Tutorials

Quantum machine learning (QML) is an emerging technique that leverages the principles of quantum mechanics for potential computational speed-ups. It has been shown that certain problems can be solved more efficiently using quantum algorithms over classical approaches. However, the practical utility of these algorithms is limited on the current generation of quantum computers, so-called noisy intermediate-scale quantum (NISQ) systems, as they only offer a limited amount of qubits and are prone to noise and imperfections that strongly limit possible circuits depth and thus the length of quantum computations. Hybrid variational algorithms are considered key candidates for exploiting advantages of near-term quantum devices, but could also be beneficial in post-NISQ systems because of their resource efficiency.

In this tutorial, we discuss how within the class of hybrid variational algorithms, quantum machine learning (QML) has shown promise by moving certain parts of classical machine learning to quantum computers. QCs will, despite common misperceptions, likely be inapt for handling large amounts of data. Consequently, we focus on qantum reinforcement learning (QRL), which requires little training data. However, we also discuss how QML has been shown to outperform classical machine learning for certain tasks, and how this could benefit ML tasks by, for instance, reducing the number of parameters, or in terms of energy efficiency.