Quantum Cryptography – Principles, Protocols, and Future Directions: A Review
DOI:
https://doi.org/10.62411/jimat.v3i1.15959Abstract
The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems that rely on the computational hardness of mathematical problems such as integer factorization and discrete logarithm problems. In this context, quantum cryptography has emerged as a promising paradigm for secure communication based on the fundamental principles of quantum mechanics rather than on computational assumptions. This paper presents a comprehensive review of quantum cryptography, with a particular focus on Quantum Key Distribution (QKD), the most mature application. The study discusses the theoretical foundations of quantum security, including superposition, entanglement, and the No-cloning theorem, which collectively enable eavesdropping detection and guarantee information-theoretic security. Furthermore, the review examines major QKD protocols, such as BB84 and E91, as well as their advanced variants designed to address practical vulnerabilities and enhance performance. Recent progress in real-world implementations, including fiber-optic networks, free-space communication, and satellite-based systems such as the Micius satellite, is also analyzed. In addition, the paper highlights critical challenges related to scalability, hardware limitations, and security loopholes arising from imperfect devices. Finally, emerging research directions, including hybrid cryptographic frameworks that integrate quantum and post-quantum approaches, are discussed to provide insights into the future of secure communication. This review aims to provide a structured, up-to-date understanding of quantum cryptography, bridging the gap between theoretical developments and practical implementations, and emphasizing its crucial role in shaping next-generation cybersecurity systems.References
Maria Schuld, Francesco Petruccione, Supervised Learning with Quantum Computers, Springer, 2018.
Vojtěch Havlíček et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, 2019.
Edward Farhi, Hartmut Neven, “Classification with quantum neural networks on near term processors,” arXiv, 2018.
Jarrod R. McClean et al., “Barren plateaus in quantum neural network training landscapes,” Nature Communications, 2018.
Mikhail Schuld, Nathan Killoran, “Quantum machine learning in feature Hilbert spaces,” Physical Review Letters, 2019.
Aram W. Harrow et al., “Quantum algorithm for linear systems of equations,” Physical Review Letters, 2009.
Michael Cerezo et al., “Variational quantum algorithms,” Nature Reviews Physics, 2021.
Stefanie Woerner, Daniel J. Egger, “Quantum risk analysis,” npj Quantum Information, 2019.
Kerstin Borras et al., “Quantum computing for materials science and engineering,” Nature Reviews Materials, 2022.
Yudong Cao et al., “Quantum chemistry in the age of quantum computing,” Chemical Reviews, 2019.
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai. Quantum machine learning for ABO3 perovskite structure prediction. Computational Materials Science, Volume 250, Pages 113694, 2025, https://doi.org/10.1016/j.commatsci.2025.113694.
S Rustad, M Akrom, T Sutojo, HK Dipojono. A feature restoration for machine learning on anti-corrosion materials. Case Studies in Chemical and Environmental Engineering 10, 100902, 2024, https://doi.org/10.1016/j.cscee.2024.100902.
Akrom, M., Rustad, S. & Dipojono, H.K. Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning. J. of Materi Eng and Perform 34, 14948–14962 (2025). https://doi.org/10.1007/s11665-024-10129-x.
Muhamad Akrom, Wise Herowati, De Rosal Ignatius Moses Setiadi. A quantum circuit learning-based investigation: A case study in iris benchmark dataset binary classification. Journal of Computing Theories and Applications. Volume 2, Issue 3, Pages 355-367, 2024, https://doi.org/10.62411/jcta.11779.
Muhamad Akrom, Supriadi Rustad, Totok Sutojo, De Rosal Ignatius Moses Setiadi, Pulung Nurtantio Andono, Guruh Fajar Shidik, Hermawan Kresno Dipojono, Ryo Maezono. A novel quantum-enhanced model cascading approach based on support vector machine in blood-brain barrier permeability prediction. Materials Today Communications, Volume 45, Pages 112341, 2025, https://doi.org/10.1016/j.mtcomm.2025.112341.
