Tensor networks are a computational framework used to efficiently represent and manipulate high-dimensional data by decomposing complex tensors into networks of simpler, interconnected tensors. They are particularly useful in quantum physics and machine learning for handling large-scale systems and reducing computational complexity.

Tensor Networks

Tensor networks

computational framework

High-dimensional data refers to datasets with a large number of variables or features, often leading to challenges in analysis due to the 'curse of dimensionality'. This complexity can result in overfitting, increased computational cost, and difficulties in visualization and interpretation, necessitating advanced techniques for effective analysis.

high-dimensional data

complex tensors

Simpler tensors

interconnected tensors

Quantum physics is the branch of physics that deals with the behavior of matter and energy at the smallest scales, such as atoms and subatomic particles. It fundamentally challenges classical notions of determinism and locality, introducing concepts like wave-particle duality and quantum entanglement that have profound implications for technology and our understanding of reality.

quantum physics

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

large-scale systems

Computational complexity is a branch of computer science that studies the resources required for algorithms to solve problems, focusing on time and space as primary metrics. It categorizes problems based on their inherent difficulty and the efficiency of the best possible algorithms that solve them, providing a framework for understanding what can be computed feasibly.

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