We discuss the applicability of large quantum annealers for the purpose of processing Remote Sensing images. We show an application of currently existing quantum annealers for the purpose of post-processing segmentation of a hyperspectral image. We show that in principle there might exist useful applications of large scale quantum annealers for the purpose of Remote Sensing data processing.

Supervised machine learning techniques are widely used for hyper-spectral images segmentation. A typical simple scheme of classification of such images probabilistically assigns a label to each individual pixel omitting information about pixel surroundings. In order to achieve better classification results for real world images one has to agree the local label obtained from the classifier with the classes of pixel neighborhood. A popular way to do it is through a probabilistic graphical model, where label distributions for individual pixels are mapped into a graph of neighborhood relations. One way to realize this approach is to use Ising models, where class probability is mapped to spin energy and class-class interaction is mapped to the spins coupling. By finding low energy states of such an Ising model we can perform post-processing of segmented images. In this work we present how this postprocessing can be implemented using a quantum annealer.

This article describes a practical Earth Observation use case that would benefit from quantum computing. We analyze three quantum neural network algorithms. We implemented one of the algorithms on the EuroSAT dataset. We compare the algorithms with respect to complexity and degree of quantization. We believe that the algorithms we propose would be useful for the remote sensing community when quantum computing technologies become widely available.1

Probabilistic quantum error correction is an error-correcting procedure which uses postselection to determine if the encoded information was successfully restored. In this work, we analyze the probabilistic version of the error-correcting procedure for general noise. We generalize the Knill-Laflamme conditions for probabilistically correctable errors. We show that for some noise channels, the initial information has to be encoded into a mixed state to maximize the probability of successful error correction. Finally, the probabilistic error-correcting procedure offers an advantage over the deterministic procedure. Reducing the probability of successful error correction allows for correcting errors generated by a broader class of noise channels. Significantly, if the errors are caused by a unitary interaction with an auxiliary qubit system, we can probabilistically restore a qubit state by using only one additional physical qubit.