Zhang, Juping;
Zheng, Gan;
Koike-Akino, Toshiaki;
Wong, Kai-Kit;
Burton, Fraser;
(2024)
Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization.
IEEE Transactions on Wireless Communications
p. 1.
10.1109/TWC.2024.3442091.
(In press).
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Abstract
This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters; while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices.
| Type: | Article |
|---|---|
| Title: | Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TWC.2024.3442091 |
| Publisher version: | http://dx.doi.org/10.1109/twc.2024.3442091 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Quantum machine learning, parameterized quantum circuit, hybrid quantum and classical neural network, beamforming |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196823 |
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