👉 Spectral Computing, or Spectral Computing, is a computational paradigm that leverages the properties of signals and their frequency domains to solve complex problems more efficiently than traditional computing methods. This approach transforms data into the frequency domain using techniques like the Fast Fourier Transform (FFT), where operations such as filtering, convolution, and matrix multiplication become significantly faster and more intuitive. By working with the frequency representation of data, Spectral Computing can exploit the inherent parallelism and structure of many problems, making it particularly effective for tasks like signal processing, image analysis, and machine learning. This paradigm is especially advantageous when dealing with large datasets or real-time applications where speed and efficiency are paramount.
