Research

This page organises my research into a set of themes and links to representative publications on each topic.
For a complete list, please visit my Google Scholar page.

Selected Publications

• M. Rowshan, S. H. Dau, and E. Viterbo, “On the Formation of Min-weight Codewords of Polar/PAC Codes and Its Applications,” IEEE Trans. Inf. Theory, vol. 69, no. 12, pp. 7627–7649, Dec. 2023, doi: 10.1109/TIT.2023.3319015.
• M. Rowshan, A. Burg, and E. Viterbo, “Polarization-adjusted (PAC) Codes: Sequential Decoding vs List Decoding,” IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1434–1447, 2021, doi: 10.1109/TVT.2021.3052550.
• M. Rowshan, M. Qiu, Y. Xie, X. Gu, and J. Yuan, “Channel Coding Towards 6G: Technical Overview and Outlook,” IEEE Open J. Commun. Soc., vol. 5, pp. 2585–2685, 2024, doi: 10.1109/OJCOMS.2024.3390000.
• M. Rowshan, V. F. Dragoi, and J. Yuan, “Weight Structure of Low/High-Rate Polar Codes and Weight Contribution-based Partial Order,” IEEE Trans. Inf. Theory, vol. 71, no. 12, pp. 9340–9358, Dec. 2025, doi: 10.1109/TIT.2025.3616283.
• M. Rowshan and J. Yuan, “Segmented GRAND: Complexity Reduction through Sub-pattern Combination,” IEEE Trans. Commun., vol. 73, no. 8, pp. 5607–5620, Aug. 2025, doi: 10.1109/TCOMM.2025.3541094.
• X. Gu, M. Rowshan, and J. Yuan, “PAC Codes Meet CRC-Polar Codes,” in Proc. IEEE Inf. Theory Workshop (ITW), Sydney, NSW, Australia, 2025, pp. 1–6, doi: 10.1109/ITW62417.2025.11240368.

A. Fualt Tolerant Quantum Computing (FTQC) and Networking

1. Quantum Error Correction (QEC)

Quantum error correction (QEC) is a foundational technique that enables reliable quantum computation by protecting fragile quantum information from noise and decoherence through the use of redundant encoding across multiple physical qubits. It is essential for fault-tolerant quantum computing, as it allows logical qubits to maintain coherence long enough to execute deep quantum circuits, making large-scale and practically useful quantum algorithms feasible.

Focus and contributions

• Structure-driven design of quantum LDPC and topological codes from classical algebraic, coding-theoretic perspective.
• Noise- and structure-aware decoding algorithms for quantum codes including bias-aware, degeneracy-exploiting, and single-/few-shot decoders.
• Noise- and hardware-tailored quantum error-correcting architectures for biased-noise, and Floquet codes, integrating code design with realistic device and noise models.
• Code-structure–enabled fault-tolerant quantum computation leveraging code strucutre to realize native logical gates and scalable magic-state factories (e.g., CCZ “fountains”).

Representative work

• M. Rowshan, “Geometry, Degree, and Distance in Low-Weight Stabilizer Codes,” submitted to IEEE Transactions on Information Theory, March, 2026.
• M. Rowshan, “Structural Analysis of Directional qLDPC Codes,” submitted to Quantum, Feb, 2026.
• M. Rowshan, “Structural conditions for native CCZ magic-state fountains in qLDPC codes,” submitted to Physical Review A, Jan, 2026.
• M. Rowshan, “Single-Shot and Few-Shot Decoding via Stabilizer Redundancy in Bivariate Bicycle Codes,” to be presented at 2026 IEEE Int. Symp. Inf. Theory (ISIT), Guangzhou, China.
• M. Rowshan, “Strip-Symmetric Quantum Codes for Biased Noise: Z-Decoupling in Stabilizer and Floquet Codes,” to be presented at 2026 IEEE Int. Symp. Inf. Theory (ISIT), Guangzhou, China.
• M. Rowshan, “Bias-Aware BP Decoding of Quantum Codes via Directional Degeneracy,” 2016.

2. Quantum Algorithms for the Fault-Tolerant Era

Fault-tolerant quantum computing enables the execution of deep quantum circuits required for practical applications such as chemistry, materials science, and physics. Among these, Hamiltonian Simulation is a foundational primitive, underlying many higher-level algorithms including phase estimation and quantum linear algebra.

