Quantum at Scale

Luis de la Peña, Ana Marı́a Cetto, Andrea Valdés-Hernández

Jincheng Zhang

This paper investigates the potential of multi-scale approaches to quantum information representation learning. Traditional quantum information representation methods often operate at a single scale, neglecting the inherent multi-scale nature of quantum data. We propose a framework for learning quantum information representations by decomposing the information into various scales and subsequently learning a distinct feature representation for each scale. These representations are then fused to create a comprehensive multi-scale quantum representation. We hypothesize that this multi-scale approach will enable more effective extraction and representation of key quantum information features, ultimately improving the efficiency of quantum computation. The core claim of this work is that utilizing multi-scale analysis methods can significantly enhance the effectiveness of extracting and representing crucial quantum information characteristics, leading to improved quantum computing performance. The central mechanism involves the decomposition of quantum information into distinct scales, followed by the learning of feature representations for each scale, culminating in the fusion of these representations to establish a robust multi-scale quantum information representation. This research addresses a critical limitation in current quantum information processing by acknowledging and leveraging the multi-scale properties of quantum data.

Kenneth W. Ford

Jincheng Zhang

This paper investigates the potential of multi-scale approaches to quantum information representation learning. Traditional quantum information representation methods often operate at a single scale, neglecting the inherent multi-scale nature of quantum data. We propose a framework for learning quantum information representations by decomposing the information into various scales and subsequently learning a distinct feature representation for each scale. These representations are then fused to create a comprehensive multi-scale quantum representation. We hypothesize that this multi-scale approach will enable more effective extraction and representation of key quantum information features, ultimately improving the efficiency of quantum computation. The core claim of this work is that utilizing multi-scale analysis methods can significantly enhance the effectiveness of extracting and representing crucial quantum information characteristics, leading to improved quantum computing performance. The central mechanism involves the decomposition of quantum information into distinct scales, followed by the learning of feature representations for each scale, culminating in the fusion of these representations to establish a robust multi-scale quantum information representation. This research addresses a critical limitation in current quantum information processing by acknowledging and leveraging the multi-scale properties of quantum data.

Chao Gao, Ri‐Gui Zhou, Xin Li

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2 n 1 × 2 n 2 quantum color image scaling. Firstly, the improved novel quantum representation of color digital images (INCQI) is employed to represent a 2 n 1 × 2 n 2 quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed. Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.

Frederik F. Flöther