Quantum computers could offer substantial energy-saving advantages over conventional compute servers, particularly in the context of 5G signal processing. This paper explores the potential of Quantum Annealing (QA) to implement Forward Error Correction (FEC) and introduces Qu4Fec, an innovative solution for decoding Low-Density Parity Check (LDPC) codes. Our findings demonstrate that Qu4Fec improves state-of-the-art approaches in terms of Block Error Rate (BLER). However, our experiments reveal that the current leading Quantum Processing Units (QPUs) design is suboptimal for the FEC problem formulation. We identify key challenges, including qubit chain length, scaling, and quantization, and propose Quantum architectures to address these limitations.
Quantum computers could offer substantial energy-saving advantages over conventional compute servers, particularly in the context of 5G signal processing. This paper explores the potential of Quantum Annealing (QA) to implement Forward Error Correction (FEC) and introduces Qu4Fec, an innovative solution for decoding Low-Density Parity Check (LDPC) codes. Our findings demonstrate that Qu4Fec improves state-of-the-art approaches in terms of Block Error Rate (BLER). However, our experiments reveal that the current leading Quantum Processing Units (QPUs) design is suboptimal for the FEC problem formulation. We identify key challenges, including qubit chain length, scaling, and quantization, and propose Quantum architectures to address these limitations.
Quantum computers could offer substantial energy-saving advantages over conventional compute servers, particularly in the context of 5G signal processing. This paper explores the potential of Quantum Annealing (QA) to implement Forward Error Correction (FEC) and introduces Qu4Fec, an innovative solution for decoding Low-Density Parity Check (LDPC) codes. Our findings demonstrate that Qu4Fec improves state-of-the-art approaches in terms of Block Error Rate (BLER). However, our experiments reveal that the current leading Quantum Processing Units (QPUs) design is suboptimal for the FEC problem formulation. We identify key challenges, including qubit chain length, scaling, and quantization, and propose Quantum architectures to address these limitations.
In mobile communication systems, the increasing densification of radio access networks is creating unprecedented computational stress for baseband processing, threatening the industry's sustainability, and new computing paradigms are urgently needed to improve the efficiency of wireless processors. Quantum computing promises to revolutionize many computing-intensive tasks across diverse fields and therefore may be the key to realizing ultra-dense next-generation mobile systems that remain economically and environmentally viable. This paper investigates the potential of Quantum computing to accelerate Forward Error Correction (FEC), the most compute-heavy component of wireless processors. We first propose Qu4Fec, a novel solution for decoding Low-Density Parity Check (LDPC) codes on Quantum Processing Units (QPUs), which we show to outperform state-of-the-art approaches, by reducing the Block Error Rate (BLER) by nearly an order of magnitude in simulation. We then implement Qu4Fec on a real-world QPU platform to study its practical viability and performance. Our experiments reveal that current cutting-edge QPU architectures curb the capabilities of FEC and expose the underlying factors, including long qubit chains, scaling, and quantization. Based on these insights, we suggest original blueprints for future QPUs that can better support Quantum-based wireless processors. Overall, this paper provides a reliable reality check for the feasibility of wireless processing on Quantum annealers: as QPUs start to be considered part of a possible 6G landscape, our work may open new research paths towards the design of FEC methods for Quantum-powered wireless processors.
Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. However, its processing demands significant fraction of a wireless network's resources, due to its computationally-expensive decoding process. This forces network designers to compromise between performance and implementation complexity. In this paper, we investigate a novel processing architecture for FEC decoding, one based on the quantum approximate optimization algorithm (QAOA), to evaluate the potential of this emerging quantum compute approach in resolving the decoding performance-complexity tradeoff. We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. To accelerate QAOA-based decoding towards practical utility, FDeQ exploits temporal similarity among the FEC decoding tasks. This similarity is enabled by the fixed structure of a particular FEC code, which is independent of any time-varying wireless channel noise, ambient interference, and even the payload data. We evaluate FDeQ at a variety of system parameter settings in both ideal (noiseless) and noisy QAOA simulations, and show that FDeQ achieves successful decoding with error performance at par with state-of-the-art classical decoders at low FEC code block lengths. Furthermore, we present a holistic resource estimation analysis, projecting quantitative targets for future quantum devices in terms of the required qubit count and gate duration, for the application of FDeQ in practical wireless networks, highlighting scenarios where FDeQ may outperform state-of-the-art classical FEC decoders.
In the forthcoming 6G era, driven by the escalating demands for enhanced performance and reliability in communication systems, Low-Density Parity-Check (LDPC) codes emerge as a robust error correction coding scheme poised to effectively meet these requirements. In recent years, with the advancement of quantum computing, a proposal has emerged to integrate classical and quantum computing for the decoding of LDPC. The LDPC decoding can be treated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, thereby allowing the utilization of the D-Wave 2000Q quantum annealer for solving it. Expanding on this groundwork, the paper proposes an approach to enhance quantum annealing performance using classical algorithms. Specifically, it involves performing quantum annealing on the problem first, followed by post-processed using Maximum Likelihood Estimation (MLE) for the multiple outputs, known as QAML. Furthermore, to fully exploit the advantages of quantum annealing, this paper incorporates the energy and frequency of occurrence of candidate codewords as prior information into the Maximum Likelihood Estimation. These are called QAMLE and QAMLF respectively, further enhancing decoding performance. The proposed method is evaluated and compared with direct Quantum Annealing (QA) and Quantum Annealing with post-processing using Minimum Distance (QAMD). Research findings indicate that QAML exhibits a lower bit error rate compared to QA and QAMD.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.