Variational Quantum Machine Learning

Variational quantum machine learning (VQML) aims to leverage quantum computers for machine learning tasks by training parameterized quantum circuits (analogous to neural networks) to solve problems. Current research focuses on improving the trainability and efficiency of these circuits, exploring architectures like hardware-efficient ansatzes and tensor networks, and incorporating symmetries to enhance performance. This field is significant because it explores the potential of quantum computation for tackling computationally challenging machine learning problems, with applications ranging from materials science (e.g., force field generation) to solving complex equations like the Navier-Stokes equations.