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Modern Physics Letters B

High fidelity quantum teleportation assistance with quantum neural network

Chun-Hui Huang, Bichun Wu

Published Sep 20, 2014• 4 citations
Publisher

Abstract

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.

DOI
10.1142/s0217984914501899

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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.
Abstract Quantum teleportation as the key strategy for quantum communication requires pure maximally shared entangled states among quantum nodes. In practice, quantum decoherence drastically degrades the shared entanglement during entanglement distribution, which is a serious challenge for the development of quantum networks. However, most of the decoherence control strategies proposed thus far are either resource-intensive or time-consuming. To overcome this obstacle, we enable noise-resistant teleportation through a noisy channel with a limited number of qubits and without applying time-consuming weak measurements. We apply a quantum gate control unit consisting of a controlled NOT gate and a rotation gate after the original teleportation protocol is accomplished. Furthermore, we demonstrate that a teleportation fidelity of unity is attainable when environment-assisted measurement is added to the proposed teleportation protocol via quantum gates. Moreover, we present an entanglement distribution process by employing the designed quantum gate control unit followed by the deterministic standard teleportation protocol to improve teleportation fidelity by establishing improved shared entanglement. Our performance analysis indicates that the proposed teleportation schemes offer a competitive fidelity and success probability compared with the conventional schemes and a recent weak measurement-based teleportation protocol.
In this paper, a novel continuous variable quantum teleportation (CVQTS) scheme based on quantum neural network (QNN) is proposed to implement the high-efficient and communication security. To achieve the teleportation in two-dimensional Hilbert space, the continuous variable quantum states are split into N modes by an array of N − 1 beam splitters (N-splitter) in the continuous variable quantum teleportation channel (CVQTC). The QNN is applied to trace and restore the distortion signals. It used QNN training indirectly to obtain the weight parameters. In order to ensure the communication security, only a small number of information is extracted as training expectation. The results demonstrate that our scheme is capable of enhancing the fidelity close to 1 for almost all teleported information. Due to the simple structure of QNN, CVQTS scheme based on QNN can be applied to any other inputs and improves the maneuverability and realizability in the experiment.
We investigate quantum teleportation through noisy quantum channels by solving analytically and numerically a master equation in the Lindblad form. We calculate the fidelity as a function of decoherence rates and angles of a state to be teleported. It is found that the average fidelity and the range of states to be accurately teleported depend on types of noises acting on quantum channels. If the quantum channels are subject to isotropic noise, the average fidelity decays to 1/2,1/2,1/2, which is smaller than the best possible value of 2/32/32/3 obtained only by the classical communication. On the other hand, if the noisy quantum channel is modeled by a single Lindblad operator, the average fidelity is always greater than 2/3.2/3.2/3.
Quantum teleportation is a quantum communication protocol that has been extensively studied both theoretically and experimentally. The proposed approach of using the LOCCNet framework to learn quantum teleportation protocols is investigated in this study. In the proposed protocols consider various training scenarios, including controlling gate parameters between shared entangled states, and recovery operations. To optimize the training, the parameterized quantum circuits (PQC) with training parameters is used in the LOCCNet framework, and the results show that a high-fidelity quantum teleportation protocol can be achieved even under non-ideal channels. Moreover, simulations are conducted to evaluate the effect of quantum channel noise on teleportation performance by varying the noise of entangled states and entanglement parameters under adjustable operations. The findings suggest that the trained PQC can improve system performance and potentially offer denoising capabilities. Compared with the existing LOCCNet protocol, the newly designed LOCCNet protocol has better noise immunity and can improve the fidelity and transmission success rate in specific scenarios.
Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To investigate the reliability of quantum neural networks, this paper proposes a methodology wherein operator measurements are performed on the final states of the output quantum states of a quantum neural network. The proximity of these measurements to the target value is compared, and the fidelity value, combined with a quantum gate operation, is utilized to assess the reliability of the quantum neural network. Through network training, the results demonstrate that, under optimal parameters, both the fidelity of the final state measurement value and the target value of the model approach are approximately equal to 1. It indicates that training mitigates the errors stemming from encoding into the initial quantum state, thereby resulting in enhanced system reliability and accuracy.