Quantum Machine Learning Using Quantum Illumination With Quantum Enhanced Interference

TL;DR

This paper explores how quantum illumination and interference can enhance quantum machine learning by analyzing superposition states and constructing quantum neural networks for improved quantum optimization and search algorithms.

Contribution

It introduces a quantum-enhanced technique for analyzing superposition states and develops a quantum neural network back propagation method for quantum machine learning.

Findings

Quantum interference diffraction patterns can be analyzed using quantum illumination.

A quantum neural network back propagation technique is constructed.

Enhanced analysis of superposition states improves quantum optimization.

Abstract

Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques in a hybrid framework that can give faster, exponential, more efficient power of quantum computing with the data driven intelligence. Quantum illumination(QI) is the quantum mechanical technique along with analysis of light matter interaction from source to detection end that connects quantum principle to hardware implementation. Superposition and entanglement control are deeply needed for the information-qubit processing in quantum computing. Improvement of measurement and performance are directly linked to detecting weak signal or intensity. This paper motivated that using quantum-enhanced technique how we can analysis previous superposition of qubit state which can clearly analyzed quantum interference diffraction patterns and its superposition using double slit experiment. Then constructed quantum neural network back propagation technique such that can give information of qubit position in any previous superposition state. Which is very import for any quantum optimization and search algorithm.