Evolution and Applications of Quantum Computing

18Quantum Intelligent Systems and Deep Learning

Bhagaban Swain¹ and Debasis Gountia²*

¹ Department of Computer Science & Engineering, Assam University, Silchar, Assam, India

² Department of Computer Science Application, OUTR University, Bhubaneshwar, Odisha, India

Abstract

Quantum computing can provides better solutions to many complex problems, and thus quantum technology can help to improve machine-learning problems. This chapter discuses about quantum machine learning algorithm for the quantum data like quantum support vector machine (QSVM), quantum principal component analysis (QPCA) and quantum neural network (QNN). For learning from classical data in in the noisy intermediate scale quantum (NISQ) computer, quantum variational classifier with a classical optimizer to train a parameterized quantum circuit is discussed.

Keywords: Quantum machine learning, QPCA, quantum feature map, variational quantum classifier, QNN

18.1 Introduction

Machine Learning is a powerful component in the field of data science. Machine learning algorithms tells how to learn from data and improve the learning accuracy gradually. Machine learning has wide area of applications like in health care, computer security, speech and natural language processing, robotics and machine automation etc. In recent years, both in theory and practice quantum computing has rapidly advanced. Quantum Computing algorithms like database search, Prime factorization, quantum cryptography has a potential advantage as compared ...