Quantum-Native Maximum Likelihood Detection in Random Access Channel with Overloaded MIMO

TL;DR

This paper introduces a quantum-native maximum likelihood detection method for overloaded MIMO systems in random access channels, leveraging Grover search to achieve optimal detection with reduced computational complexity.

Contribution

It formulates MLD as a binary optimization problem solved via Grover adaptive search, incorporating search space reduction and parameter optimization for efficient quantum detection.

Findings

Achieves optimal detection performance in overloaded MIMO scenarios.

Reduces Grover rotation count by up to 65% compared to conventional GAS.

Demonstrates potential for quantum-accelerated wireless systems.

Abstract

In this paper, we propose a quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (MIMO) systems in a random access channel, where numerous user terminals share the same channel resource and asynchronously transmit signals. Classical linear detectors suffer from significant performance degradation in this scenario, whereas the exhaustive-search MLD achieves the optimal performance but incurs an exponential computational complexity. To overcome this trade-off, we formulate the MLD as a binary optimization problem and solve it via Grover adaptive search (GAS) -- a quantum exhaustive search algorithm offering quadratic speedup in fault-tolerant quantum computing. We then introduce a search space reduction technique to substantially decrease the required computational resources. In addition, we investigate efficient parameter settings for GAS through probability analysis to improve convergence performance. We demonstrate that the proposed detector achieves the optimal detection performance while reducing the required Grover rotation count to reach the solution by up to approximately 65% compared with the conventional GAS, showing its potential as a viable solution for future quantum-accelerated wireless systems.