Quantum Machine Learning

9Quantum Computing Meets Machine Learning: A New Frontier

Renuka Devi D.^*, Diana Judith I. and Swetha Margaret T.A.

Department of Computer Science, Stella Maris College (Autonomous), Chennai, India

Abstract

Quantum machine learning (QML) coupled with machine learning to benefit from the computational power transforms into artificial intelligence. In this chapter, the detailed overview of QML is put forward, which covers all the techniques used for the encoding of data into the quantum states that are subsequently used for superposition and entanglement. Some of the quantum machine learning methods are used for the regression, classification, and reinforcement problems. Faster optimization with quantum reinforcement learning (QRL) or hybrid quantum–classical algorithms like variational quantum eigen solver (VQE) or quantum approximate optimization algorithm (QAOA) for more complex optimization tasks. Analyzing the use of quantum annealing for D-Wave systems alongside speedup by Grover’s Search Algorithm and the HHL Algorithm on linear systems, the chapter also covers kernel metrics, quantum efficiency, quantum noise, and scalability and also highlights the requirement of fault tolerance. This chapter has also highlighted some of the advances in this algorithmic domain, wherein QML holds the potential to make AI revolutionary by resolving some of the real-world problems and crossing the boundaries that are at present posed to it.

Keywords: Quantum computing, quantum machine ...