Gate Based Quantum

Gate-based quantum computing is exploring its potential to accelerate various computationally intensive tasks, particularly in machine learning and optimization problems. Current research focuses on developing and optimizing quantum algorithms for tasks like neural network training, robust fitting in computer vision, and solving mixed-integer linear programs, often employing variational quantum algorithms and parametrized quantum circuits. These efforts aim to leverage the unique capabilities of quantum computers to surpass classical methods in efficiency and solution quality, with applications ranging from improved image processing to more efficient machine learning model training. The ultimate goal is to demonstrate a practical quantum advantage in these domains using near-term noisy intermediate-scale quantum (NISQ) devices.