Keyword Analysis & Research: qnn
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Quantum neural network - Wikipedia
https://en.wikipedia.org/wiki/Quantum_neural_network
WEBQuantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation were published independently in 1995 by Subhash Kak and Ron Chrisley, [1] [2] engaging with the theory of quantum mind, which posits that quantum effects play a role in cognitive ...
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Training deep quantum neural networks | Nature Communications
https://www.nature.com/articles/s41467-020-14454-2
WEBFeb 10, 2020 · In this paper we meet these challenges and propose a natural quantum perceptron which, when integrated into a quantum neural network (QNN), is capable of carrying out universal quantum...
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Qualcomm Neural Processing SDK - Qualcomm Developer Network
https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk
WEBThe Qualcomm Neural Processing SDK is designed to help developers run one or more neural network models trained in TensorFlow, PyTorch, Keras and ONNX on Qualcomm® platforms, whether that is the CPU, GPU or Qualcomm® Hexagon™ Processor. The Qualcomm Neural Processing SDK provides tools for model conversion and execution …
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Qualcomm Neural Processing SDK for AI Getting Started
https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk/getting-started
WEBSet up the Qualcomm Neural Processing SDK. This step allows the SDK to work with the TensorFlow, ONNX, and TensorFlow Lite frameworks via Python APIs. Follow the steps below to set up the SDK on Ubuntu 20.04: Download the latest version of the SDK. Unpack the SDK’s .zip file to an appropriate location (e.g., ~/snpe-sdk ).
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The power of quantum neural networks | Nature Computational …
https://www.nature.com/articles/s43588-021-00084-1
WEBJun 24, 2021 · The power of quantum neural networks. Amira Abbas, David Sutter, Christa Zoufal, Aurelien Lucchi, Alessio Figalli &. Stefan Woerner. Nature Computational Science 1 , 403–409 ( 2021) Cite this ...
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Quantum Convolutional Neural Network | TensorFlow Quantum
https://www.tensorflow.org/quantum/tutorials/qcnn
WEBMar 23, 2024 · 1.1 Assemble circuits in a TensorFlow graph. TensorFlow Quantum (TFQ) provides layer classes designed for in-graph circuit construction. One example is the tfq.layers.AddCircuit layer that inherits from tf.keras.Layer. This layer can either prepend or append to the input batch of circuits, as shown in the following figure.
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Theory of overparametrization in quantum neural networks
https://www.nature.com/articles/s43588-023-00467-6
WEBJun 26, 2023 · A theoretical framework for quantum neural network (QNN) overparametrization, a phase transition in loss landscape complexity, is established. The precise characterization of the critical number ...
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Quantum Neural Networks: Concepts, Applications, and …
https://arxiv.org/pdf/2108.01468.pdf
WEBThe QNN and quantum reinforcement learning algorithms can be used in various research fields, and this paper considers the applications in terms of communications and networks. In terms of the acceleration of computation in fully distributed platforms, e.g., blockchain [27], [28], QNN can be used. In addition, various advanced communication ...
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MNIST classification | TensorFlow Quantum
https://www.tensorflow.org/quantum/tutorials/mnist
WEBJan 12, 2024 · This tutorial builds a quantum neural network (QNN) to classify a simplified version of MNIST, similar to the approach used in Farhi et al. The performance of the quantum neural network on this classical data problem is compared with a …
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A review of Quantum Neural Networks: Methods, Models, …
https://arxiv.org/pdf/2109.01840
WEBDue to quantum properties, QNN shows higher storage capacity and computational efficiency compared to its classical counterparts. This article will review the development of QNN in the past six years from three parts: implementation methods, quantum circuit models, and difficulties faced.
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