This repository contains the code for a hybrid quantum-classical model for job resume matching. The model combines classical text preprocessing (using TF-IDF vectorization) with a quantum circuit that performs feature mapping to predict the match between job descriptions and resumes.
The quantum-assisted resume matching model works by first converting job descriptions and resumes into numerical feature vectors using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization. These vectors are then processed using a quantum circuit for prediction.
The quantum circuit is built using PennyLane, a Python library for quantum machine learning. The model leverages angle embedding for feature mapping and a strongly entangling layer for quantum operations.
Install the required packages using pip:
!pip install pennylane