Abstract: Quantum Key Distribution (QKD) is a cryptographic solution that leverages the properties of quantum mechanics to be resistant and secure even against an attacker with unlimited computational power. Satellite-based links are important in QKD because they can reach distances that the best fiber systems cannot. However, links between satellites in Low Earth Orbit (LEO) […]
Abstract: This paper proposes an optimization model for the design of quantum communication infrastructures (QCI) with the aim of minimizing capital expenditure (CAPEX), focusing on the costs associated with the deployment of quantum links and trusted repeater nodes (TRNs). The model is formulated using an integer linear programming (ILP) program that incorporates physical and operational […]
Abstract: In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP’s computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a […]
Abstract: Automated and instantaneous stabilizing control of the quantum key decoder is the mandatory procedure, which guarantees real-time and high-speed differential-phase-shift (DPS) quantum key distribution (QKD) at low erroneous rate, and ensures the delay-line interferometric (DLI) decoding of the weak-coherent-pulse quantum bit-stream at a qualified and constant visibility. As the QKD system often suffers from […]
Abstract: Quantum computation offers the potential to solve certain problems with substantial speed advantages over classical computing, but its practical realization is hindered by significant challenges associated with error propagation and decoherence. Topological quantum computing addresses these issues by encoding qubits in particular two-dimensional topological condensed matter systems, effectively described by topological quantum field theories, […]
Abstract: Capsule Network (CapsNet) provides a richer hierarchical representation than conventional neural networks through vector-shaped capsules and inter-capsule routing. However, most CapsNet designs still rely on classic matrix-multiplication-based vote transformations, which limit the formation of the prediction vector to linear mappings in Euclidean space. In this paper, we propose the Hybrid Quantum Capsule Network (HQCapsNet), […]
Abstract: In this study, we investigated the Variational Quantum Eigensolver (VQE) application for the Ising model as a testbed model, in which we thoroughly delved into several optimizers, both classical and quantum, and analyzed the extent to which each of these methods would offer a benefit. We then investigated a new combinatorial optimization scheme, termed […]
Abstract: Topological superconductivity could host quasiparticles that are key candidates for fault tolerant quantum computation due to their immunity to noise as they obey non-Abelian exchange statistics. For example, in the case of Majorana Zero Modes (MZM), braiding enables two topologically protected quantum gates. While their direct manipulation in solid-state systems remains experimentally challenging, digital […]
Abstract: We develop an exact error probability framework for quantum free space optics (FSO) communication over atmospheric channels with turbulence and pointing errors under a physically admissible bounded transmissivity model. The proposed framework is grounded in the fundamental physical observation that, in a quantum optical channel, the random channel quantity is the transmissivity itself, namely, […]
Abstract: By simulating the selective focus characteristics of human visual attention, the attention network has greatly improved the model’s ability to capture key features, promoting significant progress in natural language processing, computer vision and other fields. As a typical attention mechanism, the Squeeze-and-Excitation (SE) module finely adjusts the feature representation of the neural network by […]