Quantum Variational Optimization for Machine Learning

Exponential speedup can be achieved for classically infeasible problems using principles of quantum computing, such as entanglement and superposition. The first algorithm to achieve an exponential speedup was Shor’s algorithm. Object detection is a significant task in computer vision techniques, and it involves a postprocessing algorithm called non-maximum suppression. Using the quantum approach, we aim to overcome the constraint of the greedy algorithm and achieve more accurate and efficient suppression of bounding boxes. We discuss definitions, methodology, challenges, and advantages of using the quantum approach over the classical greedy approach, exploring new possibilities in object detection.