ABSTRACT The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a function's evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUD's computational complexity is significantly lower. INDEX TERMS Bit-interleaved coded modulation, computational complexity, EXIT chart, Grover's quantum search algorithm, BBHT quantum search algorithm, Dürr-Høyer algorithm, iterative decoding, multiuser detection, quantum amplitude amplification, quantum amplitude estimation, quantum computation, quantum entanglement, quantum mean algorithm.
To elaborate further, Botsinis et al. REF have introduced an extension of the QMA algorithm, namely the Quantum Weighted Sum Algorithm (QWSA), which was employed for Soft-Input Soft-Output Multi-User Detection (SISO-MUD) in the context of Code Division Multiple Access (CDMA) systems.
Quantum Search Algorithms, Quantum Wireless, and a Low-Complexity Maximum Likelihood Iterative Quantum Multi-User Detector Design
IEEE Access
IEEE Access
Computer Science