Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. The main drawback of QKD is its high cost, which comes, among others, from:i) the requirements for the quantum transmitters and receivers; andii) the need of carefully selecting the fibers supporting the quantum channel to minimize the environmental effects that could dramatically change the polarization state of photons. In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. The main drawback of QKD is its high cost, which comes, among others, from:i) the requirements for the quantum transmitters and receivers; andii) the need of carefully selecting the fibers supporting the quantum channel to minimize the environmental effects that could dramatically change the polarization state of photons. In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Due to the fast-paced advancements of the quantum computing field, current public key cryptographic protocols need to be rethought. One promising solution is Quantum Key Distribution (QKD), whose security is robust to any computational advancement. The security of QKD relies on the laws of quantum mechanics, ensuring that any eavesdropper attempting to access the data will be detected. In particular, polarization-encoding QKD systems allow to generate secret keys in two remote locations to encode/decode information, using the State of Polarization (SOP) of single photons. Although its security has been theoretically proven, its practical implementation poses some challenges, part of which are the main focus of this PhD thesis. One relevant process in QKD systems is the synchronization between the transmitter and receiver, namely, the frame synchronization. This way, in this thesis, a frame synchronization method is developed and implemented, showing its potential for the use in prepare-and-measure QKD systems. The developed method has been shown to be effective, even with a low number of photons reaching the receiver. Furthermore, its architecture allows it to be adjusted for different attenuation regimes of the QKD system. The ability to encode data on the physical properties of single photons, in this case, polarization, highly depends on the capacity to accurately generate the required polarization states on demand. This way, an automatic SOP generation method, comprising an optical polarization monitoring system and a dedicated algorithm was developed and implemented, capable of generating four SOPs. This method reached an average Quantum Bit Error Rate (QBER) of 0.75%, which is well below the security threshold. After the SOP generation, and their transmission through the quantum channel, polarization alignment algorithms are required to compensate for the random rotations induced by birefringence in the optical fiber. In this thesis, a non-iterative polarization compensation method was implemented. This method requires only three QBER estimations to re-align the polarization, reaching average QBERs of 1.8%. Additionally, multiple iterative methods were implemented for comparison purposes, with the results clearly showing a better efficiency of the deterministic method. Beyond fiber-based transmissions, free-space transmissions were analysed due to their ability to reach sites without installed fiber and due to its potential for satellite-based quantum communications. This thesis demonstrates the feasibility of polarization-encoding QKD over free-space channels, by subjecting quantum signals to atmospheric turbulence to study its impact. In that sense, polarization variations over a 1.8 km outdoor free-space channel were monitored, to understand the effect of atmospheric turbulence on polarization. These results have shown that SOP variations in free-space links are slow, allowing their compensation. Furthermore, it was identified that, under conditions tested, the Secret Key Rate (SKR) could reach values up to 8.7×10ˉ⁴ bits per second, allowing QKD over free space for distances up to 2.31 km.
Discrete variables quantum key distribution (DV-QKD), with its well-studied and scrutinized BB84 protocol, benefits from being very attractive for highly secure communications. However, current detection schemes rely on the use of InGaAs SPDAs, which limits not only its use in high temperature environments, but also high secure communication rates. A possible approach is the use of coherent homodyne detection schemes for polarization encoding based DV-QKD combined. To deploy polarization encoding DV-QKD over standard optical fiber high speed networks, the polarization drift suffered due to birefringence over the channel must be compensated. In this work, we use a machine learning (ML) polarization tracking and compensation algorithm combined with a coherent homodyne receiver, thus allowing the deployment of high-speed polarization encoding based DV-QKD in standard optical fibers. The ML-algorithm predicts the SOP evolution keeping the error rate below 1 %. In this way, the overhead to polarization monitoring is reduced leading to a secure key exchange rate (SKR) of 79 Mbps for a communication over 40 km optical fiber.
Quantum key distribution (QKD) relies on quantum communication to allow distant parties to share a secure cryptographic key. Widespread adoption of QKD in current telecommunication networks will require the development of simple, low-cost, and stable systems. However, current QKD implementations usually include additional hardware that perform auxiliary tasks such as temporal synchronization and polarization basis tracking. Here we present a polarization-based QKD system operating at 1550 nm that performs synchronization and polarization compensation by exploiting only the hardware already needed for the quantum communication task. Polarization encoding is performed by a self-compensating Sagnac loop modulator that exhibits high temporal stability and the lowest intrinsic quantum bit error rate reported so far. The QKD system was tested over a fiber-optic link, demonstrating tolerance up to about 40 dB of channel losses. Due to its reduced hardware requirements and the quality of the source, this work represents an important step towards technologically mature QKD systems.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.
The rapid increase on the information sharing around the world, leads to an utmost requirement for capacity and bandwidth. However, the need for security in the transmission and storage of information is also of major importance. The use of quantum technologies provides a practical solution for secure communications systems. Quantum key distribution (QKD) was the first practical application of quantum mechanics, and nowadays it is the most developed one. In order to share secret keys between two parties can be used several methods of encoding. Due to its simplicity, the encoding into polarization is one of the most used. However, when we use optical fibers as transmission channels, the polarization suffers random rotations that may change the state of polarization (SOP) of the light initially sent to the fiber to a new one at the output. Thus, in order to enable real-time communication using this encoding method it is required the use of a dynamic control system. We describe a scheme of transmission of quantum information, which is based in the polarization encoding, and that allows to share secret keys through optical fibers without interruption. The dynamic polarization control system used in such scheme is described, both theoretically and experimentally. Their advantages and limitations for the use in quantum communications are presented and discussed.