Maximum Likelihood Detection Based on Warm-start Quantum Optimization Algorithm
Xinlin He, Han Zeng, Xutao Yu · 2024
Quantum computing, with its powerful parallel computing capabilities, is considered one of the promising candidates for solving complex communication problems. This paper proposes a new detection scheme by combining quantum approximation optimization algorithms (QAOA) with maximum likelihood (ML) detection. The scheme addresses the computational complexity problem faced by ML detection in large-scale multiple-input multiple-output (MIMO) channels. The primary focus lies in the warm-start optimization based on the QAOA-ML detection. We use semi-definite relaxation (SDR) to calculate the temporary solution of ML for initializing quantum circuits. The feasibility of the scheme is verified through simulation. The proposed detection scheme is evaluated and compared with classical computer-based ML detection and Minimum Mean Square Error (MMSE) detection, demonstrating similar bit error rate (BER) performance. The enormous potential of quantum algorithms in computing power solving the drawbacks of traditional ML detection algorithms provides new possibilities for improving detection efficiency.