Focus and contributions

• Optimizing T-count and T-depth via circuit synthesis and algorithmic reformulation
• Co-designing algorithms with underlying QEC schemes to reduce decoding and communication overhead

Representative work

• M. Rowshan, “Error Propagation in Quantum Signal Processing Circuits: Noise Analysis and Robust Phase Design,” submitted to IEEE Journal of Selected Topics in Signal Processing, April, 2026.
• M. Rowshan, “Architecting Fault-Tolerant Quantum Signal Processing: Analytical Foundations, Fidelity Transitions, and Efficient Synthesis,” submitted to IEEE Journal of Selected Topics in Signal Processing, April, 2026.

3. Distributed Networked Fault-Tolerant Quantum Computing (FTQC)

Distributed Networked Fault-Tolerant Quantum Computing (FTQC) systems are architectures in which scalable quantum computation is achieved by interconnecting multiple error-corrected quantum modules through classical and optical networks.

Focus and contributions

• Latency-sensitive iterative decoding across racks
• Congestion and bandwidth limitations in top-of-rack and aggregation switches
• Heterogeneous link reliability, especially lossy inter-rack optical connections

Representative work

• M. Rowshan, “Network-Coded Syndrome Distribution for Topological QEC,” to be submitted to IEEE Networking Letters, April, 2026.

B. Error Correction Codes for Communications Systems

1. Channel Coding - Polar and PAC Codes: Structure, Algebra, and Weight Distribution

Focus and contributions

• Algebraic and combinatorial structure of polar and PAC codes (monomial / group-action viewpoint).
• Exact and partial weight enumerators, with emphasis on minimum- and low-weight codewords.
• Weight-based partial orders and their use in code design and performance prediction.

Representative publications

• M. Rowshan and V. F. Dragoi, “Generalized Weight Structure of Polar Codes: Selected Template Polynomials,” to be presented at 2026 IEEE Int. Symp. Inf. Theory (ISIT), Guangzhou, China.
• V. F. Dragoi and M. Rowshan, “Algebraic Properties of PAC Codes,” to be presented at 2026 IEEE Int. Symp. Inf. Theory (ISIT), Guangzhou, China.
• M. Rowshan, V. F. Dragoi, and J. Yuan, “Weight Structure of Low/High-Rate Polar Codes and Weight Contribution-based Partial Order,” IEEE Trans. Inf. Theory, vol. 71, no. 12, pp. 9340–9358, Dec. 2025, doi: 10.1109/TIT.2025.3616283.
• V. F. Dragoi, M. Rowshan, and J. Yuan, “On the Closed-form Enumeration of Polar Codes: 1.5d-Weight Codewords,” IEEE Trans. Commun., vol. 72, no. 10, pp. 5972–5987, Oct. 2024, doi: 10.1109/TCOMM.2024.3394749.
• M. Rowshan, S. H. Dau, and E. Viterbo, “On the Formation of Min-weight Codewords of Polar/PAC Codes and Its Applications,” IEEE Trans. Inf. Theory, vol. 69, no. 12, pp. 7627–7649, Dec. 2023, doi: 10.1109/TIT.2023.3319015.
• [J10] M. Rowshan and J. Yuan, “On the Minimum Weight Codewords of PAC Codes: The Impact of Pre-Transformation,” IEEE J. Sel. Areas Inf. Theory, vol. 4, pp. 487–498, 2023, doi: 10.1109/JSAIT.2023.3312678.
• M. Rowshan and J. H. Yuan, “Fast Enumeration of Minimum Weight Codewords for PAC Codes,” 2022 IEEE Information Theory Workshop (ITW), Mumbai, India, 2022, pp. 255–260, doi: 10.1109/ITW54588.2022.9965901.
• M. Rowshan, V. F. Dragoi, and J. Yuan, “Weight Structure of Low/High-Rate Polar Codes and Weight Contribution-based Partial Order,” IEEE Trans. Inf. Theory, vol. 71, no. 12, pp. 9340–9358, Dec. 2025, doi: 10.1109/TIT.2025.3616283.
• M. Rowshan, V. F. Dragoi, and J. Yuan, “Weight Structure of Low/High-Rate Polar Codes and Its Applications,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Athens, Greece, 2024, pp. 2945–2950, doi: 10.1109/ISIT57864.2024.10619618.
• V. F. Dragoi and M. Rowshan, “On Polar Codes Weight Distribution,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Ann Arbor, MI, USA, 2025, pp. 1–6, doi: 10.1109/ISIT63088.2025.11195548.

2. Channel Coding - Construction of (Pretransformed) Polar Codes

Focus and contributions

• Design of polar codes that explicitly incorporate weight distribution into the construction.
• Hybrid reliability–weight constructions (e.g., “retransform” polar codes) that go beyond standard Bhattacharyya-parameter–based designs.
• Bridging algebraic weight results with practical finite-length code design.

