2 Dimensional Hilbert

Two-dimensional Hilbert space representations are finding increasing application across diverse fields, primarily focusing on improving efficiency and performance in data processing and machine learning. Current research explores the use of Hilbert curves for efficient data quantization in neural networks, leading to reduced computational costs and improved accuracy in tasks like image processing and reinforcement learning. Furthermore, Hilbert space frameworks are being leveraged to analyze complex data structures, such as images and graphs, revealing underlying patterns and facilitating tasks like causal discovery and statistical inverse problems. These advancements offer significant potential for enhancing the scalability and performance of various algorithms and models.