AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling

TL;DR

AQ-PINNs integrate quantum computing with physics-informed neural networks to improve climate modeling accuracy while significantly reducing computational resources and carbon footprint.

Contribution

This paper introduces AQ-PINNs, a novel quantum-enhanced neural network model that reduces parameters and enhances efficiency in climate-related fluid dynamics modeling.

Findings

51.51% reduction in model parameters

Maintains comparable convergence and loss to classical models

Uses quantum tensor networks for improved efficiency

Abstract

The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by the Jevons paradox. We propose an attention-enhanced quantum physics-informed neural networks model (AQ-PINNs) to tackle these challenges. This approach integrates quantum computing techniques into physics-informed neural networks (PINNs) for climate modeling, aiming to enhance predictive accuracy in fluid dynamics governed by the Navier-Stokes equations while reducing the computational burden and carbon footprint. By harnessing variational quantum multi-head self-attention mechanisms, our AQ-PINNs achieve a 51.51% reduction in model parameters compared to classical multi-head self-attention methods while maintaining comparable convergence and loss. It also employs quantum tensor networks to enhance representational capacity, which can lead to more efficient gradient computations and reduced susceptibility to barren plateaus. Our AQ-PINNs represent a crucial step towards more sustainable and effective climate modeling solutions.