Representative publications

• M. Rowshan and V. F. Dragoi, “A Hybrid Reliability–Weight Framework for Construction of Polar Codes,” 2016.
• M. Rowshan and V. F. Dragoi, “Towards Weight Distribution-Aware Polar Codes,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Ann Arbor, MI, USA, 2025, pp. 1–6, doi: 10.1109/ISIT63088.2025.11195275.
• X. Gu, M. Rowshan, and J. Yuan, “PAC Codes Meet CRC-Polar Codes,” in Proc. IEEE Inf. Theory Workshop (ITW), Sydney, NSW, Australia, 2025, pp. 1–6, doi: 10.1109/ITW62417.2025.11240368.
• X. Gu, M. Rowshan, and J. Yuan, “Reverse Convolutional Precoding of Polar Codes: Design, Analysis, and Decoding Algorithms,” IEEE Open J. Commun. Soc., vol. 6, pp. 7184–7199, 2025, doi: 10.1109/OJCOMS.2025.3644338.
• M. Rowshan and E. Viterbo, “How to Modify Polar Codes for List Decoding,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Paris, France, 2019, pp. 1772–1776, doi: 10.1109/ISIT.2019.8849539.
• X. Gu, M. Rowshan, and J. Yuan, “Improved Convolutional Precoder for PAC Codes,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Kuala Lumpur, Malaysia, 2023, pp. 1836–1841, doi: 10.1109/GLOBECOM54140.2023.10437715.
• X. Gu, M. Rowshan, and J. Yuan, “Rate-Compatible Shortened PAC Codes,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC) Workshops, Dalian, China, 2023, pp. 1–6, doi: 10.1109/ICCCWorkshops57813.2023.10233795.

3. Channel Coding - Advanced Decoding Algorithms for Polar, PAC, and Related Codes

Focus and contributions

• Low-complexity decoding of polar and PAC codes beyond standard SC and SCL schemes.
• GRAND-based decoders with segmentation, constrained error pattern generation, and improved complexity–performance trade-offs.
• Sequential, list, and Fano-type decoders, including partial rewind and shifted-pruning strategies.
• Connections between code structure (e.g., weight distribution) and decoder behaviour.

Representative publications

• M. Rowshan, A. Burg, and E. Viterbo, “Polarization-adjusted (PAC) Codes: Sequential Decoding vs List Decoding,” IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1434–1447, 2021, doi: 10.1109/TVT.2021.3052550.
• M. Rowshan and E. Viterbo, “List Viterbi Decoding of PAC Codes,” IEEE Trans. Veh. Technol., vol. 70, no. 3, pp. 2428–2435, Mar. 2021, doi: 10.1109/TVT.2021.3059370.
• M. Rowshan and E. Viterbo, “[On Convolutional Precoding in PAC Codes(),” 2021 IEEE Globecom Workshops (GC Wkshps), 2021, pp. 1–6, doi: 10.1109/GCWkshps52748.2021.9681987.
• M. Rowshan, A. Burg, and E. Viterbo, “Complexity-efficient Fano Decoding of Polarization-adjusted Convolutional (PAC) Codes,” in Proc. Int. Symp. Inf. Theory Appl. (ISITA), Virtual/Kapolei, HI, USA, 2020, pp. 200–204, doi: 10.34385/proc.65.B03-2.
• M. Rowshan and J. Yuan, “Segmented GRAND: Complexity Reduction through Sub-pattern Combination,” IEEE Trans. Commun., vol. 73, no. 8, pp. 5607–5620, Aug. 2025, doi: 10.1109/TCOMM.2025.3541094.
• M. Rowshan and J. Yuan, “Low-Complexity GRAND by Segmentation,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Kuala Lumpur, Malaysia, 2023, pp. 6145–6151, doi: 10.1109/GLOBECOM54140.2023.10436895.
• M. Rowshan and J. H. Yuan, “Constrained Error Pattern Generation for GRAND,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Helsinki, Finland, 2022, pp. 1767–1772, doi: 10.1109/ISIT50566.2022.9834343.
• M. Rowshan and E. Viterbo, “Efficient Partial Rewind of Successive Cancellation-based Decoders for Polar Codes,” IEEE Trans. Commun., vol. 70, no. 11, pp. 7160–7168, Nov. 2022, doi: 10.1109/TCOMM.2022.3207842.
• M. Rowshan and E. Viterbo, “Improved List Decoding of Polar Codes by Shifted-pruning,” 2019 IEEE Information Theory Workshop (ITW), Visby, Sweden, 2019, pp. 1–5, doi: 10.1109/ITW44776.2019.8989330.
• M. Rowshan and E. Viterbo, “Shifted-pruning for Path Recovery in List Decoding of Polar Codes,” 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), NV, USA, 2021, pp. 1179–1184, doi: 10.1109/CCWC51732.2021.9375833.
• M. Rowshan and E. Viterbo, “SC List-Flip Decoding of Polar Codes by Shifted Pruning: A General Approach,” Entropy, vol. 24, no. 9, p. 1210, 2022, doi: 10.3390/e24091210.
• M. Rowshan, E. Viterbo, R. Micheloni, and A. Marelli, “Repetition-assisted Decoding of Polar Codes,” Electron. Lett., vol. 55, no. 5, pp. 270–272, 2019, doi: 10.1049/el.2018.6940.
• M. Rowshan and E. Viterbo, “Stepped List Decoding for Polar Codes,” IEEE International Symposium on Turbo Codes & Iterative Signal Processing (ISTC), Hong Kong, Dec. 2018, pp. 1–5, doi: 10.1109/ISTC.2018.8625267.

