Project Overview
This project is based on the research paper "Quantum Permutation Synchronization". It explores the application of quantum computing techniques, specifically QUBO formulation and QAOA, to solve the problem of permutation synchronization.
Permutation synchronization is a fundamental problem in computer vision and network analysis, where the goal is to find consistent mappings between different sets of objects. This project demonstrates how quantum algorithms can potentially offer advantages over classical approaches for solving these complex combinatorial optimization problems.
Implementation
The project implements both classical and quantum approaches to permutation synchronization, allowing for direct comparison of results and performance. The quantum implementation utilizes the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing techniques.
Key features of the implementation include:
- QUBO formulation of the permutation synchronization problem
- QAOA circuit implementation using Qiskit
- Quantum annealing solver integration
- Performance comparison tools and visualization
- Point matching algorithms for computer vision applications
Results & Analysis
Point matching across images has been accomplished using both QAOA and quantum annealing approaches. The project involved solving the same QUBO formulation using both methods and comparing their effectiveness in terms of solution quality and computational efficiency.
The results demonstrate the potential of quantum algorithms for solving complex optimization problems in computer vision and highlight the trade-offs between different quantum approaches.
