Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection

Cui, Jingjing; Xiong, Yifeng; Ng, Soon Xin; Hanzo, Lajos

doi:10.1109/tcomm.2022.3185287

Cited by 14 publications

(21 citation statements)

References 56 publications

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“…To complete the channel decoding process by QAOA, the problem needs to be encoded into cost Hamiltonian based on ML function: [18,19,25] argmin…”

Section: Qaoa For Solving Channel Decoding Problemsmentioning

confidence: 99%

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Improved Quantum Approximate Optimization Algorithm for Low‐Density Parity‐Check Channel Decoding

Zeng,

Meng,

Luan

et al. 2024

Adv Quantum Tech

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Quantum computing shows promise for 6G networks due to its parallel computing capabilities. In the context of the Noisy Intermediate‐Scale Quantum era, the introduction of hybrid quantum‐classical algorithms like Quantum Approximate Optimization Algorithm (QAOA) offer powerful solutions to many combinatorial optimization problems in 6G. This paper focuses on Low‐Density Parity‐Check (LDPC) channel decoding and proposes an improved QAOA algorithm assisted by the learning‐to‐learn strategy. We also investigate the parameter concentration phenomenon in QAOA‐based LDPC decoding to assess the rationality. To evaluate effectiveness, a comprehensive numerical expression for the energy expectation of single‐layer QAOA and propose indicators for transfer performance evaluation is provided. Based on simulation results, the similarity in parameter distribution across specific LDPC configurations is investigated. This similarity facilitates the transfer of training outcomes from smaller to larger‐scale problems for optimization initialization, thereby avoiding the need for retraining. This approach offers insights and potential solutions for rapid, large‐scale channel decoding in 6G networks, despite the current limitations of quantum hardware.

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“…To complete the channel decoding process by QAOA, the problem needs to be encoded into cost Hamiltonian based on ML function: [18,19,25] argmin…”

Section: Qaoa For Solving Channel Decoding Problemsmentioning

confidence: 99%

“…To complete the channel decoding process by QAOA, the problem needs to be encoded into cost Hamiltonian based on ML function: [ 18,19,25 ]

\begin{equation} {\operatorname{argmin} \sum _{\mathrm{v}=1}^{\mathrm{N}}{\left(y_{v}-(-1)^{x_{v}}\right)}^{2}} \end{equation}

…”

Section: System Modelmentioning

confidence: 99%

Improved Quantum Approximate Optimization Algorithm for Low‐Density Parity‐Check Channel Decoding

Zeng,

Meng,

Luan

et al. 2024

Adv Quantum Tech

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“…Although, at the time of writing, implementations using quantum hardware are limited by the available capability of the NISQ devices, they hold the promise of reducing computational complexity. The maximum likelihood (ML) detection problem has been solved for smallscale systems using the quantum approximate optimization algorithm [4]. Indeed, the employment of quantum annealing (QA) in ML detection is already a well studied problem [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A Lattice-Reduction Aided Vector Perturbation Precoder Relying on Quantum Annealing

Winter,

Zhang,

Zheng

et al. 2024

IEEE Wireless Commun. Lett.

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Quantum annealing (QA) is proposed for vector perturbation precoding (VPP) in multiple input multiple output (MIMO) communications systems. The mathematical framework of VPP is presented, outlining the problem formulation and the benefits of lattice reduction algorithms. Lattice reduction aided quantum vector perturbation (LRAQVP) is designed by harnessing physical quantum hardware, and the optimization of hardware parameters is discussed. We observe a 5dB gain over lattice reduction zero forcing precoding (LRZFP), which behaves similarly to a quantum annealing algorithm operating without a lattice reduction stage. The proposed algorithm is also shown to approach the performance of a sphere encoder, which exhibits an exponentially escalating complexity.

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“…The computational complexity is shown to be either O(4 q p ) or O(p 2 4 p ). In this article, we extend the algorithm in [5] for SK models which include linear terms in addition to quadratic terms and where the variances of quadratic and linear cost coefficients increase as O(n) and O(n 2 ), respectively, with respect to problem size n. Then, we apply QAOA with the extended SK model on NP-hard optimal maximum likelihood (ML) detection problem [8]. We observe near-optimum performance in extensive simulation studies with promising applications of QAOA in NISQ devices.…”

Section: Introductionmentioning

confidence: 99%

“…QAOA is recently applied for optimal ML detection problem especially for multiple-input-multiple-output (MIMO) communication systems as a low complexity alternative for the optimum ML detector for which the complexity increases exponentially with respect to symbol size n for a general n×n MIMO system with large number of users and transmitters [8]. Massive MIMO systems with optimum ML detection capability are highly promising to increase the performance of next generation communication systems compared with conventional solutions including minimum mean square error (MMSE) detection with low error performance and sphere decoding resulting in high complexity for large systems [9].…”

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Detection With QAOA for Massive MIMO and Sherrington-Kirkpatrick Model With Local Field at Infinite Size

Gülbahar

2024

IEEE Trans. Wireless Commun.

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Quantum-approximate optimization algorithm (QAOA) is promising in Noisy Intermediate-Scale Quantum (NISQ) computers with applications for NP-hard combinatorial optimization problems. It is recently utilized for NP-hard maximum-likelihood (ML) detection problem with fundamental challenges of optimization, simulation and performance analysis for n × n multiple-input multiple output (MIMO) systems with large n. QAOA is recently applied by Farhi et al. on infinite size limit of Sherrington-Kirkpatrick (SK) model with a cost model including only quadratic terms. In this article, we extend application of QAOA on SK model by including also linear terms and then realize SK modeling of massive MIMO ML detection by ensuring independence from specific problem instance and size n while preserving computational complexity of O(16 p ) designed by Farhi et al. We provide both optimized and extrapolated angles for p ∈ [1, 14] and signal-to-noise (SNR) < 12 dB achieving near-optimum ML performance for 25 × 25 MIMO system with p ≥ 4 in extensive simulations where 236500 different QAOA circuits are simulated. We present two conjectures about the concentration properties of QAOA and its near-optimum performance for massive MIMO systems with large sizes covering n < 300 promising significant performance for next generation massive MIMO systems.

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Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection

Cited by 14 publications

References 56 publications

Improved Quantum Approximate Optimization Algorithm for Low‐Density Parity‐Check Channel Decoding

Improved Quantum Approximate Optimization Algorithm for Low‐Density Parity‐Check Channel Decoding

A Lattice-Reduction Aided Vector Perturbation Precoder Relying on Quantum Annealing

Maximum-Likelihood Detection With QAOA for Massive MIMO and Sherrington-Kirkpatrick Model With Local Field at Infinite Size

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