Muhamad Akrom, Usman Sudibyo, Achmad Wahid Kurniawan, Noor Ageng Setiyanto, Ayu Pertiwi, Aprilyani Nur Safitri, Novianto Hidayat, Harun Al Azies, Wise Herawati. Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi. JoMMiT: Jurnal Multi Media dan IT, Volume 7, Issue 1, Pages 015-020, 2023, https://doi.org/10.46961/jommit.v7i1.721.
M. Akrom, T. Sutojo, A. Pertiwi, S. Rustad, H.K. Dipojono, Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds, J Phys Conf Ser, 2673(1), 012014 (2023), https://doi.org/10.1088/1742-6596/2673/1/012014.
M. Akrom, Green corrosion inhibitors for iron alloys: a comprehensive review of integrating data-driven forecasting, density functional theory simulations, and experimental investigation. J Mult Mater Inf, 1(1), 22–37 (2024), https://doi.org/10. 62411/jimat.v1i1.10495
M. Akrom, S. Rustad, H.K. Dipojono, A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors, Phys Scr, 99(3), 036006 (2024), https://doi.org/10.1088/1402-4896/ad28a9.
M. Akrom, S. Rustad, H.K. Dipojono, Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors, Results in Chemistry 6 (2023) 101126, https://doi.org/10.1016/j. rechem.2023.101126.
M. Akrom, S. Rustad, A.G. Saputro, H.K. Dipojono, Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors, Comput. Theor. Chem. 1229 (2023) 114307, https://doi.org/10.1016/ J.COMPTC.2023.114307.
M. Akrom, S. Rustad, H.K. Dipojono, Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning, Comput. Theor. Chem. 1236 (2024), https://doi.org/10.1016/j.comptc.2024.114599.
M. Akrom, Investigation of natural extracts as green corrosion inhibitors in steel using density functional theory, Jurnal Teori dan Aplikasi Fisika, 10(1), 89-102 (2022), https://doi.org/10.23960%2Fjtaf.v10i1.2927.
M. Akrom, S. Rustad, H.K. Dipojono. Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds. Materials Today Communications, 39, 108758 (2024), https://doi.org/10.1016/j.mtcomm.2024.108758.
M. Akrom, S. Rustad, H.K. Dipojono, SMILES-based machine learning enables the prediction of corrosion inhibition capacity, MRS Commun 14 (2024) 379–387, https://doi.org/10.1557/s43579-024-00551-6.
M. Akrom, S. Rustad, A.G. Saputro, A. Ramelan, F. Fathurrahman, H.K. Dipojono, A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds, Mater. Today Commun. 35 (2023) 106402, https://doi.org/10.1016/J. MTCOMM.2023.106402.
M. Akrom, et al., DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract, Appl. Surf. Sci. 615 (2023), https://doi.org/10.1016/j. apsusc.2022.156319.
M. Akrom, S. Rustad, H.K. Dipojono. Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds. Materials Today Quantum, 2, 100007 (2024), https://doi.org/10.1016/j.mtquan.2024.100007.
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai. Enhanced quantum support vector regression with quantum kernels and virtual sampling for ABX3 perovskite formation energy. Expert Systems with Applications, Pages 130817, 2025, https://doi.org/10.1016/j.eswa.2025.130817.
Muhamad Akrom. Quantum Neural Network in Architectures, Learning Mechanisms, and Emerging Applications Across Domains: A Review. Journal of Multiscale Materials Informatics, Volume 2, Issue 2, Pages 30-39, 2025, 10.62411/jimat.v2i2.14929.
Downloads
Published
Issue
Section
License
Authors who publish their articles in this journal agree to the following conditions:
- Copyright remains with the author and gives the JIMAT journal the right as first priority to publish the article under a Creative Commons Attribution License which allows articles to be shared with acknowledgment of the author of the article and this journal as the place of publication.
- Authors can distribute their published articles non-exclusively (for example: in university repositories or in books) with notification or acknowledgment of publication in JIMAT.
- Authors are permitted to list their work online (for example: on a personal website or in a university repository) before and after the submission process (see The Effect of Open Access).
