Learning Image Registration and Quantum Annealing Statistics
Abstract
The advent of commercially available quantum computers has marked the beginning of quantum computing as a reality. Both quantum gate and annealing computers have been released by major computer hardware companies. In this work, the D-Wave 2XTM quantum annealing computer housed at the NASA Advanced Systems computational facility is investigated to accelerate Machine Learning (ML) for image registration.
NASA collects large amounts of images over the globe using space-based monitoring. Image time series of the land surface are generated as the monitoring devices orbit the Earth. The viewing angles of subsequent orbits deviate slightly, and it is necessary to align or register the images precisely to create accurate time series; Unaligned images can lead to substantial analysis errors. These time-series are then used in Earth Systems models such as hydrological, weather, and carbon monitoring models. In this work, we consider the Moderate Resolution Image Spectrometer (MODIS) data collected by NASA's terra satellite. Artificial Neural Networks (ANNs) are a natural fit for ML modeling of images. Several successes have been reported using machine learning related to image processing. We investigate the use of ML to register MODIS images. ANNs are investigated in combination with a Restricted Boltzmann Machine (RBM) as an auto-encoder. We will present results showing the accuracy and efficiency of this approach. The D-Wave 2XTM quantum annealer samples the ground-state wave-function of a spin-Ising system with quadratic interactions between qubits and a chimera connectivity. The system sits in a 15 mK thermal bath. One can think of the system as beginning in the ground-state subject to thermal excitations governed by Boltzmann statistics. If this is assumed true, one can use the statistics from the D-Wave 2XTM to train RBMs. Generating statistics for training Boltzmann machines is an NP-hard problem, and it constitutes the largest compute cost. We investigate the use of the D-Wave 2XTM to accelerate the training of the RBMs in our ANNs and report on the results.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMIN41B..24P
- Keywords:
-
- 3315 Data assimilation;
- ATMOSPHERIC PROCESSESDE: 0428 Carbon cycling;
- BIOGEOSCIENCESDE: 1920 Emerging informatics technologies;
- INFORMATICSDE: 1932 High-performance computing;
- INFORMATICS