Quantum algorithm boost for automotive image sensors
Volkswagen has developed a quantum-inspired algorithm for automotive image recognition that is more effective than classical machine learning neural networks.
Researchers at VW have developed a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning.
Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters, or hyperparameter, that usually require significant computational time to be adjusted.
This was benchmarked against standard black-box objective functions and showed reduced expected run times and higher accuracy. It was tested in a car image classification task using a full-scale implementation of the hybrid quantum ResNet model with the tensor trained hyperparameter optimization.
The tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.
Other automotive quantum articles
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