We investigate the optimization of epsilon-security parameters in quantum key distribution (QKD), aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm (CGA) to optimize the epsilon-security components of two representative protocols: the homodyne protocol from the continuous-variable (CV) family and the BB84 protocol from the discrete-variable (DV) family. We detail the CGA configuration, summarize the derivation of the composable key rate, and emphasize the role of the epsilon-parameters in both protocols. We then compare key rates obtained with optimized epsilon-values against those derived from standard and randomized choices. Our results demonstrate substantial key rate improvements at high security levels, where the key rate typically vanishes, and uncover positive-rate regimes that are inaccessible without optimization.
We investigate the optimization of epsilon-security parameters in quantum key distribution (QKD), aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm (CGA) to optimize the epsilon-security components of two representative protocols: the homodyne protocol from the continuous-variable (CV) family and the BB84 protocol from the discrete-variable (DV) family. We detail the CGA configuration, summarize the derivation of the composable key rate, and emphasize the role of the epsilon-parameters in both protocols. We then compare key rates obtained with optimized epsilon-values against those derived from standard and randomized choices. Our results demonstrate substantial key rate improvements at high security levels, where the key rate typically vanishes, and uncover positive-rate regimes that are inaccessible without optimization.
Quantum key distribution (QKD) enables two remote users to share a string of key bits with information-theoretical security. Parameter optimization is a crucial step in achieving optimal performance in practical QKD systems. In general, such optimization is implemented using a local search algorithm (LSA). However, LSAs inevitably fail to find out the optimal values when the searched key rate function is nonconvex or has a discontinuity of first-order derivatives and a narrow parameter search space. This paper proposes a genetic algorithm-based method to overcome the limitations of LSAs for QKD parameter optimization. We tested the proposed method with various types of common QKD protocols and found that it has very high parameter optimization performance for QKD with a time consumption comparable to that using a standard LSA. We expect our method to be a valuable optimization tool for quantum information processing tasks.
We explore the interplay between security and key rate of a quantum key distribution (QKD) protocol. This fundamental trade-off is investigated both at the link layer, i.e., for a QKD protocol implemented on a channel, and at the network layer, i.e., within the context of a multiuser multihop communication network. In the former scenario, we consider both discrete- and continuous-variable protocols in the composable finite-size framework, showing how their epsilon security restricts the key rate achievable on a single fiber link under various assumptions for the process of parameter estimation. In the latter scenario, we analyze the impact of composable security in the network setting, studying the interplay between end-to-end (ETE) epsilon security and ETE key rate for arbitrarily chosen remote end points. Starting from basic topologies to more complex ones, such as perturbed lattices, we analyze this trade-off under different types of routing strategies. In multipath routing, a greater number of paths can improve key rates, but this enhancement is accompanied by a reduction in the ETE security level. In the setting of single-path routing, we introduce a modified Dijkstra algorithm, which we call “lambda protocol,” able to optimize route construction depending on the joint ETE demand for security and rate. Our results are first derived for specific network instances (i.e., fixed topologies within a given network class) and subsequently extended to ensemble averages over entire network classes, where phase transitions are revealed with respect to node connectivity.
Continuous-variable (CV) quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties, and this is usually achieved via a Gaussian modulation of coherent states. The resulting secret key rate depends not only on the loss and noise in the communication channel, but also on a series of data processing steps that are needed for transforming shared correlations into a final string of secret bits. Here we consider a Gaussian-modulated coherent-state protocol with homodyne detection in the general setting of composable finite-size security. After simulating the process of quantum communication, the output classical data are postprocessed via procedures of parameter estimation, error correction, and privacy amplification. In particular, we analyze the high signal-to-noise regime, which requires the use of high-rate (nonbinary) low-density parity check codes. We implement all these steps in a python-based library that allows one to investigate and optimize the protocol parameters to be used in practical experimental implementations of short-range CV-QKD.
Continuous variable (CV) quantum key distribution (QKD) provides a powerful setting for secure quantum communications, thanks to the use of room-temperature off-the-shelf optical devices and the potential to reach much higher rates than the standard discrete-variable counterpart. In this paper, we provide a general framework for studying the composable finite-size security of CV-QKD with Gaussian-modulated coherent-state protocols under various levels of trust for the loss and noise experienced by the parties. Our paper considers both wired (i.e., fiber-based) and wireless (i.e., free-space) quantum communications. In the latter case, we show that high key rates are achievable for short-range optical wireless (LiFi) in secure quantum networks with both fixed and mobile devices. Finally, we extend our investigation to microwave wireless (WiFi) discussing security and feasibility of CV-QKD for very short-range applications.
Abstract Quantum key distribution (QKD) protocols allow for information theoretically secure distribution of (classical) cryptographic key material. However, due to practical limitations the performance of QKD implementations is somewhat restricted. For this reason, it is crucial to find optimal protocol parameters, while guaranteeing information theoretic security. The performance of a QKD implementation is determined by the tightness of the underlying security analysis. In particular, the security analyses determines thekey-rate, i.e., the amount of cryptographic key material that can be distributed per time unit. Nowadays, the security analyses of various QKD protocols are well understood. It is known that optimal protocol parameters, such as the number of decoy states and their intensities, can be found by solving a nonlinear optimization problem. The complexity of this optimization problem is typically handled by making a number of heuristic assumptions. For instance, the number of decoy states is restricted to only one or two, with one of the decoy intensities set to a fixed value, and vacuum states are ignored as they are assumed to contribute only marginally to the secure key-rate. These assumptions simplify the optimization problem and reduce the size of search space significantly. However, they also cause the security analysis to be non-tight, and thereby result in sub-optimal performance. In this work, we follow a more rigorous approach using both linear and nonlinear programs describing the optimization problem. Our approach, focusing on the decoy-state BB84 protocol, allows heuristic assumptions to be omitted, and therefore results in a tighter security analysis with better protocol parameters. We show an improved performance for the decoy-state BB84 QKD protocol, demonstrating that the heuristic assumptions typically made are too restrictive. Moreover, our improved optimization frameworks shows that the complexity of the performance optimization problem can also be handled without making heuristic assumptions, even with limited computational resources available.
This paper investigates the optimization of Quantum Key Distribution (QKD) protocols based on quantum decoherence. The core claim is to enhance key generation rates and security through intelligent protocol parameter tuning. Leveraging machine learning algorithms, we analyze the impact of decoherence noise, aiming to improve key generation efficiency and security. The proposed approach represents a novel application of machine learning within QKD, offering a significant pathway towards improved key distribution performance. We present a framework for adaptive parameter control, demonstrating the potential for substantial gains in key rates and robustness against decoherence effects. The research focuses on theoretical optimization, without experimental validation, providing a robust mathematical framework for understanding and improving current QKD protocols. The key performance metrics considered are key generation rate (R), Bit Error Rate (BER), and security level (Φ). The work contributes to the field by outlining a systematic methodology for utilizing machine learning to tackle the inherent challenges of decoherence in QKD systems. ---