We present a method for optimizing the waveplate’s voltage range of an electro-optic based electrical polarization controller for qubit encoding in discrete-variable quantum key distribution (QKD). By increasing the number of used waveplates, the maximum voltage range to generate each of the states-of-polarization required by a QKD protocol can be reduced. The proposed machine learning algorithm, particle swarm optimization, was validated through numerical and experimental results. Results show that transitions between six states of polarization used in QKD systems can be generated using a maximum voltage range of 10V in each waveplate.
We present a method for optimizing the waveplate’s voltage range of an electro-optic based electrical polarization controller for qubit encoding in discrete-variable quantum key distribution (QKD). By increasing the number of used waveplates, the maximum voltage range to generate each of the states-of-polarization required by a QKD protocol can be reduced. The proposed machine learning algorithm, particle swarm optimization, was validated through numerical and experimental results. Results show that transitions between six states of polarization used in QKD systems can be generated using a maximum voltage range of 10V in each waveplate.
We present a framework to optimize the voltage range of electro-optic polarization controllers (EPC) in polarization-based quantum key distribution (QKD) subsystems. In this study, we consider an EPC capable of modifying both the phase difference between its fast and slow axes and the orientation of the fast axis. This capability allows it to transform any input state of polarization (SOP) into any desired output SOP on the Poincaré sphere using a single wave-plate. When multiple wave-plates are available, properly distributing the required polarization modulation across them effectively reduces the electronic demands, lowers the implementation costs, and enhances the polarization modulation speeds. This optimization is achieved through the application of multi-objective optimization (MOO) and wave-plate splitting techniques. Within a simulation model, using the calibration parameters from a commercially available six-wave-plate EPC, we determined the optimized voltage ranges required to achieve the six, four, and three SOPs typically used in polarization-based QKD protocols. Two voltage reference points are considered in our study: bias voltage points, which result in zero birefringence, and zero voltage points. For optimization procedures centered around the bias voltage points, we observe a significant reduction in the voltage range, from ±37 V, for a single wave-plate, to approximately ±6 V, for six wave-plates. Furthermore, using wave-plate splitting techniques, we conclude that only two independent wave-plates (four variables) need to be considered in our model to achieve optimized results, which contributes to the efficient design of polarization-based QKD subsystems by minimizing voltage transitions while ensuring precise SOP control, ultimately enabling cost-effective and high-speed polarization modulation.
In this paper we propose and validate an algorithm for the automatic and deterministic state of polarization (SOP) generation, with direct application in polarization-encoded quantum key distribution (QKD) systems. The proposed SOP generation algorithm uses an electronic polarization controller (EPC), whose active elements entail the concatenation of several fiber-squeezing-based waveplates. Initially, we carry out a characterization of the waveplates. After that, we present in detail the working principle of the proposed algorithm. Finally, the effectiveness of the algorithm is experimentally assessed through the calculation of the quantum bit error rate (QBER), where an Arduino Due is used to control the different waveplates of the EPC. The QBER was measured for periods of 4 hours and 10 hours when generating two or four SOPs, respectively, with an overall average QBER value of 2%. Results show the potential of the proposed method regarding the generation of automatic and deterministic SOPs, thus demonstrating viability for its practical implementation in polarization-encoding QKD systems.
In this work, a fully functional polarization-encoding quantum key distribution system (QKD) is demonstrated. At the transmitter side, the polarization states are prepared using an algorithm to determine the voltages to be applied on the electronic polarization controller (EPC), to generate up to four polarization states. At the receiver, a second EPC is used to perform the basis alignment. The symbol synchronization is achieved using a reference optical signal that is wavelength division multiplexed with the quantum signal which carries the polarization-encoded qubit. Frame synchronization is achieved at a protocol level. The presented QKD system works at a repetition rate of 500 Hz, with an average quantum bit error rate of 1.35%.
In this work, we propose and experimentally validate a polarization basis alignment method based on a hybrid approach, using the Particle Swarm Optimization (PSO) and a gradient search algorithm. This method uses the Quantum Bit Error Ratio (QBER) as metric to be minimized. At first, the PSO runs one iteration to perform a coarse basis alignment, followed by the gradient search algorithm which performs a fine basis alignment, until the QBER is below the threshold. The polarization basis alignment method here proposed is implemented and tested at the receiver of a Quantum Key Distribution (QKD) system testbed. With this proposed hybrid basis alignment approach, a QBER below 1% can be achieved after, on average, nineteen QBER estimations, when using five particles for the PSO. These nineteen QBER estimations represent a 24% improvement regarding the method mostly used in the state of the art which.
An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.
Abstract We present the design and implementation of a state-of-polarization (SOP) management technique and two efficient synchronizing methods for quantum key distribution (QKD) systems. This is achieved following a wavelength-division multiplexing approach, where the classical synchronization signal and the quantum states are propagated in the same optical fiber. The employed frame synchronization method is based on the monitoring of the quantum bit error ratio (QBER) of the quantum channel, thus avoiding additional hardware and high computational resources. We evaluate the operation of SOP generation method through the assessment of the individual response of the waveplates that comprise the employed electronic polarization controller. Finally, the performance was assessed by computing the overall QBER and the QBER contributions of each of the four polarization states associated with the different qubits. The measurements, obtained during six hours, show a slight variation of the QBER values associated with the individual contributions, reaching an overall QBER of 0.75%. This demonstrates the capability of the presented methods to operate, stably, with very low QBER values, making its application in practical QKD systems reliable.