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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum Physics-Informed Neural Networks.

Corey Trahan1, Mark Loveland1, Samuel Dent1

  • 1U.S. Army Engineer Research and Development Center, Information and Technology Laboratory, 3909 Halls Ferry Rd., Vicksburg, MS 39180, USA.

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Summary
This summary is machine-generated.

Quantum and hybrid physics-informed neural networks (PINNs) show promise for solving partial differential equations. Quantum PINNs can achieve comparable accuracy with fewer parameters than classical models.

Keywords:
physics informed neural networksquantum algorithmsquantum computingquantum data-derived methodsquantum machine learningquantum variational algorithm

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Area of Science:

  • Computational Physics
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Physics-informed neural networks (PINNs) integrate physical laws into neural network training.
  • Investigating quantum and hybrid approaches for PINNs is an emerging area.

Purpose of the Study:

  • To explore the efficacy of quantum and hybrid, quantum/classical PINNs for solving partial differential equations (PDEs).
  • To compare the expressibility and performance of quantum, hybrid, and classical neural networks.

Main Methods:

  • Utilized the PennyLane quantum device simulator.
  • Investigated quantum and hybrid PINNs for transient and steady-state, 1D and 2D PDEs.
  • Analyzed comparative expressibility and explored hybrid configurations.

Main Results:

  • Quantum PINNs demonstrated comparable accuracy to classical PINNs with fewer parameters in certain applications.
  • Incorporating quantum nodes into classical PINNs improved model accuracy with reduced parameters for noiseless scenarios.

Conclusions:

  • Quantum and hybrid PINNs offer a potentially more efficient alternative to classical PINNs for solving PDEs.
  • Hybrid quantum-classical approaches show significant potential for enhancing neural network performance in physics-informed modeling.