Quantum Machine Learning for Real-World, Large Scale Datasets with Applications in Earth Observation

Abstract

Quantum machine learning is the synergy between quantum computing resources and machine learning methods. In particular, quantum machine learning refers to quantum algorithms promising to compute some machine learning methods and optimization problems (polynomially) faster than conventional algorithms. Quantum algorithms for computing any problems are algorithms using a quantum computer. This work (I) identifies intractable real-world problems of practical significance which can be computed efficiently on a quantum computer, (II) provides an encoding strategy of real-world, large scale problems in a small-scale quantum computer, and (III) invents so-called hybrid classical-quantum (HPC+nQC) learning networks and analyses their performance in comparison to conventional machine (deep) learning methods in order to gain quantum advantage as early and efficiently as possible; here, HPC+nQC is referred to as high performance computing and n quantum computers, where n stands for n different types of quantum computers.