Ioannis Krikidis

Nikos A. Mitsiou, Ioannis Krikidis

In this paper, we investigate the symbol error probability (SEP) of phase-squeezed M-ary phase-shift keying (M-PSK). Since the relevant observable for M-PSK detection is the optical phase, we adopt the adaptive Mark-II receiver which is a physically realizable phase measurement. First, we develop a theoretical analysis based on the phase probability operator measure (POM) of the Mark-II scheme in the Fock basis. Then, we develop two SEP methods based on the statistics of the received PSK symbol and the error introduced by the Mark-II measurement. The first method derives the phase probability density induced by the squeezed state noise and incorporates the additional Mark-II phase uncertainty through an angular convolution. Since this convolution does not admit a simple closed form, we also introduce an effective tangential-variance model, which yields a closed form SEP expression in terms of the Owen's T-function. Numerical results show that phase squeezing substantially reduces the SEP of M-PSK compared to coherent state transmission, with greater gains for higher constellation orders. Notably, for the investigated scenario, squeezing can almost double the photon efficiency of M-PSK as the mean number of transmitted photons increases. Finally, the proposed approximations closely follow the Mark-II POM analysis, typically within an accuracy of 2-4 photons, and therefore provide accurate and computationally efficient tools for analyzing phase squeezed quantum M-PSK communication.

Mahdi Chehimi, Nour Dehaini, Nikos A. Mitsiou et al.

Future networks will need to make network-wide decisions, including traffic engineering, network slicing, and wireless optimization, under strict latency, energy, and reliability constraints. The computational complexity of these problems increasingly challenges classical optimization methods. This article proposes Q-Backbone (QB), a quantum-enhanced control plane for communication networks in which quantum processing units (QPUs) operate alongside classical computing resources as accelerators for network intelligence. QB is designed as a fourlayer architecture that combines heterogeneous infrastructure, hybrid quantum-classical runtime services, policy-driven task orchestration, and communication-network applications. A central component of QB is the Quantum Invocation Policy (QIP), which dynamically determines when quantum acceleration is beneficial and when classical execution should be preferred. A case study on deadline-aware orchestration of distributed quantum jobs over heterogeneous QPUs shows that QB can improve workload execution under tight deadline constraints, serving up to 25% more jobs than existing quantum-cloud scheduling baselines. Finally, open challenges and opportunities towards the deployment of QB are highlighted and discussed.