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World Congress on Nature and Biologically Inspired Computing (NaBIC)

Communication via quantum neural networks

Alaa Sagheer, NASSER METWALLY

Published Dec 1, 2010• 8 citations
Publisher

Abstract

In this paper, a quantum teleportation protocol based on quantum neural networks is presented. We studied the relation between the network's weight and the fidelity of transferring information among neurons. It has been found that as the network's weight increases the accuracy of the transformed information increases. Preliminary results of a practical example are given in this paper.

DOI
10.1109/nabic.2010.5716339

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In this paper, a quantum teleportation protocol based on quantum neural networks is presented. We studied the relation between the network's weight and the fidelity of transferring information among neurons. It has been found that as the network's weight increases the accuracy of the transformed information increases. Preliminary results of a practical example are given in this paper.
In this study, the partially entangled neural networks is used to transfer information between two neurons, where the original teleportation protocol is employed this for this purpose. The effect of the network strength on the fidelity of the transported information is investigated. We show that as the strength of the network increases, the accuracy of the transformed information increases. As a practical application, we consider the spread of swine flu virus between two equivalent tranches of the community. In this treatment two factors are considered, one for humanity and the other for influence factor. The likelihood of infection between different age group is investigated, where we show that the strength of the neural network and the degree of infection plays an important role on transferring infection between different age group. From theoretical point of view, we show that it is possible to control the spread of the virus by controlling the network parameter. Also, by using local rotation, one can decrease the rate of infection between the young.
In this paper, a high fidelity scheme of quantum teleportation based on quantum neural network (QNN) is proposed. The QNN is composed of multi-bit control-not gates. The quantum teleportation of a qubit state via two-qubit entangled channels is investigated by solving the master equation in Lindblad operators with a noisy environment. To ensure the security of quantum teleportation, the indirect training of QNN is employed. Only 10% of teleported information is extracted for the training of QNN parameters. Then the outputs are corrected by the other QNN at Bob's side. We build a random series of numbers ranged in [0, π] as inputs and simulate the properties of our teleportation scheme. The results show that the fidelity of quantum teleportation system is significantly improved to approach 1 by the error-correction of QNN. It illustrates that the distortion can be eliminated perfectly and the high fidelity of quantum teleportation could be implemented.
A bidirectional quantum teleportation protocol is introduced over a quantum network consisting of more than four members sharing a coherent entangled state, where it is implemented in a perfect or noisy environment. The results show that the amplitude of the coherent state and the decoherence parameter play important roles in maximizing or minimizing this probability. At fixed values of the amplitude and decoherence parameters, the success of probability increases as the number of users increases. The fidelity of the teleported coherent state depends on the type of encoded information, whether entangled/partial or classical information, namely, it depends on the weight parameter. The stable behavior of the fidelity is displayed when users teleport classical information.
Classical neural networks (CNNs) provide wide applications in communication technology, ranging from enhancement of key performance indicators (KPIs) of communication protocols to utilities in intelligent networks. Quantum neural networks (QNNs) have advantages over CNNs for applications in future communication networks. This article surveys existing methods for implementing QNNs and suggests a novel resource-friendly quantum system with enhanced activation for better performance in QNNs. Our approach is motivated by a principle of neuronal gain control established in neuroscience and was implemented on a quantum computer using IBM's qiskit. Potential implications of such a model for communication technology are discussed.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
This paper investigates the potential of leveraging quantum entanglement to enhance neural network performance and efficiency. We propose a novel neural network architecture incorporating quantum entanglement to facilitate a more efficient and robust information transfer between neurons. The core mechanism centers on exploiting the unique properties of entangled states to minimize computational complexity and improve learning speed. The research explores the feasibility of implementing this architecture within a simulated environment, demonstrating preliminary results suggesting a potential improvement in training speed and accuracy compared to conventional neural networks. The paper concludes with a discussion of future research directions, including exploring the scalability of the quantum entanglement approach and investigating its applicability to diverse neural network tasks.