Optimal State

Finding optimal states—states that minimize a given objective function—is a central problem across diverse scientific fields, from quantum computing to reinforcement learning. Current research focuses on developing efficient algorithms, such as variational methods employing generative networks and novel policy optimization schemes like those incorporating optimism and local fitting, to identify these optimal states within various model frameworks, including Markov decision processes and quantum systems. These advancements are crucial for improving the performance of quantum algorithms, accelerating reinforcement learning, and enabling more efficient solutions to complex optimization problems in various scientific and engineering domains.