# 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.

- [Original product page](https://best-deals.pro/product/classical-quantum-principal-component-analysis-data-engineering)
- [View offer](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies > Books, Music & Movies > Books & Comics
- Price: 151.60 USD
- Previous price: 205.00 USD
- Discount: 26%
- Price checked: 2026-11-14T05:49:53.523Z

Prices and deal status are the latest recorded data, not a guarantee of availability. Confirm the price and availability with the store.

## 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.

## Pros

- Brings classical PCA and quantum-computing concepts together in one volume.
- The stated scope spans theory, applications, examples, and software implementation guidance.

## Specifications

### Publication details
- Publisher: Wiley-Scrivener
- Publication date: September 8, 2026
- Edition: 1st
- Language: English
- Print length: 368 pages
- ISBN-10: 1394382650
- ISBN-13: 978-1394382651
### Dimensions and installation
- Weight listed: 1.74 pounds

## 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.

## Alternative options

- [Computational Topology for Data Analysis](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- [Network Models for Data Science: Theory, Algorithms, and Applications](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- [Bayesian Machine Learning in Quantitative Finance: Theory and Practical Applications](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- [High Dimensional Data Analysis: An Interpoint Distance Approach](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- [Deep Learning Architectures: A Mathematical Approach \(Springer Series in the Data Sciences\)](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)
- [Information Theory, Inference and Learning Algorithms](https://best-deals.pro/visit/classical-quantum-principal-component-analysis-data-engineering)

## Images

![Classical and Quantum Principal Component Analysis in Data Engineering — 1](https://best-deals.pro/i/n5SHQfaQKkYge4-7B3EJO778YBWK6n1VmzMXI2jBwtXa8qJUqz8qQbtOr_YDaMK5-F3QpwMvMYKEqgjns_hxG4N6IkwOYUpEAnEH2F9793W_7bKVce4D.jpg)