Quantum Computing Approaches for High-Speed Visual Search

This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need $100-150 ~\text{ms}$. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.