Development of A BB84-Based Quantum Security System

The rapid advancement of quantum computing poses a serious threat to traditional cryptographic systems such as RSA and Elliptic Curve Cryptography (ECC), which rely on computational difficulty for security. Quantum algorithms like Shor’s and Grover’s have the potential to break or weaken these methods, putting modern communication systems at risk. To address this challenge, this work presents a quantum-safe com-munication framework that combines the BB84 Quantum Key Distribution (QKD) protocol with Post-Quantum Cryptography (PQC) techniques, aiming to ensure secure data exchange in the presence of quantum-capable adversaries.

In this system, the BB84 protocol is simulated to enable secure key exchange between two parties over a virtual quantum channel. The generated key is refined through processes such as basis matching, error correction, and privacy amplification using SHA-256 to ensure reliability and secrecy. For secur-ing communication, One-Time Pad (OTP) encryption is used alongside HMAC-SHA3-256 to provide both confidentiality and message integrity. Additionally, file encryption is handled using the XChaCha20-Poly1305 algorithm, selected for its efficiency and strong protection against potential vulnerabilities like nonce reuse.

To further enhance security, the framework integrates a hybrid key management approach using the Kyber-512 Key Encapsulation Mechanism (KEM) combined with a Key Deriva-tion Function (KDF), merging classical and quantum-resistant techniques. Experimental results demonstrate the effectiveness of the system, achieving a Quantum Bit Error Rate (QBER) of 2.5% and successful performance in both message and file en-cryption. Overall, this work contributes toward the development of practical and resilient communication systems designed for the emerging quantum era.

Quantum Key Distribution (QKD), BB84 Pro-tocol, Quantum Cryptography, Tensor Networks, Quantum Sim-ulation, QBER Analysis, Eavesdropping Detection, Secure Com-munication Systems

1. X. Wu, B. Zhang, G. Chen, and D. Jin, “A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation,” ACM Transactions on Modeling and Computer Simulation, vol. 32, no. 2, article 11, pp. 1–25, Feb. 2022. doi: 10.1145/3490029. [Google Scholar] [Crossref]

2. I. Gkouliaras, A. Kordas, K. Vlachos, and C. Kollmitz, “NuQKD: A Modular Quantum Key Distribution Simulation Framework,” Ad-vanced Physics Research, vol. 3, no. 1, pp. 1–15, Jan. 2024. doi: 10.1002/apxr.202400016. [Google Scholar] [Crossref]

3. S. Mangini, M. Grossi, M. W. Johnson, and R. Oru´s, “Tensor network noise characterization for near-term quantum computers,” Physical Re-view Research, vol. 6, no. 2, p. 023174, Apr. 2024. doi: 10.1103/Phys-RevResearch.6.023174. [Google Scholar] [Crossref]

4. D. Soler, J. Cillero, P. Dafonte, and J. A. Ferna´ndez-Veiga, “QKDNet-Sim+: Improvement of the Quantum Key Distribution Simulator for NS-3,” arXiv preprint arXiv:2402.10822, Feb. 2024. [Online]. Available: https://arxiv.org/abs/2402.10822. [Google Scholar] [Crossref]

5. A. Meddeb, F. Guen, and H. Touati, “Interactive simulation of quan-tum key distribution protocols and application in Wi-Fi networks,” Wireless Networks, vol. 29, no. 6, pp. 1759–1774, Aug. 2023. doi: 10.1007/s11276-023-03438-x. [Google Scholar] [Crossref]

6. A. Melnikov, A. Termanova, S. V. Dolgov, F. Neukart, and M. R. Perelshtein, “Quantum state preparation using tensor networks,” Quan-tum Science and Technology, vol. 8, no. 4, p. 045016, Jul. 2023. doi: 10.1088/2058-9565/acd9e7. [Google Scholar] [Crossref]

