Open AccessSurvey of NISQ Era Hybrid Quantum-Classical Machine Learning Research
Author(s) -
Gennaro De Luca
Publication year - 2021
Publication title -
journal of artificial intelligence and technology
Language(s) - English
Resource type - Journals
ISSN - 2766-8649
DOI - 10.37965/jait.2021.12002
Subject(s) - quantum computer , computer science , quantum machine learning , qubit , quantum , quantum information , field (mathematics) , theoretical computer science , computer engineering , computational science , artificial intelligence , mathematics , physics , quantum mechanics , pure mathematics
Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia. Several physical quantum computers are now freely available to use through cloud services, with some implementations supporting upwards of hundreds of qubits. These advances mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, paving the way for hybrid quantum-classical systems. This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to hybrid quantum-classical machine learning.