Quantum Computations and Images Recognition

TL;DR

This paper explores quantum computing principles applied to image recognition, emphasizing quantum parallelism and properties of quantum systems to identify objects in noisy or distorted images.

Contribution

It introduces a quantum-based approach to image recognition that leverages quantum properties without requiring extremely large quantum states.

Findings

Quantum principles can be applied to image recognition tasks.

The approach handles noisy or distorted images effectively.

Quantum methods may offer advantages over classical recognition techniques.

Abstract

The using of quantum parallelism is often connected with consideration of quantum system with huge dimension of space of states. The n-qubit register can be described by complex vector with 2^n components (it belongs to n'th tensor power of qubit spaces). For example, for algorithm of factorization of numbers by quantum computer n can be about a few hundreds for some realistic applications for cryptography. The applications described further are used some other properties of quantum systems and they do not demand such huge number of states. The term "images recognition" is used here for some broad class of problems. For example, we have a set of some objects V_i and function of "likelihood": F(V,W) < F(V,V) = 1 If we have some "noisy" or "distorted" image W, we can say that recognition of W is V_i, if F(W,V_i) is near 1 for some V_i.