Pauli channels are ubiquitous in quantum information, both as a dominant noise source in
many computing architectures and as a practical model for analyzing error correction and …

Efficient methods for loading given classical data into quantum circuits are essential for
various quantum algorithms. In this paper, we propose an algorithm called Approximate …

Set function learning has emerged as a crucial area in machine learning, addressing the
challenge of modeling functions that take sets as inputs. Unlike traditional machine learning …

Compressive sensing allows the reconstruction of original signals from a much smaller
number of samples as compared to the Nyquist sampling rate. The effectiveness of …

As quantum computers approach the fault-tolerance threshold, diagnosing and
characterizing the noise on large-scale quantum devices is increasingly important. One of …

Given an n-length input signal x, it is well known that its Discrete Fourier Transform (DFT), X,
can be computed in O (nlogn) complexity using a Fast Fourier Transform. If the spectrum X is …

It is a well-known fact that the Discrete Fourier Transform (DFT) X̅ of an arbitrary n-length
input signal x̅, can be computed from all the n time-domain samples in O (n log n) …

Currently, the FFT is used in different areas, starting from identification of frequency on
mechanical vibration to image enhancement. Real-time computation by interpret the …

We present a novel class of convolutional neural networks (CNNs) for set functions, ie, data
indexed with the powerset of a finite set. The convolutions are derived as linear, shift …

Walsh transform is used in a wide variety of scientific and engineering applications,
including bent functions and cryptanalytic optimization techniques in cryptography. In linear …