Quantum Linear

Quantum linear solvers (QLSs) aim to leverage quantum computing to efficiently solve systems of linear equations, a fundamental problem across numerous scientific and engineering disciplines. Current research focuses on developing and improving QLS algorithms, including variational approaches (VQLS) and those based on classical optimization methods like the proximal point algorithm, often hybridized with classical computation to mitigate limitations imposed by factors such as matrix condition number and sparsity. These advancements hold the potential to accelerate applications ranging from machine learning (e.g., enhancing support vector machines and neural network training) to linear programming, offering significant speedups over classical methods for certain problem instances.