Classical and Quantum Principal Component Analysis in Data Engineering
Classical and Quantum Principal Component Analysis in Data Engineering is a technical book about PCA for large, high-dimensional datasets and how quantum methods may relate to classical approaches. Its description covers quantum algorithms, matrix decomposition, practical software guidance, and applications including machine learning, bioinformatics, finance, encryption, and quantum chemistry.
At a glance
- Discusses PCA for high-dimensional and large-scale datasets, including classical and quantum approaches.
- Describes quantum information theory, quantum algorithms, and matrix-decomposition methods.
- The listing says it includes case studies, hands-on examples, and guidance on modern software tools and libraries.
- Applications named in the listing include machine learning, bioinformatics, finance, quantum machine learning, encryption, and quantum chemistry.
Reference price: $205.00
Price checked: Nov 14, 2026
View offerAhead of Black FridayExplore the selectionPros and cons
Pros
- Brings classical PCA and quantum-computing concepts together in one volume.
- The stated scope spans theory, applications, examples, and software implementation guidance.
Technical specifications
8 specifications| Publisher | Wiley-Scrivener |
|---|---|
| Publication date | September 8, 2026 |
| Edition | 1st |
| Language | English |
| Print length | 368 pages |
| ISBN-10 | 1394382650 |
| ISBN-13 | 978-1394382651 |
| Weight listed | 1.74 pounds |
|---|
Who is it for?
A good fit for
- Researchers and students exploring principal component analysis and quantum computing.
- Data science or engineering professionals seeking an overview of PCA applications for large, high-dimensional datasets.
Less suited to
- Readers seeking a narrowly focused introductory guide to classical PCA only.
- Buyers who need confirmed details about the specific software libraries or depth of implementation before purchasing.
Frequently asked questions
What topics does the book cover?
The listing describes classical and quantum PCA, quantum information theory, quantum algorithms, matrix decomposition, and applications across areas such as machine learning, bioinformatics, and finance.
Does it include practical material?
The listing claims real-world case studies, hands-on examples, and guidance on implementing PCA with modern software tools and libraries. Specific tools are not named.
How long is the book?
The listed print length is 368 pages.
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