Journal Article10.48550/arxiv.2310.12389
Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution
Yuhong Huang,Wenxin Li,Chengkang Pan,Shuai Hou,Xian Lu,Chunfeng Cui,Jingwei Wen,Jiaqi Xu,Chongyu Cao,Yin Ma,Hai Wei,Kai Wen +11 more
TL;DR: This work investigates the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable, and shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams.
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Abstract: Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.
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Figures
Fig. 3: Evolution process of the cut value. 
Fig. 1: A schematic illustration of the MBS problem. 
Fig. 2: Illustration of CIM. 
Fig. 5: Variation of efficiency ratio with number of bits. 
Fig. 4: Result found by CIM physical machine for m = 5. 
TABLE I: Comparision of CIM physical machine, SA and Tabu search in objective value and running time.
Citations
Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization
Juping Zhang,Gan Zheng,Toshiaki Koike‐Akino,Kai‐Kit Wong,Fraser Burton +4 more
X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism
Min Soo Kim,Abhishek Kumar Singh,Davide Venturelli,John Kaewell,Kyle Jamieson +4 more
- 28 Feb 2024
TL;DR: X-ResQ is a QA-accelerated MIMO detection system featuring flexible parallelism enabled by Reverse Annealing (RA). It achieves near-optimal throughput for large MIMO systems and significantly outperforms other detectors.
Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking
Fei Zhu,Haifeng Qiu,Ziyu Wang +2 more
- 06 Jun 2024
TL;DR: The proposed pre-training scheme based on QUBO models with quantum-inspired algorithms enhances the adversarial robustness of CVQKD systems, ensuring their security against hacking attempts.
Research on accelerated solution of SVM classification task based on quantum computing
teng zhang,kai li,yijie fu,fei xue +3 more
- 09 May 2025
TL;DR: This study reformulates SVM classification as a QUBO problem to leverage quantum computing, employing simulated annealing to enhance solution process, improving convergence speed, efficiency, and performance in non-linear classification tasks.
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Network of Time-Multiplexed Optical Parametric Oscillators as a Coherent Ising Machine
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