Circuit Complexity

Circuit complexity studies the resources (size and depth) required to compute functions using Boolean circuits, a fundamental model in computer science. Current research focuses on analyzing the circuit complexity of neural network architectures like transformers and graph neural networks, relating their expressive power to formal language classes and logical formalisms, and investigating the representation complexity of reinforcement learning algorithms. These investigations provide insights into the limitations and capabilities of various computational models, impacting the design of efficient algorithms and the understanding of learnability in machine learning.