Quantum Machine Learning: A Modern Approach
Editat de S Karthikeyan, M Akila, D. Sumathi, T Poongodien Limba Engleză Hardback – 28 oct 2024
In summary, the book:
- Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines.
- Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks.
- Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting.
- Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost.
- Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.
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Specificații
ISBN-13: 9781032544717
ISBN-10: 1032544716
Pagini: 300
Ilustrații: 172
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
ISBN-10: 1032544716
Pagini: 300
Ilustrații: 172
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
Public țintă
Academic, Postgraduate, and Professional ReferenceCuprins
Part I: Introduction to Statistical & Quantum Learning 1: Fundamentals of Statistics 2: Fundamentals of Quantum Machines Part II: Introduction to Quantum Machine Learning 3: Machine Learning with Supervised Quantum Models 4: Machine Learning with Unsupervised Quantum Models 5: Artificial Neural Networks Part III: Quantum Models 6: Quantum Information Science: Bridging the Gap between the Classical and Quantum Worlds 7: Quantum Machine Learning Approaches 8: Quantum Classification 9: Boosting in QMLPart IV: Quantum Evaluation Models 10: Deep Quantum Learning 11: Ensembles and QBoost 12: Quantum Process Tomography and Regression
Notă biografică
Dr. S. Karthikeyan received his B.E degree in computer science and engineering from Anna University, Chennai, India in the year 2010, his M.E degree in Software engineering from Anna University, Chennai, India in the year 2012. Ph.D. Degree from VIT University, Andhra Pradesh, India in the year Jan 2021. Currently working as an associate professor in computer science and engineering (Artificial intelligence and Machine Learning) at KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. His research interests include artificial intelligence and machine learning, high-performance computing, cloud and big data analytics, and data sciences. Published 40+ papers in reputed journals, 7 book chapters, and 12+ patents. He is Life member in international professional bodies such as ISTE, IAENG, ISRD, IFERP. Also, he is a Senior Member of IEEE.
Dr. M.Akila is the Principal of KPRIET and a professor of CSE. Her main research interest is machine learning with applications to computer vision and data science, with an interest in the efficient implementation of optimization algorithms in engineering problems. She is engaged with organizations such as IET and IEEE and is the additional secretary in the institution of Green Engineers. She has published 3 patents and delivered 32 invited lectures at various institutes. She has been awareded the Kalam 2020 award for service to Green technology and the Cambridge International certificate for teachers and trainers. She is also the recipient of Certificate of Achievement from IGEN, alongside being the reviewer for Neuro computing, IET Biometrics, IET image processing, IET Electronics letters.
Dr. D. Sumathi is currently serving as a Professor Grade 1-SCOPE at VIT-AP University, Andhra Pradesh. She earned her B.E in Computer Science and Engineering from Bharathiar University in 1994 and her M.E in Computer Science and Engineering from Sathyabama University in 2006, Chennai. She completed her doctoral degree at Anna University, Chennai. With a total of 23 years of experience, including 6 years in the industry and 17 years in the teaching field, she holds the additional responsibility of serving as an Assistant Director of the Software Development Cell, which automates various campus upkeep functionalities. Her research interests encompass Cloud Computing, Network Security, Data Mining, Natural Language Processing, Machine Learning, Deep Learning, and Theoretical Foundations of Computer Science. She has published numerous papers in reputed international journals and conferences. Furthermore, she has organized several international conferences, acting as a Technical Chair and tutorial presenter. Dr. D. Sumathi is a life member of ISTE and has published book chapters and edited books with reputed publishers. In addition to this, she holds patents related to the health sector. Currently, she is guiding five research scholars under research areas in biomedical applications.
