Dataset for the paper "Duality of Quantum and Classical Error Correction Codes: Design Principles & Examples", Zunaira Babar, Daryus Chandra, Hung Viet Nguyen, Panagiotis Botsinis, Dimitrios Alanis, Soon Xin Ng and Lajos Hanzo, IEEE Communications Surveys & Tutorials. Abstract: Quantum Error Correction Codes (QECCs) can be constructed from the known classical coding paradigm by exploiting the inherent isomorphism between the classical and quantum regimes, while also addressing the challenges imposed by the strange laws of quantum physics. In this spirit, this paper provides deep insights into the duality of quantum and classical coding theory, hence aiming for bridging the gap between them. Explicitly, we survey the rich history of both classical as well as quantum codes. We then provide a comprehensive slow-paced tutorial for constructing stabilizer-based QECCs from arbitrary binary as well as quaternary codes, as exemplified by the dual-containing and non-dual-containing Calderbank-Shor-Steane (CSS) codes, non-CSS codes and entanglement-assisted codes. Finally, we apply our discussions to two popular code families, namely to the family of Bose-Chaudhuri-Hocquenghem (BCH) as well as of convolutional codes and provide detailed design examples for both their classical as well as their quantum versions. Acknowledgements: The financial support of the European Research Council under the Advanced Fellow Grant, that of the Royal Society’s Wolfson Research Merit Award and that of the Engineering and Physical Sciences Research Council under Grant EP/L018659/1 is gratefully acknowledged. * Figure 9: Sh_cap.fig plots the Shannon capacity limit for AWGN channel, which is characterized by Eq. (26) of the paper. * Figure 11: classical_bounds.fig plots the Rate versus normalized minimum distance bounds for classical codes. * Figure 12: cap-H4.fig plots the Hashing bounds for the unassisted (c = 0) and maximally-entangled (c = n − k) quantum codes, characterized by Eq. (27) and Eq. (29), respectively. * Figure 14: quantum_bounds.fig plots the Rate versus normalized minimum distance bounds for quantum codes. * Figure 15: minD_codewordL.fig plots the achievable minimum distance with increasing codeword length based on the finite-length closed-form formulation. * Figure 16: Codes-comp-cap.gle may be reproduced using the Graphics Layout Engine (GLE). It plots the schievable performance of various codes against the Hashing bound.