Quantum-Inspired Tensor Network for Earth Science

Otgonbaatar, Soronzonbold und Kranzlmüller, Dieter (2023): Quantum-Inspired Tensor Network for Earth Science. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 16-21 July 2023. Sidharth, Misra und Shannon, Brown (eds.) : In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium : proceedings : 16-21 July, 2023, Pasadena, California, USA (2023), [Piscataway, NJ]: IEEE.

Abstract

Deep Learning (DL) is one of many successful methodologies to extract informative patterns and insights from ever increasing noisy large-scale datasets (in our case, satellite images). However, DL models consist of a few thousand to millions of training parameters, and these training parameters require tremendous amount of electrical power for extracting informative patterns from noisy large-scale datasets (e.g., computationally expensive). Hence, we employ a quantum-inspired tensor network for compressing trainable parameters of physics-informed neural networks (PINNs) in Earth science. PINNs are DL models penalized by enforcing the law of physics; in particular, the law of physics is embedded in DL models. In addition, we apply tensor decomposition to HyperSpectral Images (HSIs) to improve their spectral resolution. A quantum-inspired tensor network is also the native formulation to efficiently represent and train quantum machine learning models on big datasets on GPU tensor cores. Furthermore, the key contribution of this paper is twofold: (I) we reduced a number of trainable parameters of PINNs by using a quantum-inspired tensor network, and (II) we improved the spectral resolution of remotely-sensed images by employing tensor decomposition. As a benchmark PDE, we solved Burger’s equation. As practical satellite data, we employed HSIs of Indian Pine, USA and of Pavia University, Italy.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 979-8-3503-2010-7 ; 979-8-3503-2009-1 ; 979-8-3503-3174-5
• Place of Publication: [Piscataway, NJ]
• Language: English