7. Y.-Q. Zhao, R.-G. Li, J.-Z. Jiang, C. Li, H.-Z. Li, E.-D. Wang, W.-F. [Google Scholar] [Crossref]

8. Gong, X. Zhang, and Z.-Q. Wei, “Simulation of quantum computing on classical supercomputers with tensor-network edge cutting,” Phys-ical Review A, vol. 104, p. 032603, Sep. 2021. doi: 10.1103/Phys-RevA.104.032603. [Google Scholar] [Crossref]

9. C. Huang et al., “Efficient parallelization of tensor network contraction for simulating quantum computation,” Nature Computational Science, 2021. [Google Scholar] [Crossref]

10. J. Tindall, M. Fishman, M. Stoudenmire, and D. Sels, “Efficient tensor network simulation of IBM’s Eagle kicked Ising experiment,” arXiv preprint arXiv:2306.14887, Jun. 2023. [Google Scholar] [Crossref]

11. F. Pan, X. Gao, L. Zhao, and L. Lin, “Efficient quantum circuit simulation by tensor network methods on modern GPUs,” arXiv preprint arXiv:2310.03978, Oct. 2023. [Google Scholar] [Crossref]

12. M. D´ıez Garc´ıa and A. Ma´rquez Romero, “Survey on computa-tional applications of tensor network simulations,” arXiv preprint arXiv:2408.05011, Aug. 2024. [Google Scholar] [Crossref]

13. B. Aizpurua, S. Patra, J. Etxezarreta Martinez, and R. Oru´s, “Hacking cryptographic protocols with tensor network attacks,” arXiv preprint arXiv:2409.04125, Sep. 2024. [Online]. Available: https://arxiv.org/abs/ 2409.04125. [Google Scholar] [Crossref]

14. A. M. Pastor, J. M. Badia, and M. Castillo, “A community detec-tion–based parallel algorithm for quantum circuit simulation using tensor networks,” The Journal of Supercomputing, vol. 80, pp. 5212–5233, Dec. [Google Scholar] [Crossref]

15. 2023. [Google Scholar] [Crossref]

16. A. P. Thompson, A. Arrasmith, A. Anand, and M. Cerezo, “Accurately simulating noisy quantum circuits with tensor networks,” arXiv preprint arXiv:2501.13237, Jan. 2025. [Online]. Available: https://arxiv.org/abs/ 2501.13237. [Google Scholar] [Crossref]

17. S. Masot-Llima and A. Garcia-Saez, “Stabilizer Tensor Networks: Universal Quantum Simulator on a Basis of Stabilizer States,” Phys-ical Review Letters, vol. 133, no. 23, p. 230601, Dec. 2024. doi: 10.1103/PhysRevLett.133.230601. [Google Scholar] [Crossref]

18. M. Pereira, L. Pereira, and H.-K. Lo, “Modified BB84 quantum key distribution protocol robust to source imperfections,” Physical Review Research, vol. 5, no. 2, p. 023065, Jun. 2023. doi: 10.1103/PhysRevRe-search.5.023065. [Google Scholar] [Crossref]

19. M. Geng, “Advances of Quantum Key Distribution and Network Nonlocality,” Entropy, vol. 27, no. 9, p. 950, Sep. 2025. doi: 10.3390/e27090950. [Google Scholar] [Crossref]

20. O. Bel, H. Shapira, A. Tzitrin, and M. Shapiro, “Simulators for quantum network modeling,” Computer Networks, vol. 254, p. 110009, Mar. 2025. doi: 10.1016/j.comnet.2025.110009. [Google Scholar] [Crossref]

21. A. M. Pastor, J. M. Badia, and M. Castillo, “A community detec-tion–based parallel algorithm for quantum circuit simulation using tensor networks,” The Journal of Supercomputing, vol. 81, pp. 14285–14302, Jan. 2025. doi: 10.1007/s11227-025-06918-3. [Google Scholar] [Crossref]

22. A. Berezutskii et al., “Tensor networks for quantum computing,” arXiv preprint arXiv:2503.08626, Mar. 2025. [Google Scholar] [Crossref]

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