首页|Researcher from University of Kansas Describes Findings in Machine Learning (Optimizing Multidimensional Pooling for Variational Quantum Algorithms)
Researcher from University of Kansas Describes Findings in Machine Learning (Optimizing Multidimensional Pooling for Variational Quantum Algorithms)
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New research on artificial intelligence is the subject of a new report. According to news reporting from Lawrence, Kansas, by NewsRx journalists, research stated, “Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision.” The news journalists obtained a quote from the research from University of Kansas: “Data pooling, a major component of CNNs, plays a crucial role in extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data to 2-D image data to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods.”
University of KansasLawrenceKansasUnited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging TechnologiesMachine LearningQuantum Algorithm
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