Feed forward QNN
Artificial neural networks, usually just called neural networks, arecomputing systems indefinitely inspired by the biological neural networks and they are extensive in both research and industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed forward neural networks capable of universal quantum computation. This structure takes input as from one layer of qubits and passes that input onto another layer of qubits. This layer of qubits evaluates this information and passes on the output to the next layer. Eventually the path leads to the final layer of qubits.
The layers do not have to be of the same width, meaning they don't have to have the same number of qubits as the layer before or after it. This structure is trained on which path to take similar to classical artificial neural networks.
The proposed project can be summarized by the following points given below:
• The efficient training of the quantum neural network using the fidelity as a cost function, providing both classical and efficient quantum implementations.
• Use of methods that allows for fast optimization with reduced memory requirements.
• Benchmarking our proposal for the quantum task of learning an unknown unitary and find remarkable generalization behavior and a striking robustness to noisy training data.