Adiabatic Quantum

Adiabatic quantum computing (AQC) leverages the principles of adiabatic evolution to solve complex optimization problems, finding applications in machine learning and other computationally intensive fields. Current research focuses on developing and applying AQC algorithms for training various neural network architectures, including support vector machines and binary neural networks, often formulated as Quadratic Unconstrained Binary Optimization problems. This approach shows promise in accelerating training speeds and improving efficiency compared to classical methods, particularly for large datasets and rapid retraining scenarios, with demonstrated successes in image reconstruction and motion segmentation. The potential impact lies in significantly reducing the computational cost of complex optimization tasks across diverse scientific and engineering domains.