Quantum Machine Learning Compiler for Hybrid Quantum-Classical Models

The proposed work aims at designing a composite classical-quantum machine learning architecture for binary classification using VQCs. The goal is to use the power of quantum computing to enhance and improve the efficiency of classical machine learning techniques. This is done through the development of a customized compiler that maps the high-level quantum code to specific quantum circuits that are then incorporated with conventional Neural Networks into what can be termed as Quantum Machine Learning (QML) model. Goaded by the likelihood of quantum computation to enhance computational precision and speed, this proposed work presents an outline of the theoretical quantum circuits, architecture and development of this special compiler and linking of both atomic and classical models. A proof of concept is done using the Breast Cancer Wisconsin dataset to compare the capabilities of the suggested model with a more profound understanding of the strengths and weaknesses of this type of hybrid strategy.