Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
Yaswitha Gujju, Atsushi Matsuo, Rudy Raymond
Abstract
This Review focuses on the practical implications of quantum machine learning (QML) algorithms and their applicability in real-world domains such as high-energy physics, healthcare, and finance. Despite rising interest in QML, the field contends with numerous challenges, particularly in execution on real quantum devices. This comprehensive exploration of the field delves into those challenges and the proposed solutions to overcome them. The authors provide an extensive survey of different techniques in QML, from data-encoding methods to model types, and offer insight into open questions in the field from a practical standpoint.
Topics & Concepts
Term (time)Current (fluid)Computer scienceQuantumQuantum machine learningState (computer science)Unsupervised learningArtificial intelligenceMachine learningQuantum computerElectrical engineeringPhysicsQuantum mechanicsAlgorithmEngineeringQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum and electron transport phenomena