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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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Generalized Quantum Convolution for Multidimensional Data.

Mingyoung Jeng¹, Alvir Nobel¹, Vinayak Jha¹

¹Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA.

Entropy (Basel, Switzerland)

|November 24, 2023

Summary

This summary is machine-generated.

This study introduces optimized quantum circuits for multidimensional convolution, preserving feature locality and reducing circuit depth for high-dimensional data processing. These advancements are crucial for quantum machine learning applications in areas like remote sensing.

Keywords:

convolution quantum algorithms quantum computing quantum image processing

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Area of Science:

Quantum Computing
Machine Learning
Image Processing

Background:

Convolution operations are essential in digital image processing, convolutional neural networks, and quantum machine learning.
Existing quantum convolution methods struggle with preserving spatial-temporal localities for high-dimensional data.
Deep quantum circuits for unity-stride convolution risk decoherence, limiting practical applications.

Purpose of the Study:

To propose depth-optimized quantum circuits for generalized multidimensional convolution with unity stride.
To address challenges in preserving feature locality for high-dimensional data in quantum computations.
To enable efficient quantum processing of complex datasets like hyperspectral imagery.

Main Methods:

Development of novel, depth-optimized quantum circuits for multidimensional convolution.
Implementation of generalized quantum convolution with unity stride.
Experimental evaluation on a quantum simulator using real-world, high-resolution image data.

Main Results:

Demonstrated applicability of proposed circuits for multidimensional quantum convolution.
Successfully preserved spatial and temporal localities of input features.
Showcased reduced circuit depth compared to existing methods for unity stride operations.

Conclusions:

The proposed depth-optimized circuits offer an efficient solution for multidimensional quantum convolution.
These techniques are suitable for processing high-dimensional data in quantum machine learning, particularly in remote sensing and hyperspectral imaging.
Experimental validation confirms the practical viability on quantum simulators.