QML algorithms

This discipline combines classical machine learning with quantum capabilities to produce better solutions. Enhancing ML algorithms and/or classical training with quantum resources broadens the scope of pure ML, as happens with some classical devices such as GPUs or TPUs.

It has been extensively reported that using quantum approaches in learning algorithms could have several advantages (reviewed by Schuld et al., 2018). However, most of the earliest research in this framework chased a decrease in computational complexity in conjunction with a speedup. Current investigations also study methods for quantum techniques to provide unconventional learning representations that could even outperform standard ML in the future.

In recent years, the theories and techniques of QC have evolved rapidly, and the potential benefits for real-world applications have become increasingly evident (Deb et al., 2021; Egger et al., 2021). How QC may affect ML is a key topic of research...