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Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
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Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) has emerged to complement existing DL methods. Furthermore, a quantum distributed deep learning (QDDL) technique that combines and maximizes these advantages is getting attention. This paper compares several model structures for QDDL and discusses their possibilities and limitations to leverage QDDL for some representative application scenarios.
Forward citations
Cited by 2 Pith papers
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Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Using variational quantum imaginary time evolution as a training rule can fit toy functions with a KAN-style quantum circuit, but classification performance remains worse than standard approaches.
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The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Adding controlled noise from a simulated time crystal improved fitting accuracy for two quantum neural network variants while degrading quantum reservoir computing, in small numerical tests.
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