Unitary Transformation

Unitary transformations, representing fundamental operations in quantum mechanics and crucial for quantum computing, are being extensively studied for their application in machine learning and signal processing. Current research focuses on developing efficient algorithms, such as generalized approximate message passing and variational quantum circuits, to learn or approximate these transformations from data, often leveraging neural networks and tensor networks to handle high-dimensional Hilbert spaces. These advancements are improving the performance of quantum machine learning models, enabling more accurate simulations of quantum systems, and enhancing classical signal processing techniques like image reconstruction in medical imaging.