Prof. T. Poongodi is currently working as a Professor in the Department of Computer Science and Engineering, School of Engineering at Dayanand Sagar University, Bangalore, Karnataka. She received her Ph.D. degree in Information Technology (Information and Communication Engineering) from Anna University, Tamil Nadu, India. Her current research interests include Network Security, Wireless Ad Hoc and Sensor Networks, Internet of Things (IoT), Data Science, and Blockchain Technology for emerging communication networks. She is CISCO, Oracle Academy, and Structured Query Language certified. Prof. T. Poongodi is the author of over 50+ book chapters including some reputed publishers, and 30+ international journals and conferences. She has published 15+ authored/edited books in the areas of Internet of Things, Data Analytics, Blockchain Technology, Artificial Intelligence, Machine Learning, and Healthcare Informatics, published by reputed publishers. She adopts a universal and humanistic approach in her academic and research works. In her research, she has undertaken meticulous scientific studies of emerging issues in networking disciplines. She has 17+ years of academic work experience in teaching and multi-disciplinary research. She received awards namely the Research and Innovation award (2019, 2020, 2021), and Excellence in the area of Research & Innovation/ Academic Excellence / Extension Activities (2018-19, 2019-20) from Galgotias University. Prof. T. Poongodi has also received invitations to address international conferences as a keynote speaker. She is the reviewer for international journals, conferences and she has 5 Indian patents also.
Dr. M.Akila is the Principal of KPRIET and a professor of CSE. Her main research interest is machine learning with applications to computer vision and data science, with an interest in the efficient implementation of optimization algorithms in engineering problems. She is engaged with organizations such as IET and IEEE and is the additional secretary in the institution of Green Engineers. She has published 3 patents and delivered 32 invited lectures at various institutes. She has been awareded the Kalam 2020 award for service to Green technology and the Cambridge International certificate for teachers and trainers. She is also the recipient of Certificate of Achievement from IGEN, alongside being the reviewer for Neuro computing, IET Biometrics, IET image processing, IET Electronics letters.
Dr. D. Sumathi is currently serving as a Professor Grade 1-SCOPE at VIT-AP University, Andhra Pradesh. She earned her B.E in Computer Science and Engineering from Bharathiar University in 1994 and her M.E in Computer Science and Engineering from Sathyabama University in 2006, Chennai. She completed her doctoral degree at Anna University, Chennai. With a total of 23 years of experience, including 6 years in the industry and 17 years in the teaching field, she holds the additional responsibility of serving as an Assistant Director of the Software Development Cell, which automates various campus upkeep functionalities. Her research interests encompass Cloud Computing, Network Security, Data Mining, Natural Language Processing, Machine Learning, Deep Learning, and Theoretical Foundations of Computer Science. She has published numerous papers in reputed international journals and conferences. Furthermore, she has organized several international conferences, acting as a Technical Chair and tutorial presenter. Dr. D. Sumathi is a life member of ISTE and has published book chapters and edited books with reputed publishers. In addition to this, she holds patents related to the health sector. Currently, she is guiding five research scholars under research areas in biomedical applications.
Prof. T. Poongodi is currently working as a Professor in the Department of Computer Science and Engineering, School of Engineering at Dayanand Sagar University, Bangalore, Karnataka. She received her Ph.D. degree in Information Technology (Information and Communication Engineering) from Anna University, Tamil Nadu, India. Her current research interests include Network Security, Wireless Ad Hoc and Sensor Networks, Internet of Things (IoT), Data Science, and Blockchain Technology for emerging communication networks. She is CISCO, Oracle Academy, and Structured Query Language certified. Prof. T. Poongodi is the author of over 50+ book chapters including some reputed publishers, and 30+ international journals and conferences. She has published 15+ authored/edited books in the areas of Internet of Things, Data Analytics, Blockchain Technology, Artificial Intelligence, Machine Learning, and Healthcare Informatics, published by reputed publishers. She adopts a universal and humanistic approach in her academic and research works. In her research, she has undertaken meticulous scientific studies of emerging issues in networking disciplines. She has 17+ years of academic work experience in teaching and multi-disciplinary research. She received awards namely the Research and Innovation award (2019, 2020, 2021), and Excellence in the area of Research & Innovation/ Academic Excellence / Extension Activities (2018-19, 2019-20) from Galgotias University. Prof. T. Poongodi has also received invitations to address international conferences as a keynote speaker. She is the reviewer for international journals, conferences and she has 5 Indian patents also.
Descriere
This text presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming.