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Frontiers in Quantum Science and Technology ·

Volume 4 - 2025 | https://doi.org/10.3389/frqst.2025.1575498

Quantum key distribution through quantum machine learning: a research review

  • Krupa Purohit*corresponding author
  • Ajay Kumar Vyas
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Abstract

Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols plays a crucial role in enhancing security measures, addressing previously inaccessible threats, and improving cryptographic efficiency. Key research areas in quantum cryptography include Quantum Key Distribution (QKD), eavesdropping detection, QSDC, security analysis of QKD protocols, post-quantum cryptography, Quantum Network Security & Intrusion Detection, Quantum-secure communication beyond QKD, quantum random number generation, Quantum Secure Multi-Party Computation (QSMPC), Quantum Homomorphic Encryption (QHE), and privacy-preserving computation. QML algorithms improve the key generation of QKD, by improving quantum state selection and reducing measurements. This also allows them to increase efficiency because it identifies trends in errors and applies corrections, making quantum cryptography a more dependable option. With intelligent processing machine learning is excellent at handling complex, high-dimensional data-this may provide a viable strategy for enhancing QKD performance and increasingly real-world secure quantum communication networks. This review will explore current research gaps and future developments in QKD, security analysis of QKD protocols, and eavesdropping detection by leveraging various QML algorithms.

Keywords

1 Introduction

Beyond the constraints of classical encryption, the field of quantum cryptography (QC) provides a revolutionary method of secure communication. Despite traditional cryptography methods, QC protocols offer verifiable security assurances by utilizing the concepts of quantum mechanics (Goyal, 2024). Quantum teleportation (QT), quantum secret sharing (QSS), quantum secure direct communication (QSDC), and quantum key distribution (QKD) represent the four fundamental branches of quantum cryptography protocols. Unlike QKD, which is primarily concerned with the secure negotiation of cryptographic keys, QSDC introduces a novel communication paradigm that provides a comprehensive, confidential, and near-instantaneous solution by transmitting actual messages directly over a quantum channel. By eliminating the need for cryptographic keys for encryption and decryption, QSDC ensures secure communication (Pan et al., 2024). Quantum private communication is a practical application for quantum mechanics. It enables secure communication against traditional approaches and advanced technologies such as quantum computers. There are several types of Cryptographic tasks, such as quantum private inquiry and quantum digital signatures control communication. The most basic one is quantum key distribution (QKD) (Liu et al., 2022). The QKD system utilizes physics principles rather than the computational complexity of mathematics problems to enable the provably safe exchange of cryptographic keys against eavesdropping. This fact helps make sure anyone attempting to access the key exchange process may be identified, ensuring the highest level of security possible. Quantum Random Number Generation (QRNG) improves cryptographic solutions by producing real random numbers needed for key-setting and growth processes, among other areas (Gowda et al., 2025). For quantum key generation, random numbers are used. Various protocols used in QKD include BB84 protocols, E91 protocol, CV-QKD protocol, Measurement-Device-Independent QKD (MDI-QKD), Twin-Field QKD Protocol etc. QML is a rapidly evolving field, which is formed by integrating standard machine learning with the concepts of quantum computing. It uses machine learning methods to further research on quantum computing and strives to revolutionize machine learning by utilizing the unique capabilities of quantum physics (Qi et al., 2024). QML plays a significant role in improving the Quantum cryptography research landscape. Various QML algorithms like quantum-inspired feature maps and kernel-based classifiers, Real-time protocol prediction for quantum key distribution, Applications of artificial neural networks in quantum key distribution, Dimensionality reduction using quantum algorithms, Hybrid quantum-classical convolutional neural networks, Kernal-based QML, quantum neural networks with machine learning principles, Quantum convoluted neural networks (QCNN), Quantum Particle Swarm Optimization (QPSO) improves QKD, Random number generation, side channel attack mitigation, Adaptive protocols, post-quantum cryptography, anomaly detection essential for quantum-safe communication and data protection techniques. The rapid growth of developments of QML algorithms to maximize the efficiency of Quantum cryptography research like QKD, eavesdropping detection, and security analysis of QKD protocols is demonstrated in Figure 1a, which represents the number of articles published in this field from 1990 to 2025 as per research data collected google scholar and web of science. Figure 1b illustrates key aspects of both QML and Quantum Cryptography (QC), highlighting how advancements in quantum mechanics and computing enhance classical cryptography and machine learning, leading to the evolution of quantum cryptography and QML. As shown in the figure, integrating QML with quantum cryptography enhances security, optimizes cryptographic processes, and improves vulnerability detection.

The integration of QML into QKD research has facilitated the resolution of more complex problems within the quantum cryptography community. As illustrated in **(a)** research on eavesdropping detection and security analysis of QKD protocols has significantly increased after 2010, reflecting a growing focus on more robust and intelligent cryptographic applications. **(b)** Illustrates how advancements in quantum mechanics and quantum computing have contributed to the development of quantum cryptography (QC) and quantum machine learning (QML). Various technologies associated with these verticals are highlighted, paving the way for intelligent, adaptive, and secure quantum communication systems. **(a)** Quantum cryptography research landscape represents the number of articles published in Quantum cryptography research. **(b)** Quantum machine learning in augmenting quantum cryptography.
Figure 1. The integration of QML into QKD research has facilitated the resolution of more complex problems within the quantum cryptography community. As illustrated in (a) research on eavesdropping detection and security analysis of QKD protocols has significantly increased after 2010, reflecting a growing focus on more robust and intelligent cryptographic applications. (b) Illustrates how advancements in quantum mechanics and quantum computing have contributed to the development of quantum cryptography (QC) and quantum machine learning (QML). Various technologies associated with these verticals are highlighted, paving the way for intelligent, adaptive, and secure quantum communication systems. (a) Quantum cryptography research landscape represents the number of articles published in Quantum cryptography research. (b) Quantum machine learning in augmenting quantum cryptography.

The rest of the article explores the fundamental aspects of quantum cryptography, the role of QML in Quantum cryptography, and three major research areas: quantum key distribution, eavesdropping detection, and the security analysis of QKD protocols with advancements in QML algorithms. Additionally, it highlights current research gaps and future developments in the domain in detail.

2 Quantum cryptography

Quantum cryptography utilizes quantum mechanics to develop secure communication systems that differ from conventional methods. Quantum cryptography’s security depends on physics principles, unlike traditional encryption, which relies on the computational complexity of certain mathematical problems. Quantum cryptography utilizes quantum physics principles to ensure secure communication. Quantum Encryption employs quantum states to encrypt data directly, along with the prominent QKD application for secure key exchange. Random numbers are essential for generating keys in QKD. As a result, both QKD and random number generation play a crucial role in the landscape of quantum cryptography. This includes advanced protocols like Quantum Secure Direct Communication and Quantum Homomorphic Encryption (Goyal, 2024). The two primary categories of Quantum Key Distribution Protocols (QKDPs) are Continuous Variable (CV) and Discrete Variable (DV) QKDPs. Discrete Variable QKDPs generate discrete outcomes by employing the polarization of a single photon or the spin of an electron for key distribution. Discrete Variable QKDPs, which employ a single proton to store information, CV-QKDPs use light, which has the advantage of being easier to create coherent light. Single photons are used in DV protocols, whereas homodyne or heterodyne detection techniques are used in CV protocols. Some examples of Discrete Variable QKDPs include BB84, E91, B92, and SARG04 protocols. Some protocols like the Coherent-One-Way (COW) protocol, Differential-Phase-Shift (DPS) protocol, and Round-Robin Differential Phase Shift (RRDPS) protocol are Distributed Phase Reference QKDPs. Continuous Variable QKDPs also include some protocols based on source state, squeezed state of light, two-state protocol, and coherent states for discrete modulation. Super-Dense Coding (SDC) also known as the “Ping-Pong” Protocol and LM05 Protocol are examples of two-way protocol (Nwaga and Nwagwughiagwu, 2024). Some of the experimental implementations of quantum cryptography include Fiber-Based QKD Systems, Free-Space Quantum Communication, Integrated Quantum Photonics, Quantum Repeaters, Satellite-Based Quantum Communication, and Quantum Cryptographic Networks (Goyal, 2024). Recent advancements in quantum cryptography include the Semi-Quantum Private Comparison (SQPC) protocol based on Bell states, without quantum entanglement swapping. This enhances the performance of quantum cryptography against various attacks, making it particularly effective for noisy quantum channels (Geng et al., 2024). Gong et al. (2024c) discuss the Novel semi-quantum private comparison protocol with Bell states in which any quantum state is not required to prepare and measure for classical users and it also excludes unitary operation on received quantum particles. This novel approach is also secure against external and internal attacks. Another recent revaluation in quantum cryptography protocols includes Mode-pairing quantum key distribution based on wavelength division multiplexing in multi-user networks developed by Cui et al. (2024) which includes the performance of MP-QKD for multiple users with integration of WDM (wavelength division multiplexing). This analysis is essential for asymmetric network channels to find many applications in quantum communication networks. Multi-party semi-quantum private comparison (MQPC) protocols developed by Gong et al. (2023) represent an advancement in quantum cryptography in which the use of decoherence-free states (DFS) against collective noise multiparty can communicate in the presence of collective-dephasing noise and collective-rotation noise which affects the integrity of quantum communication.

3 Quantum machine learning in augmenting quantum cryptography

With the integration of quantum computing into classical machine learning, QML emerges as a powerful approach to enhance computational performance. Various classical machine learning algorithms, including supervised and unsupervised learning, benefit from quantum principles, leading to improved efficiency and scalability. Unsupervised learning, particularly in processing large real-time data, has gained popularity with the development of quantum generative models. Several variants of Generative Adversarial Networks (GANs) with advanced computing capabilities have been introduced. Recently, a hybrid quantum-classical Generative Adversarial Network (GAN) has been developed for image generation, leveraging QML to overcome quantum hardware constraints (Zhou et al., 2023). With the advancement of artificial intelligence and machine learning, traditional optimization techniques struggle to handle complex, nonlinear, and systemic problems. Advanced algorithms such as ant colony optimization, bat optimization, simulated annealing, genetic optimization, fruit fly optimization, particle swarm optimization (PSO), and the gravitational search method, combined with effective preprocessing techniques, offer more robust solutions. Among these, PSO, a nature-inspired metaheuristic algorithm, stands out due to its fewer control parameters, faster search rate, and lower computational complexity. It has proven highly effective in addressing various engineering and AI optimization challenges, particularly in identifying optimal solutions across a wide range of applications (Gong C. et al., 2024; She et al., 2025) discuss quantum-classical hybrid neural network model—St-HQCNN which can be used in quantum-enhanced cryptographic security, anomaly detection, and QKD protocols optimization (Gong L. et al., 2024). discusses the Quantum K-Nearest Neighbor (QKNN) classification algorithm, utilizing a divide-and-conquer strategy, which offers several advantages in QKD, including eavesdropping detection, error and noise reduction, and improved scalability. The advancement of QKD includes a proposed strategy for measurement-free mediated semi-quantum key distribution (MSQKD) using single-particle states. This approach enables two classical users to establish a secret key with the assistance of a third party, enhancing security and scalability while eliminating the need for a quantum detector (Zhou et al., 2024). The Multi-Party Semi-Quantum Private Comparison (SQPC) protocol, utilizing d-dimensional single-particle states, enables the secure comparison of private data sizes with the assistance of a quantum third party. It is well-suited for multi-user and large-scale quantum cloud applications (Gong et al., 2025). An MSQPC protocol is constructed using d-dimensional SPSs to securely determine the size relationship between classical participants’ private data. This protocol relies on unitary operations and a pre-shared key, while entanglement swapping remains optional (Gong et al., 2025). Two prominent fields of quantum technology, QML and quantum cryptography (QC) hold immense potential for future advancements. While research at the intersection of QML and QC is still in its early stages, the outlook is promising as both areas continue to evolve. The integration of QML and QC could pave the way for more secure communication systems in the quantum era, as hardware capabilities and practical applications progress.

3.1 Prospects and constraints of quantum machine learning in QKD, eavesdropping detection and security analysis of QKD protocols

QKD is a fundamental aspect of quantum cryptography, and integrating QML with QKD can significantly enhance the performance of quantum cryptographic systems. Once keys are successfully generated in QKD, detecting eavesdropping becomes a critical step in ensuring the security of quantum communication channels. Strengthening QKD protocols remains a vital frontier, representing the convergence of quantum communication and quantum computing.

The Table 1 provides a summary of recent QML protocols applied in QKD, highlighting current research gaps and potential future directions.

TABLE 1. Recent advancements in QML algorithms for QKD highlight current research gaps and future directions.

QML in QKDProposed algorithmProspectsConstraints
Quantum machine learning in Quantum key distributionQNN-QRL: (Behera et al., 2025)In this approach, two novel QRL-based algorithms are introduced and integrated with a QNN implemented on BB84 and B92 protocols. This integration enhances security, optimizes key management, improves adaptability, reduces noise, and optimizes resource utilizationIntegration QNN-QRL with the classical system is challengingReinforcement learning in a quantum environment creates instabilityMany hardware constraints with the real-time implementation of QNN-QRL
Optimal parameter prediction for secure QKD using QML models (Bhargavi and Subramanya, 2020)Optimization of parameters enhances the key rate, and scalability, and reduces noiseReliability, Standardization, Computational overload, and Training data dependency are major constraints
Phase compensation for CV- QKD based on CNN (Xing et al., 2022)Application of CNN in phase noise compensation will reduce Classical Post-Processing Overhead, improve channel variation adaptation, and improve secret key rateLatency concernsAdversarial attacksFurther research is required to implement the proposed machine-learning method across various environments and modulation schemes
Neural network-based prediction of the secret-key rate of QKD (Zhou et al., 2022)Prediction of the secret key -rate using a neural network improves performance in noisy channels and is efficient for secret key rate estimationGeneralization IssuesOverhead issues
Quantum Federated Learning (Dutta et al., 2024)A Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption offers privacy and performance optimizationConstraints in hardware, scalability, and implementation
QML-IDS (Abreu et al., 2024)In QML-IDS, three QML models: Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM), and Quantum Convolutional Neural Network (QCNN)There are still challenges and limitations to address before practical implementation in real network systems can be achieved, making this an active area of research with significant potential for future development. Integrating the approach with existing classical IDS frameworks and traditional ML-based IDS systems will be explored to enhance protection mechanisms. Moreover, overcoming quantum hardware limitations and managing privacy concerns related to processing data on platforms like IBM’s will be essential for advancing quantum-enhanced cybersecurity solutions
Artificial Intelligence in Quantum Communications (Mahmud and Abdelhadi, 2025)In this article, the applications of various machine learning algorithms in quantum communication (QC) are discussed. For quantum key distribution (QKD) and simulation, QSVM and QNN techniques are utilized, enabling fast and efficient key generationMajor constraints include coherence issues in QNN, which lead to errors and training challenges, as well as hardware limitations in QSVM. To mitigate these issues, advanced qubit technologies, optimized algorithms, hybrid AI models, and error correction techniques must be employed
Quantum Machine learning in eavesdropping detectionQKD as a QML task (Decker et al., 2024)Describes a novel perspective to Optimize eavesdropping with robust security measures. The BB84 protocol is analyzed in this articleEthical and security constraintsFuture research can be conducted on other QKD protocols
QGANS (Olaoye and Potter, 2024)QGAN is applicable to improve eavesdropping detection in QKD which improves sampling efficiency, power, and data generation rate and finds many applications in fields like drug discovery and material scienceReal-time implementation of QGAN finds many challenges like training instability, convergence issues, Resource consumption, and constraints in quantum resources and algorithms
Quantum deep learning-based anomaly detection (Hdaib et al., 2024)The application of Quantum deep learning enhances the detection accuracy of anomaly detectionThe three proposed techniques, combining quantum autoencoders with quantum KNN, quantum random forest, and quantum SVM, effectively detect irregularities in network traffic with high accuracyPractical implementation and scalability issuesTo enhance the resilience of anomaly detection systems, researchers may explore various quantum algorithms, refine quantum autoencoder designs, and expand evaluations to more complex datasets
Quantum machine learning in security analysis of QKD protocolsHigh-rate Discretely Modulated CV-QKD Using QML) Liao et al. (2025)QKNN classifier is used to improve secret key rate and system performanceQkNN-based CVQKD not only enhances the CVQKD protocol but also introduces a novel approach for integrating other quantum machine learning techniques into the CVQKD domainResource requirements and security issues are critical constraintsThe proposed QkNN is not the only effective quantum classifier that can be used to enhance CVQKD when integrated with the proposed processing architecture
Machine-learning-based anomaly detection (Corli et al., 2024)Applying supervised, unsupervised, and reinforcement learning, advancements in machine learning enhance anomaly detectionA clear comparison with classical algorithms is essential
Empirical Risk-aware Machine Learning on Trojan-Horse Detection for Trusted QKD Networks (Chou et al., 2024)Advancements in QML enhance the detection of various attacks, further improving the security of QKD protocolsThis work promotes a risk-aware reinforcement learning approach that incorporates risk assessment and risk references to design the reward function while considering the trust conditionsAccurate risk modeling and scalabilityThe future directions can draw inspiration from Bayesian online change-point detection and risk-aware reinforcement learning, enabling trustworthy QKD networks to gain valuable insights into optimal trust policies. This approach, applied within the framework of trusted node and variant network design, aims to facilitate the cost-effective deployment of QKD networks

4 Discussion

This article discusses advancements in QML for quantum cryptography, especially with a focus on QKD, eavesdropping detection, and security analysis. Recent studies are reviewed, and their prospects and constraints are summarized in a tabular format. Key research gaps include optimization challenges due to the lack of dedicated QML models, practical implementation and real-time testing limitations, hybrid quantum-classical tradeoffs, scalability issues, hardware constraints, security and robustness concerns, quantum memory and data loading difficulties, data encoding challenges, and computational overhead. Future research directions include optimizing model design to enhance security and robustness. Hybrid quantum-classical Generative Adversarial Networks (GANs) help overcome hardware constraints by requiring fewer Qubits and enabling parallel processing. Unsupervised learning minimizes resource usage, reduces noise, lowers computational overhead, and facilitates adaptive quantum encoding and compression—critical for real-time problem analysis. Additionally, advanced optimization algorithms such as ant colony optimization, bat optimization, simulated annealing, genetic optimization, fruit fly optimization, particle swarm optimization (PSO), and the gravitational search method, when combined with effective preprocessing techniques, offer more robust and efficient solutions. Additionally, QSDC protocols are well-suited for a wide range of cryptographic applications, and several advanced protocols extending beyond QSDC have been developed. Unlike the QKD family of protocols, which focuses solely on secret key negotiation, QSDC enables secure communication without requiring cryptographic keys for encryption and decryption.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

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Statements

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Author contributions

KP: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing. AV: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – review and editing.

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Citation

Purohit, K. & Vyas, A. K. (2025). Quantum key distribution through quantum machine learning: a research review. Frontiers in Quantum Science and Technology, Volume 4. https://doi.org/
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Copyright © 2025 Purohit and Vyas.

License: http://creativecommons.org/licenses/by/4.0/

*Correspondence: Krupa Purohit

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