This article presents a study on the applicability and feasibility of quantum-inspired tensor network algorithms and techniques for industrial environments and use cases. It provides a comprehensive literature review and analysis of the use cases that can be impacted by these methods, as well as explores the limitations of these techniques to determine their potential scalability.
The key highlights and insights are:
Tensor networks are a class of quantum-inspired algorithms and techniques that can imitate the tensor operations performed by a quantum computer, but execute them on classical computers. They can optimize the execution of these operations, especially in cases where the full quantum state vector is not required, but only properties of it.
Tensor networks can represent certain families of quantum states efficiently, such as matrix product states (MPS) and projected entangled pair states (PEPS). These representations have also been relevant for the world of machine learning, as they can compress models while reducing the required memory without significant loss of precision.
The article explores the main use cases of tensor networks for industrial scenarios, including finance (portfolio optimization, interpretable predictions), medicine (drug discovery, medical image analysis), quantum materials simulation, battery simulation, and optimization problems (routing, task assignment, manufacturing sequencing).
Tensor networks offer advantages in handling high-dimensional problems, compressing data, and performing large-scale operations efficiently compared to classical approaches. However, they also have limitations, especially for NP-hard problems, where the memory and time requirements can still scale exponentially.
The article serves as an introductory guide to the field of tensor networks and their potential applications in industrial contexts, highlighting the strengths, weaknesses, and trade-offs of these quantum-inspired techniques.
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