# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 2 Quantum Key Distribution Systems and Networks
### QKD Networking
Another experiment [51] showed that at least 170 nm of separation are required between the QKD signal and a single 6 dBm conventional channel. System feasibility with a lower separation between quantum and classical channels was experimentally demonstrated with a CV-QKD system [52]. In the experiment, a classical channel at 1550.12 nm was multiplexed with a CV-QD channel at 1530.12 nm. A positive key rate was obtained at 25, 50, and 75 km for classical channel power of 11.5 dBm, 5.5 dBm, and \(-0.5\) dBm, respectively. With a single 0 dBm classical channel at a distance of 25 km, the key rate was 24.11 kbit/s, dropping to 3.16 kbit/s at 50 km. Reducing the classical channel power to \(-3\) dBm, 0.49 kbit/s was obtained over 75 km. The impact of noise sources differing from Raman scattering was analyzed in [52], such as imperfect demultiplexer isolation, Rayleigh scattering, Brillouin backscattering, Brillouin-guided acoustic wave scattering, four-wave mixing, amplified spontaneous emission, sideband photons, and cross-phase modulation. The analysis showed a negligible cumulative contribution to the measured excess noise compared to the Raman scattering noise. Mitigating the physical impairments is not the only challenge towards an integrated quantum-classical network. Automated network operation is a necessary feature to lower the total cost of ownership to enable the adoption of QKD on a large scale. A proposal to extend the software-defined network (SDN) paradigm to QKD networks is illustrated in [53]. The proposed architecture consists of four layers: an optical layer, where classical channels are switched into optical cross-connects; a separate QKD layer, consisting of interconnected QKD trusted nodes; a control layer, in charge of the concurrent control of classical and quantum channels; and the application layer. A common path computation engine (PCE) for classical and quantum channels takes into account mutually induced penalties and common constraints, such as the total number of wavelengths. The segmentation of a single wavelength channel in multiple time slots to gain spectral efficiency and exploit bandwidth resources across the entire network is also envisaged. In [53], it is assumed that the secret keys for a service request with specific security demands are exchanged between the source and destination nodes at fixed time intervals. Each time interval consists of the channel estimation and calibration time, qubit exchange time, key sifting time, and key distillation time. Other examples of how the SDN and network function virtualization (NFV) paradigms--widely adopted in classical networks--can be extended to quantum networks, including internet of Things (IoT) [54] and 5G [55] applications, are reported in [55, 56, 57, 58, 59, 60, 61, 62, 63], further proving the importance of moving from current point-to-point setups to more complex topologies. Furthermore, recent advances in machine learning (ML) can greatly help to improve the automation of QKD systems, whose performance depends of many parameters that must be finely tuned, thereby significantly decreasing operational and maintenance costs. In [61], ML is used in a hybrid classical-quantum network to estimate the channel performance versus various spectrum allocations, launch powers, and channels spacings, and predict the optimal configuration. Similarly, ML-based parameter optimization, such as the choice of intensities and probabilities, is performed in [64]. The synchronization of single-photon gated detectors to the incoming quantum signal is another mandatory feature in practical QKD systems. This can be addressed by recovering the timing from the clock frequency of the digital classical signal or by means of a dedicated synchronization network.