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Cierra

Today’s issue includes:

• The EAGLE-1 mission is developing Europe’s first sovereign space-based quantum key distribution system.

• Researchers have proposed a new quantum data visualization technique based on quantum kernels.

• The LEP-QNN framework introduces a quantum neural network approach for predicting loan eligibility with improved predictive accuracy.

QUANTUM APPLICATION HEADLINES

🛰️ EAGLE-1: Europe’s First Space-Based Quantum Key Distribution System for Secure Global Communications

APPLICATION: The EAGLE-1 mission, led by the European Space Agency and SES, along with German Aerospace Center and other collaborators, is developing Europe’s first sovereign space-based quantum key distribution system. This mission includes a low Earth orbit satellite, advanced optical ground stations, and a quantum operational network for secure communication.

SIGNIFICANCE: The need for secure communication becomes increasingly relevant. As quantum computers continue to develop, their ability to break classical cryptographic systems poses a risk to sensitive information, from government secrets to financial transactions and personal data. Quantum key distribution provides a solution, ensuring that any attempt to intercept or tamper with the communication is detectable. EAGLE-1 is significant for Europe, as it establishes the continent's independence in quantum-secure communications. The mission supports the European Quantum Communication Infrastructure (EuroQCI), which has a goal to protect data sovereignty and provide secure communication channels across EU member states. By demonstrating a fully operational end-to-end QKD system, EAGLE-1 is not only a technological milestone but also a strategic initiative to mitigate cybersecurity threats posed by quantum technologies.

HOW: EAGLE-1's approach to secure quantum communication involves a dual-segment strategy, with innovative technologies in both space and ground components. In the space segment, the satellite's quantum key distribution transmitter generates and encodes quantum keys using the BB84 protocol. The transmitter converts these keys into single photons via electro-optical systems, which are then transmitted to ground stations. On the ground, the Optical Ground Station Oberpfaffenhofen (OGS-OP) has undergone upgrades to meet the demands of the EAGLE-1 mission. A newly installed 80 cm Nasmyth-Design telescope, equipped with adaptive optics, corrects for atmospheric turbulence that can distort incoming signals. These systems, including a high-order deformable mirror and tip-tilt mirror, optimize photon coupling efficiency into single-mode fibers for analysis and secure communication. The station supports bidirectional optical links for precise tracking and data transmission with the satellite in low Earth orbit.

BY THE NUMBERS:

• 2.25 Gbps – Final modulation rate of key bits generated by the QKD transmitter.

• 80 cm – Diameter of the Nasmyth-Design telescope at OGS-OP, optimized for high-precision optical communication.

• 2025-2026 – Expected launch date of the EAGLE-1 satellite via a Vega C rocket.

• 3+ years – Initial in-orbit validation phase, with potential mission extension.

📈 Visualizing Quantum States: Quantum Kernel t-SNE for High-Dimensional Data Analysis

APPLICATION: Researchers from Osaka University and RIKEN have proposed a new quantum data visualization technique based on quantum kernels, to reduce the complexity of high-dimensional quantum data representation.

SIGNIFICANCE: Quantum states are inherently high-dimensional and difficult to interpret directly, which creates challenges for understanding optimization behaviors in quantum algorithms. Visualizing these states enables researchers to identify patterns, trajectories, and issues in algorithmic processes like the variational quantum eigensolver. The proposed method is particularly relevant as quantum computing grows in complexity, where traditional classical visualization tools fall short. By offering efficient and accurate visualizations of quantum states, this technique helps improve quantum algorithm design, parameter optimization strategies, and insights into complex quantum behavior.

HOW: This visualization method uses quantum kernels, which compute inner products between quantum states to capture similarities. The kernel t-SNE algorithm then uses these similarities to map the high-dimensional quantum data onto a two-dimensional plane. The process avoids iterative quantum circuit optimizations, reducing computational overhead compared to prior methods. The technique was validated by visualizing quantum features of classical datasets, such as handwritten digits, and optimization trajectories in VQE applied to a transverse field Ising model.

BY THE NUMBERS:

• 15% less – Reduction in the number of quantum circuit executions required compared to prior visualization methods, improving efficiency.

• 3 unique trajectories – Distinct optimization paths visualized for the variational quantum eigensolver applied to the transverse field Ising model.

• 100 iterations – Steps of optimization performed to generate visualizations of VQE trajectories with kernel t-SNE.

💰️ LEP-QNN Upgrades Loan Eligibility Prediction with Quantum Neural Networks

APPLICATION: The LEP-QNN framework, developed by researchers from NYU Abu Dhabi and Hassan II University of Casablanca, introduces a quantum neural network approach for predicting loan eligibility with improved predictive accuracy and efficiency.

