Face Gender Recognition Using Hybrid Quantum Machine Learning

Quantum machine learning (QML) is a research area that combines algorithms from quantum computing and machine learning into one functional system. This poster presents the results of an example of its successful use to recognize the gender of people by their faces. This recognition was performed using a learned hybrid quantum neural network that contains both neural layers implemented by a classical computer and neural layers implemented by the simulator of a quantum computer in which a variational quantum circuit (VQC) is programmed. The most computationally demanding part was teaching this network a training set containing several hundred female and male faces. The learning was done relatively quickly thanks to the powerful NVIDIA CUDA Quantum simulator. This platform allows running simulations on supercomputing nodes with many CPU and GPU cores, which significantly speeds them up compared to other quantum frameworks. The achieved accuracy was approximately 90% for faces on which the network was not trained. This poster also shows examples of various VQCs along with displayed learning curves depending on the simulated depolarization error probability and learning rate.