Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Quantum annealing offers an appealing route to handle large-scale optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In particular, the novel D-Wave Advantage devi...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.131658-131671
Main Authors: Tabi, Zsolt I., Marosits, Adam, Kallus, Zsofia, Vaderna, Peter, Godor, Istvan, Zimboras, Zoltan
Format: Article
Language:English
Subjects:
Annealing
Antenna arrays
Channel decoding
Comparative studies
Decoders
graph embedding
Massive MIMO
Maximum likelihood decoding
MIMO communication
Modulation
NP-hard optimization
Optimization
Phase shift keying
Post-production processing
Quadrature amplitude modulation
quantum annealing
quantum computing
Qubit
Qubits (quantum computing)
Solution space
telecommunication
Topology
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Summary:Quantum annealing offers an appealing route to handle large-scale optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In particular, the novel D-Wave Advantage device has been recently released. Its performance is expected to improve upon the previous state-of-the-art D-Wave 2000Q annealer, due to higher number of qubits and the Pegasus topology. Here, we present a comparative study via an ensemble of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in Centralized Radio Access Network (C-RAN) architectures. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions mainly use approximate methodologies, Kim et al. leveraged Quantum Annealing for large MIMO problems with Phase Shift Keying and Quadrature Amplitude Modulation scenarios. Here, we extend their work and analyze experiments for more complex modulations and larger MIMO antenna array sizes. By implementing the extended QUBO formulae on the novel annealer architecture, we uncover the limits of state-of-the-art quantum optimization for the massive MIMO ML decoder. We report on the improvements and discuss the uncovered limiting factors learned from the 64-QAM extension. We include the enhanced evaluation of raw annealer sampling via implementation of post-processing methods in the comparative analysis between D-Wave 2000Q and the D-Wave Advantage system.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3114543