Tensor networks and tensor algebra

In summary, for literature recommendations regarding tensor networks, consider the following books: Tensor Networks: From Mathematics to Applications by Stefan E. Schiefer, An Introduction to Tensor Networks by M.M. Wolf, Quantum Many-Body Systems in Condensed Matter Physics by G. Vidal, Tensor Network Theory by Guifré Vidal, and Entanglement in Quantum Information Theory by John Watrous. These books provide comprehensive overviews of tensor networks and their applications, as well as covering relevant linear algebra topics such as singular value decomposition and spectral decomposition.
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I'm looking for literature recommendations regarding tensor networks. I never came across singular value decomposition or spectral decomposition in my linear algebra classes, so I need to brush up on the relevant mathematical background as well.
 
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For literature recommendations regarding tensor networks, consider the following books:1. Tensor Networks: From Mathematics to Applications, by Stefan E. Schiefer. This book provides a comprehensive overview of tensor networks and their applications in physics, computer science, and mathematics. It also covers topics such as spectral decomposition and singular value decomposition. 2. An Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States, by M.M. Wolf. This book provides an introduction to various aspects of tensor networks, including their mathematical background. It also discusses the relevant linear algebra topics such as singular value decomposition and spectral decomposition. 3. Quantum Many-Body Systems in Condensed Matter Physics: From Basics to Real-World Applications, by G. Vidal. This book provides a comprehensive overview of quantum many-body systems and their applications in condensed matter physics. It also covers topics such as tensor networks and their applications, as well as relevant linear algebra topics such as singular value decomposition and spectral decomposition. 4. Tensor Network Theory, by Guifré Vidal. This book provides an in-depth look at tensor networks and their applications. It also covers topics such as singular value decomposition and spectral decomposition. 5. Entanglement in Quantum Information Theory, by John Watrous. This book provides an introduction to entanglement in quantum information theory. It also covers topics such as tensor networks and their applications, as well as relevant linear algebra topics such as singular value decomposition and spectral decomposition.
 

FAQ: Tensor networks and tensor algebra

What are tensor networks and why are they important in scientific research?

Tensor networks are a mathematical framework used to represent and manipulate multidimensional data. They are important in scientific research because they allow for efficient and accurate analysis of complex systems, such as quantum states in physics or neural networks in machine learning.

How is tensor algebra used in tensor networks?

Tensor algebra is used in tensor networks to perform operations on tensors, such as contraction, multiplication, and decomposition. These operations allow for the manipulation and analysis of large and complex data sets.

Can tensor networks be applied to fields outside of physics and mathematics?

Yes, tensor networks have applications in various fields, including computer science, chemistry, and engineering. They can be used to analyze and model complex systems in these fields.

What are some advantages of using tensor networks over other data analysis methods?

Tensor networks offer several advantages, including efficient representation and manipulation of high-dimensional data, ability to capture complex relationships between data points, and scalability for large datasets.

Are there any limitations to using tensor networks?

One limitation of tensor networks is the difficulty in visualizing and interpreting the results, as they often involve high-dimensional data. Additionally, the computational complexity of certain tensor operations can be a challenge for large datasets.

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