Abstract NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.Abstract NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.

arXiv:1709.05381Quantum Science and TechnologyFeb 8, 201816 citations

Using quantum computing algorithms in future satellite communication

László Bacsárdi

Acta AstronauticaJun 2, 200520 citations

Quantum-Enhanced Attention for Federated Resource Allocation in LEO Satellite Networks

Muhammad Ahsan Nazir, Ming Chen, Abdul Aziz +2 more

Dynamic Radio Resource Allocation (RRA) in Low Earth Orbit-Space Integrated Ground Networks (LEOSIGNs) presents unprecedented challenges due to extreme dynamism, sparse resource availability, and heterogeneous quality-of-service requirements across satellite constellations like Starlink, OneWeb, and Kuiper. This paper introduces a paradigmshifting Quantum-Optimized Deep Learning with Federated Edge Intelligence (QO-FEDI) framework that synergistically integrates: (1) Variational Quantum Circuits (VQC) for decomposing the NP-hard RRA problem into parametric quantum optimization, (2) federated attention-based predictive models across satellite clusters for privacy-preserving demand forecasting, and (3) adaptive edge-compute resource coordination for sub- 100 ms allocation latency. Novel contributions include: mathematical formulation of quantum cost Hamiltonians capturing interference constraints as entanglement patterns, federated learning topology with Byzantine-robust aggregation, and spatio-temporal attention mechanisms for orbital trajectory-aware predictions. Extensive evaluations across 15 realistic LEO scenarios with varying satellite configurations (32-256 satellites), traffic patterns ( $1-25 \text{Gbps}$ ), and environmental conditions demonstrate: (1 $) 71 \%$ spectrum efficiency improvement (reaching $6.2 \text{bps} / \text{Hz}$ ), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to $\mathbf{1 0 0 0 +}$ satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.Dynamic Radio Resource Allocation (RRA) in Low Earth Orbit-Space Integrated Ground Networks (LEOSIGNs) presents unprecedented challenges due to extreme dynamism, sparse resource availability, and heterogeneous quality-of-service requirements across satellite constellations like Starlink, OneWeb, and Kuiper. This paper introduces a paradigmshifting Quantum-Optimized Deep Learning with Federated Edge Intelligence (QO-FEDI) framework that synergistically integrates: (1) Variational Quantum Circuits (VQC) for decomposing the NP-hard RRA problem into parametric quantum optimization, (2) federated attention-based predictive models across satellite clusters for privacy-preserving demand forecasting, and (3) adaptive edge-compute resource coordination for sub- 100 ms allocation latency. Novel contributions include: mathematical formulation of quantum cost Hamiltonians capturing interference constraints as entanglement patterns, federated learning topology with Byzantine-robust aggregation, and spatio-temporal attention mechanisms for orbital trajectory-aware predictions. Extensive evaluations across 15 realistic LEO scenarios with varying satellite configurations (32-256 satellites), traffic patterns ($1-25 \text{Gbps}$), and environmental conditions demonstrate: (1$) 71 \%$spectrum efficiency improvement (reaching$6.2 \text{bps} / \text{Hz}$), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to$\mathbf{1 0 0 0 +}$satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.

International Conference on Communications and Broadband Networking (ICCBN)Nov 27, 20250 citations

Research of An Improved Satellite-based Quantum Positioning System

Wei Fan

International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE)Jan 1, 20190 citations

Satellite QKD developments for secure communications

Agnes Ferenczi, Stefanie Häusler, Davide Orsucci +2 more

Quantum key distribution (QKD), the technology to generate distant secure keys for later usage in secure communication, has become a mature topic since the 1980s. While terrestrial QKD over fiber networks is suitable for short-distance urban networks, QKD via satellites promises to gap the distance requirement on a global scale that limits terrestrial QKD. Satellite QKD requires the exchange of quantum states between a satellite and multiple ground stations. The goal is then to facilitate shared secret keys between the ground stations, which can then be relayed to users near the ground stations. The satellite usually carries the quantum sources as its payload and sends them to the ground station via a downlink. The QKD protocol is heavily influenced by the choice of the quantum source, with typically one of two types: approximate single photon source (such as a weak laser) or an entangled source. Similarly, the ground station must be equipped to receive, measure and process the quantum signals sent by the satellite. Although there are also reverse schemes, where the satellite acts as the receiver and the ground station as the sender, these schemes are less explored due to higher noise in the uplink and less engineering experience. As with any system containing a high heterogeneity, many different implementation schemes lead to a variety of optimized use cases based on different criteria. Satellite QKD carries the further complication that it must be adapted to international standards. In this paper we present different satellite and ground station architectures that lean on the developments in the projects EAGLE-1, QUBE, QuNET, RoGloQuaN, addressing different protocols, sources, satellite sizes and telescope sizes. We also discuss the need for agility and configuration of components in a heterogeneous environment.Quantum key distribution (QKD), the technology to generate distant secure keys for later usage in secure communication, has become a mature topic since the 1980s. While terrestrial QKD over fiber networks is suitable for short-distance urban networks, QKD via satellites promises to gap the distance requirement on a global scale that limits terrestrial QKD. Satellite QKD requires the exchange of quantum states between a satellite and multiple ground stations. The goal is then to facilitate shared secret keys between the ground stations, which can then be relayed to users near the ground stations. The satellite usually carries the quantum sources as its payload and sends them to the ground station via a downlink. The QKD protocol is heavily influenced by the choice of the quantum source, with typically one of two types: approximate single photon source (such as a weak laser) or an entangled source. Similarly, the ground station must be equipped to receive, measure and process the quantum signals sent by the satellite. Although there are also reverse schemes, where the satellite acts as the receiver and the ground station as the sender, these schemes are less explored due to higher noise in the uplink and less engineering experience. As with any system containing a high heterogeneity, many different implementation schemes lead to a variety of optimized use cases based on different criteria. Satellite QKD carries the further complication that it must be adapted to international standards. In this paper we present different satellite and ground station architectures that lean on the developments in the projects EAGLE-1, QUBE, QuNET, RoGloQuaN, addressing different protocols, sources, satellite sizes and telescope sizes. We also discuss the need for agility and configuration of components in a heterogeneous environment.

SPIE Quantum Technologies for Defence and SecurityOct 29, 20251 citation

1-25 \text{Gbps}

), and environmental conditions demonstrate: (1

) 71 \%

spectrum efficiency improvement (reaching

6.2 \text{bps} / \text{Hz}

), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to

\mathbf{1 0 0 0 +}

satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.