Quantum Neural Nets

Davide Pastorello

Ammar Daskin

In this paper, we propose a simple neural net that requires only O(n log_2k) number of qubits and O(nk) quantum gates: Here, N is the number of input parameters, and k is the number of weights applied to these parameters in the proposed neural net. We describe the network in terms of a quantum circuit, and then draw its equivalent classical neural net which involves O(k^n) nodes in the hidden layer. Then, we show that the network uses a periodic activation function of cosine values of the linear combinations of the inputs and weights. The backpropagation is described through the gradient descent, and then iris and breast cancer datasets are used for the simulations. The numerical results indicate the network can be used in machine learning problems and it may provide exponential speedup over the same structured classical neural net.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more

Ankit Sarkar, Minakshi Banerjee, Mahua Nandy Pal

In the field of retinal disease identification, deep neural networks are exhaustively used. But the efficiency of quantum neural network in the field is not yet explored. Recently, quantum neural network achieved attention of researchers as it is required to explore if quantum network has any scope in the relevant field in terms of resource utilisation and decision-making during network learning. In this paper, efficiency of a simple quantum network model is experimented. In the present scenario, quantum classical models are unable to handle more than few qubits. Experimentally, it is found that the quantum neural network is quite efficient in representing the features of exudate affected retinal image patches. The accuracy of quantum neural net model is 84.28%. The accuracies are 51.80% and 88% respectively with comparable deep neural net and convolutional neural net models.

Davide Pastorello

Wiesław Sieńko

This paper presents some conjectures on modeling the quantum systems using lossless-orthogonal neural nets. Structure of these nets consists of n compatibly connected (entangled) pairs of neurons-qubits.