum variational algorithm; quantum machine learning; physics informed neural networks; quantum data-derived methods; quantum algorithms

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MDPI and ACS Style

Trahan, C.; Loveland, M.; Dent, S. Quantum Physics-Informed Neural Networks. Entropy 2024, 26, 649. https://doi.org/10.3390/e26080649

AMA Style

Trahan C, Loveland M, Dent S. Quantum Physics-Informed Neural Networks. Entropy. 2024; 26(8):649. https://doi.org/10.3390/e26080649

Chicago/Turabian Style

Trahan, Corey, Mark Loveland, and Samuel Dent. 2024. "Quantum Physics-Informed Neural Networks" Entropy 26, no. 8: 649. https://doi.org/10.3390/e26080649

APA Style

Trahan, C., Loveland, M., & Dent, S. (2024). Quantum Physics-Informed Neural Networks. Entropy, 26(8), 649. https://doi.org/10.3390/e26080649

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