Twin-field quantum key distribution (TF-QKD) can beat the linear bound of repeaterless QKD systems. After the proposal of the original protocol, multiple papers have extended the protocol to prove its security. However, these works are limited to the case where the two channels have equal amount of loss (i.e. are symmetric). In a practical network setting, it is very likely that the channels are asymmetric due to e.g. geographical locations. In this paper we extend the 'simple TF-QKD' protocol to the scenario with asymmetric channels. We show that by simply adjusting the two signal states of the two users (and not the decoy states) they can effectively compensate for channel asymmetry and consistently obtain an order of magnitude higher key rate than previous symmetric protocol. It also can provide 2-3 times higher key rate than the strategy of deliberately adding fibre to the shorter channel until channels have equal loss (and is more convenient as users only need to optimize their laser intensities and do not need to physically modify the channels). We also perform simulation for a practical case with three decoy states and finite data size, and show that our method works well and has a clear advantage over prior art methods with realistic parameters. maintain the same channel loss for all users (and users might join/leave a network at arbitrary locations). If channels are asymmetric, for prior art protocols, users would either have to suffer from much higher quantumbit-error-rate (QBER) and hence lower key rate, or would have to deliberately add fibre to the shorter channel to compensate for channel asymmetry, which is inconvenient (since it requires physically modifying the channels) and also provides sub-optimal key rate.Similar limitation to symmetric channels have been observed in MDI-QKD. In [15], we have proposed a method to overcome this limitation, by allowing Alice and Bob to adjust their intensities (and use different optimization strategies for two decoupled bases) to compensate for channel loss, without having to physically adjust the channels. The method has also been successfully experimentally verified for asymmetric .In this work, we will apply our method to TF-QKD and show that it is possible to obtain good key rate through asymmetric channels by adjusting Alice's and Bob's intensities-in fact, we will show that, Alice and Bob only need to adjust their signal intensities to obtain optimal performance. We show that the security of the protocol is not affected, and that an order of magnitude higher (than symmetric protocol) or 2-3 times higher (than adding fibre) key rate can be achieved with the new method. Furthermore, we show with numerical simulation results that our method works well for both finite-decoy and finite-data case with practical parameters, making it a convenient and powerful method to improve the performance of TF-QKD through asymmetric channels in reality.While we use the same main idea of allowing Alice and Bob to use asymmetric intensities to compensate for asymmetric channe...

Measurement-Device-Independent (MDI) QKD eliminates detector side channels in QKD and allows an untrusted relay between two users. A desirable yet highly challenging application is to implement MDI-QKD through free-space channels. One of the major factors that affect the secure key rate in free-space MDI-QKD is atmospheric turbulence. In this work we show two important results: First, the independent fluctuations of transmittances in the two channels can significantly reduce MDI-QKD performance due to turbulence-induced channel asymmetry. Second, we consider the Prefixed Real-Time Selection (P-RTS) method we formerly introduced to BB84 and extend it to MDI-QKD. Users can monitor classical transmittances in their channels and improve performance by post-selecting signals in real-time based on pre-calculated thresholds. We show that we can establish a 2-dimensional threshold between Alice and Bob to post-select signals with both high signal-to-noise ratio and low channel asymmetry in real time, and greatly extend the maximum range of MDI-QKD in the presence of turbulence, which can be an important step towards future free-space MDI-QKD experiments. I. BACKGROUND

For a practical quantum key distribution (QKD) system, parameter optimization -the choice of intensities and probabilities of sending them -is a crucial step in gaining optimal performance, especially when one realistically considers finite communication time. With the increasing interest in the field to implement QKD over free-space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the Internet of Things (IoT), a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search, or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a new method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board-computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 watts, and we find a speedup of up to 100-1000 times when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve over 95-99% of the optimal secure key rate. Moreover, our approach is highly general and not limited to any specific QKD protocol. I. BACKGROUND A. Parameter Optimization in QKDQuantum key distribution (QKD)[1-4] provides unconditional security in generating a pair of secure key between two parties, Alice and Bob. To address imperfections in realistic source and detectors, decoy-state QKD [5][6][7] uses multiple intensities to estimate single-photon contributions, and allows the secure use of Weak Coherent Pulse (WCP) sources, while measurement-deviceindependent QKD (MDI-QKD) [8] addresses susceptibility of detectors to hacking by eliminating detector side channels and allowing Alice and Bob to send signals to an untrusted third party, Charles, who performs the measurement.In reality, a QKD experiment always has a limited transmission time, therefore the total number of signals is finite. This means that, when estimating the single-photon contributions with decoy-state analysis, one would need to take into consideration the statistical fluctuations of the observables: the Gain and Quantum Bit Error Rate (QBER). This is called the finite-key analysis of QKD. When considering finite-size effects, the choice of intensities and probabilities of sending these intensities is crucial to getting the optimal rate. Therefore, we would need to perform optimizations for the search of parameters.Traditionally, the optimization of parameters is implemented as either a brute-for...

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