Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

Abstract

This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.

Quantum Computing and Simulations for Energy Applications: Review and Perspective

“…Integration of quantum computation to enhance sensor performance is another exciting direction with the potential to benefit the energy sector. 298,300 While progress in QIS continues, several challenges exist to its implementation in advancing energy technologies. In addition, a gap exists between the capability of current QIS stakeholders and the needs of the energy sector.…”

“…15,295−298 One emerging application of quantum computational techniques is the optimization of sensing platforms. 299,300 Quantum simulation may also be used to improve the performance of quantum sensing technologies; for example, a quantum simulator has been used to gain new insights into the entanglement between nitrogen vacancy centers in diamond, 301 which is a widely used material for quantum sensing applications. 15,302,303 Additionally, quantum machine learning techniques have shown promise for image classification in remote sensing applications.…”

“…15,302,303 Additionally, quantum machine learning techniques have shown promise for image classification in remote sensing applications. 300,304 Simulating material properties and performance is also crucial for sensor design; for example, complex systems such as MOFs are widely used for the sensitive detection of gases and ions. 305 Thus, the design and optimization of next-generation sensing technologies and materials is an additional area in which quantum computers can significantly benefit the energy sector.…”

“…High-performance sensors are also required throughout the energy sector, for applications such as pipeline integrity, greenhouse gas monitoring, resource discovery, and grid monitoring, among others. ,− One emerging application of quantum computational techniques is the optimization of sensing platforms. , Quantum simulation may also be used to improve the performance of quantum sensing technologies; for example, a quantum simulator has been used to gain new insights into the entanglement between nitrogen vacancy centers in diamond, which is a widely used material for quantum sensing applications. ,, Additionally, quantum machine learning techniques have shown promise for image classification in remote sensing applications. , Simulating material properties and performance is also crucial for sensor design; for example, complex systems such as MOFs are widely used for the sensitive detection of gases and ions . Thus, the design and optimization of next-generation sensing technologies and materials is an additional area in which quantum computers can significantly benefit the energy sector.…”

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

“…Quantum circuit-based neural network classifiers for multi-spectral land cover classification have been introduced in preliminary proofof-concept applications as presented in [23], and an ensemble of support vector machines running on the D-Wave quantum annealer has been proposed for remote sensing image classification in [24]. Finally, in our preliminary work [25], available as Preprint at [26], hybrid quantum-classical neural networks for remote sensing applications are discussed, and a proof-ofconcept for binary classification, using multispectral optical data, is reported.…”