Quantum Neural Network Algorithm Based on the Adaline Model: Training, Optimization, and Application
Sergey M. Gushanskiy, Maxim Polenov, Viktor Potapov
The paper presents the development and research of a supervised quantum neural network algorithm designed to perform image compression and restoration tasks. The proposed technique combines the capabilities of quantum computing with neural network approaches, which ensures the efficient use of quantum parallelism, superposition and entanglement. A mathematical model of a quantum neuron and a learning algorithm based on the Widrow-Hoff method adapted to quantum architecture are described. The quantum frameworks Qiskit and Pennylane are used to build and test the model. The experimental results confirm the ability of the model to achieve a high degree of image compression with minimal loss of quality, as well as to ensure accurate restoration. The data obtained indicate a significant potential of quantum neural networks in the tasks of intelligent processing of visual information.
International Ural Conference on Electrical Power Engineering (UralCon)•Sep 25, 2025•0 citations