4. Channel Coding for 5G, 6G, and Beyond

Focus and contributions

• System-level viewpoint on channel coding for 5G/6G: requirements, performance targets, and implementation constraints.
• Comparative assessment and evolution of LDPC, turbo/convolutional, polar/PAC, and related codes in standards.

Representative publications

• M. Rowshan, M. Qiu, Y. Xie, X. Gu, and J. Yuan, “Channel Coding Towards 6G: Technical Overview and Outlook,” IEEE Open J. Commun. Soc., vol. 5, pp. 2585–2685, 2024, doi: 10.1109/OJCOMS.2024.3390000.

C. Intersection of Communications Systems and Other Fields

1. Signal Processing for Wireless Communications

Focus and contributions

• Delay–Doppler channel estimation tailored to modern OFDM and multicarrier systems.
• Use of ambiguity functions and structured delay–Doppler representations to improve estimation accuracy and robustness.
• Impact of channel estimation on end-to-end coded performance.

Representative publications

• H. P. H. Shaw, M. Rowshan, and J. Yuan, “Improving OFDM Using Delay-Doppler Channel Estimation,” IEEE Open J. Commun. Soc., vol. 6, pp. 7184–7199, 2025, doi: 10.1109/OJCOMS.2025.3603197.
• H. P. H. Shaw, J. Yuan, and M. Rowshan, “Delay-Doppler Channel Estimation by Leveraging the Ambiguity Function in OFDM Systems,” in Proc. IEEE Int. Conf. Commun. (ICC) Workshops, Rome, Italy, 2023, pp. 307–313, doi: 10.1109/ICCWorkshops57953.2023.10283594.

2. VLSI and Hardware–Software Co-Design for Communication Systems

Focus and contributions

• Quantisation strategies for belief-propagation and list decoders, targeting hardware efficiency.
• Architectural design for BP decoding of polar codes with non-uniform quantisation.
• Low-complexity detection algorithms for OTFS modulation

Representative publications

• X. Gu, M. Rowshan, and J. Yuan, “A Non-Uniform Quantization-Based Hardware Architecture for BP Decoding of Polar Codes,” in Proc. Int. Symp. Commun. Inf. Technol. (ISCIT), Sydney, NSW, Australia, 2023, pp. 363–368, doi: 10.1109/ISCIT57293.2023.10376101.
• M. Rowshan, E. Viterbo, R. Micheloni, and A. Marelli, “Logarithmic Non-uniform Quantization for List Decoding of Polar Codes,” in Proc. IEEE Annu. Comput. Commun. Workshop Conf. (CCWC), Las Vegas, NV, USA, 2021, pp. 1161–1166, doi: 10.1109/CCWC51732.2021.9375932.

3. Machine Learning in Channel Coding and Communication Systems

Focus and contributions

• Ongoing research on machine-learning–assisted decoding (e.g., learned heuristics for GRAND or SCL variants).
• Data-driven design of channel estimation and detection algorithms.

Books

• M. Rowshan and E. Viterbo, Polar Codes: From Theory to Practice, Hoboken, NJ, USA: Wiley–IEEE Press, 2025, ISBN 978-1-119-91173-9.
• M. Rowshan and V.-F. Dragoi, The Algebra of Polar Codes: Partial Orders, Automorphisms, Weight Structures, and Applications, Hoboken, NJ, USA: Wiley–IEEE Press, 2027.

Patents

• H. P. H. Shaw, J. Yuan and M. Rowshan, “Delay-Doppler based channel estimation method and apparatus,” Australian Patent 2024900503, Feb. 28, 2024.