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IEEE Transactions on Vehicular Technology

Federated Quantum Neural Network with Quantum Teleportation for Resource Optimization in Future Wireless Communication

Bhaskara Narottama, Soo Young Shin

Published Jan 1, 2023• 29 citations
Publisher

Abstract

The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.

DOI
10.1109/tvt.2023.3280459

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The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
Quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.
This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.
In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme
In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme
In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labelling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UF-QGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme.
This paper proposes a quantum neural network (QNN) to address joint optimization in wireless communication. By utilizing the advantages of quantum entanglement and superposition in quantum computing and machine learning, QNN can solve optimization problems with lower complexity than classical neural networks, due to its parallel processing capabilities. Specifically, this study applies QNN to jointly optimize digital precoder-combiner and power allocation in a multi-user multiple-input multiple-output non-orthogonal multiple access (MU MIMO-NOMA) system. The performance of the QNN is analyzed and presented.