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Found 4 papers, 2 papers with code
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack.
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples
Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98. 9% to 99. 2%, 97. 8% to 98. 3%, and 71. 2% to 76. 1%, respectively, but degraded it against GTSRB from 93. 5% to 92. 0%.
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models
This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models.
Quantum-Inspired Machine Learning: a Survey
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks.
