Tensor network methods for quantum-inspired image processing and classical optics

TL;DR

This paper explores how tensor network methods, inspired by quantum mechanics, can improve classical image processing and optics tasks by enabling faster algorithms through efficient data compression and processing.

Contribution

It demonstrates the application of quantum-inspired tensor network techniques to classical image processing and optics, highlighting their potential for speedup and efficiency.

Findings

Tensor network methods enable efficient data compression in image processing.

Quantum-inspired algorithms can accelerate wave-front propagation and optical image formation.

Potential applications include astronomy, earth observation, and microscopy.

Abstract

Tensor network methods strike a middle ground between fully-fledged quantum computing and classical computing, as they take inspiration from quantum systems to significantly speed up certain classical operations. Their strength lies in their compressive power and the wide variety of efficient algorithms that operate within this compressed space. In this work, we focus on applying these methods to fundamental problems in image compression and processing and classical optics such as wave-front propagation and optical image formation, by using directly or indirectly parallels with quantum mechanics and computation. These quantum-inspired methods are expected to yield faster algorithms with applications ranging from astronomy and earth observation to microscopy and classical imaging more broadly.