SIGNIFICANCE: Loan eligibility prediction is a challenge for financial institutions, often hindered by the non-linear and high-dimensional nature of financial data. Traditional machine learning models, while effective, are limited by classical computational constraints. LEP-QNN demonstrates how quantum neural networks can overcome these limitations, achieving 98% accuracy while mitigating overfitting. By improving predictive reliability and efficiency, this framework introduces quantum-driven advancements in financial analytics, promoting inclusivity and precision in financial services.

HOW: LEP-QNN uses a quantum neural network architecture that encodes classical loan eligibility data into quantum states using angle encoding. The network structure includes parameterized quantum gates arranged in multiple layers, enabling it to model complex data relationships effectively. The framework incorporates a dropout mechanism, which randomly deactivates quantum gates during training to prevent overfitting. To optimize performance, the researchers tested various algorithms, including Adam, RMSProp, and Adagrad, identifying Adam as the most effective. The framework was validated on a dataset with 614 training instances and 367 test instances, achieving high accuracy and resilience against quantum noise models like depolarizing and amplitude damping.

BY THE NUMBERS:

• 98% accuracy – Achieved by LEP-QNN in predicting loan eligibility.

• 6 qubits – Used in the quantum neural network for encoding financial data.

• 5 layers – Depth of the quantum neural network during training, capturing complex data interactions.

• 90%+ accuracy – Maintained under low-noise conditions across all quantum noise models tested.

RESEARCH HIGHLIGHTS

✅ An IBM Quantum team uses heavy-hex lattice architecture to integrate two topological quantum error-correcting codes—the 3CX surface code and the Bacon-Shor code—on the same device to achieve entangled logical qubits. This uses unused qubits and lattice surgery techniques to achieve fault-tolerant logical Bell states, verified by a fidelity of up to 94% with Bell's inequality violations.

💎 A collaboration between Harvard, MIT, Lightsync, and others explores an implementation of blind quantum computing—processing on a quantum server without revealing input, output, or computation details—using silicon-vacancy centers in nanophotonic diamond cavities to achieve secure and private quantum computation across distributed nodes. By experimentally demonstrating universal blind gates, including single- and two-qubit operations, this work provides a scalable and secure framework for using matter-based quantum systems in distributed quantum architectures.

🤖 Engineered by a team from the University of Electronic Science and Technology of China, the MLQM framework introduces a machine learning-based approach to optimize qubit mapping for quantum circuits, addressing inefficiencies in solver-based methods. Through data augmentation, prior knowledge of circuit features, and adaptive solver constraints, MLQM reduces search space, achieving up to 1.79× speedup in solving time and a 22% reduction in memory usage compared to state-of-the-art methods.

NEWS QUICK BYTES

💸 The National Quantum Initiative Reauthorization Act, introduced by a bipartisan group of U.S. senators, seeks to authorize $2.7 billion for quantum research and development from 2025 to 2029, extending the program to 2034. The bill emphasizes practical quantum applications, creating new quantum research centers and testbeds, while expanding agency involvement to include NIH and NASA.

🐈‍⬛ Alice & Bob released a white paper and roadmap outlining a five-step plan to build a universal, fault-tolerant quantum computer by 2030 using cat qubit technology. The roadmap milestones include mastering cat qubits, creating high-fidelity logical qubits, demonstrating fault-tolerant systems, enabling universal algorithms, and achieving quantum advantage in industrial applications.

🎊 The University of the Andes has introduced Colombia's first quantum computer, developed by SpinQ. Operating at room temperature using Nuclear Magnetic Resonance, the computer offers a hands-on way to explore quantum concepts, fostering interdisciplinary collaboration across physics, engineering, and computer science. Professors highlight its potential to tackle complex problems, simulate biological systems, and accelerate advancements in AI and post-quantum cryptography.

🧬 QuEra Computing's neutral-atom quantum computers have advanced three projects to Phase Two of Wellcome Leap’s Quantum for Bio Challenge, focusing on transformative healthcare applications. These projects include quantum-enhanced drug discovery for myotonic dystrophy, scalable quantum simulations for virtual screening, and quantum chemistry techniques for studying proteins linked to Alzheimer’s and Parkinson’s diseases.

🕊️ The Centre for Quantum and Society, in collaboration with Quantum Delta NL, has launched the Quantum for Good Challenges, a four-year initiative to develop quantum applications addressing societal issues ahead of the UN International Year of Quantum. The first challenge focuses on leveraging quantum sensing to detect microplastics, a global environmental threat, through a student-led competition in the Netherlands featuring workshops, coaching, and a hackathon.

QUANTUM MEDIA

LISTEN

On the most recent episode of the Post-Quantum World podcast, host Konstantinos Karagiannis is joinged by Bob Wold, co-founder and CEO at Quantum Rings. They discuss how tensor networks could enable the simulation of large-scale quantum circuits, such as those involving over 50 qubits, even on a laptop, potentially accelerating timelines for practical quantum computing.

THAT’S A WRAP.