用量子注意力机制降低气候模型计算能耗，提升精度。
AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
- 引入量子多头自注意力机制减少参数量
- 相比经典方法参数减少51.51%，收敛性相当
- 适合关注低碳高效气候模拟的研究者
人工智能在应对气候变化中的计算需求日益增长，引发对效率与环境影响的担忧，如杰文斯悖论所示。本文提出一种增强型量子物理信息神经网络（AQ-PINNs），将量子计算技术融入物理信息神经网络（PINNs）以优化气候建模。该方法利用变分量子多头自注意力机制，在保持相似收敛速度和损失值的前提下，使模型参数量相比经典方法减少51.51%。同时采用量子张量网络提升表征能力，实现更高效的梯度计算并降低荒原高原问题风险。该模型为实现更可持续、高效的气候预测提供了关键进展。
原文摘要 · Abstract (English)
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.
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