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Today’s issue includes:

• The first successful demonstration of drone-based quantum key distribution enables secure quantum communications between a drone and a ground station over a 200-meter distance.

• Higher order quantum reservoir computing is explored as a method for modeling complex, nonlinear dynamical systems like sea surface temperature.

• A more efficient quantum algorithm for portfolio optimization uses a digitized counterdiabatic quantum optimization approach, specifically designed for application in the impulse regime.

QUANTUM APPLICATION HEADLINES

🚁 Drone-Based Quantum Key Distribution Achieves Secure 200-Meter Link Between Air and Ground

APPLICATION: A study led by Nanjing University and Xin Lian Technology presents the first successful demonstration of drone-based quantum key distribution, enabling secure quantum communications between a drone and a ground station over a 200-meter distance.

SIGNIFICANCE: As secure communications increasingly rely on quantum technology, mobile QKD may provide a valuable upgrade to data securitythrough secure links across air, land, and sea. This experiment demonstrates the possibility of creating secure, real-time quantum communication links via mobile platforms like drones, extending secure quantum networks into mobile and flexible environments as well as providing an example of how to create a broad-area quantum network.

HOW: The drone-based QKD system uses polarization-coded photons generated by an onboard QKD module, paired with a ground-based receiver module. Using an acquisition, pointing, and tracking system, the drone establishes a stable optical link for real-time data exchange. The setup includes two polarization-maintaining fibers and a system that encodes and tracks polarization to counter motion effects, achieving high fidelity and low signal loss in both daytime and nighttime conditions.

BY THE NUMBERS:

• 30 kg – Total takeoff weight of the homemade drone, including QKD and APT systems.

• 8.48 kHz – Average secret key rate during a 400-second trial.

• 9 dB – Measured link loss between the drone and ground station.

• 200 m – Distance over which secure QKD was achieved.

⛈️ Quantum Reservoir Computing Performs Efficient Real-Time Forecasting for Climate and Environmental Systems

APPLICATION: A research team from the Indian Institute of Technology in Delhi and Pennsylvania State University explores higher order quantum reservoir computing as a method for modeling complex, nonlinear dynamical systems like sea surface temperature.

SIGNIFICANCE: HQRC can use interconnected quantum reservoirs to achieve effective forecasting and data-driven predictions, handling data forecasting with reduced training time and memory requirements compared to traditional machine learning models such as LSTM and GRU networks. The technique could provide a method for accurate real-time forecasting in fields requiring high-dimensional data analysis, such as climate science and fluid dynamics. Its potential for scalability and stability could support more efficient solutions for temporal forecasting, an advantage in computational fluid dynamics and environmental monitoring.

HOW: HQRC organizes an ensemble of quantum systems as computational nodes, with linear feedback connections that refine temporal data processing. It operates in three phases: washout, training, and evaluation. After initializing states and training on historical SST data, HQRC captures the dynamics of SST through nonlinear quantum evolution, achieving stable and accurate predictions. The model is tested on weekly National Oceanic and Atmospheric Administration sea surface temperature datasets, outperforming traditional methods in stability and computational efficiency.

BY THE NUMBERS:

• 300 time steps – Forecasting range used to evaluate HQRC’s accuracy over time.

• 22.56 seconds – Average training time for HQRC, significantly faster than traditional ML models (770.86 for LSTM and 788.34 for GRU)

• 107.94 MB – Memory consumption of HQRC, far lower than LSTM (1179.01) and GRU (989.86) models.

• 0.639 RMSE – HQRC’s error rate on test data, showing improved accuracy over LSTM, GRU, and ESN models (1.095,1.374, and 1.770 respectively).

💲 DCQO for Faster, Efficient Portfolio Optimization on Near-Term Quantum Computers

APPLICATION:  A recent study from Kipu Quantum presents a faster and more efficient quantum algorithm for portfolio optimization using a digitized counterdiabatic quantum optimization approach, specifically designed for application in the impulse regime. Tested on IonQ’s trapped-ion quantum computer, the algorithm optimizes asset allocation in a portfolio by balancing returns against risk with a reduced circuit depth, suitable for current NISQ processors.

SIGNIFICANCE: Quantum techniques to financial problems, such as portfolio optimization, are useful for real-world applications, as they offer the potential to handle complex, high-dimensional data. The proposed DCQO technique not only reduces computational demands but also shows improved solution accuracy over traditional quantum algorithms. This work could extend beyond finance, impacting any field requiring combinatorial optimization.

HOW: The algorithm uses the impulse regime—a phase where rapid adjustments are used to control a quantum system efficiently, allowing it to reach results quickly without needing slow, careful changes. Using a reduced number of quantum gates, the approach discretizes evolution time to minimize circuit depth and error rates. A hybrid quantum-classical version of the algorithm (h-DCQO) further reduces reliance on quantum circuits, making it more possible on near-term quantum devices.

BY THE NUMBERS:

• 20 qubits – Number of qubits used in tests on IonQ’s quantum computer.

• 2.5-40x – Reduction in circuit depth compared to other quantum optimization methods.

• 3 regimes – The algorithm operates in impulse, intermediate, and adiabatic regimes, with impulse yielding the most efficient results.

RESEARCH HIGHLIGHTS

🤖 A collaboration of scientists, including those from NVIDIA, IBM Research, an Wells Fargo, introduce a method to enhance variational quantum circuits by integrating pre-trained neural networks, addressing qubit limitations in quantum machine learning. By fixing the neural network's parameters, it improves both the representation and generalization capabilities of VQCs, shown through theoretical analysis and experiments in quantum dot classification and human genome data.

💡 A team from the Chinese Academy of Sciences presents a fiber array architecture for neutral atom quantum computing, which achieves independent control of individual qubits in a scalable 2D array. Using a fiber array, the team demonstrated trapping and single-qubit gate operations on ten rubidium-87 atoms with high fidelity, providing a scalable approach for executing quantum algorithms with efficiency.

🕰️ Researchers from the University of Tokyo, RIKEN, and Keio University introduce a feedback-driven quantum reservoir computing framework that enables short-term memory retention and effective time-series prediction, especially in chaotic systems. The results show improved performance near the "edge of chaos" and demonstrate potential for QRC in quantum machine learning and predictive applications.

NEWS QUICK BYTES

Atlantic Quantum, in partnership with MIT's Kevin P. O'Brien, has received a $1.8 million STTR Phase II grant from AFWERX to develop scalable superconducting quantum computers for the Department of the Air Force, intended for national defense. The project will integrate projects from MIT’s Quantum Coherent Electronics group, including developments in quantum-limited amplifiers for improved processor readout.

South Korean researchers have developed a photonic integrated quantum circuit chip capable of controlling eight photons, enabling studies of quantum phenomena like multipartite entanglement. Led by ETRI, in collaboration with KAIST and the University of Trento, the team achieved 6-qubit entanglement on a silicon-photonic chip and demonstrated the scalability of photonic quantum systems. With future plans to create 16- and 32-qubit chips, ETRI intends to further develop quantum hardware for cloud-based quantum computing.

Quantinuum announced their new quantum error correction decoder toolkit, which provides users with real-time syndrome decoding and error correction. Using real-time hybrid compute with Web Assembly (Wasm), the toolkit enables classical-quantum interactions for complex algorithms and supports any QEC code. It includes three core use cases, showcasing flexible, practical applications for building better decoders and understanding fault tolerance on real hardware.

Henna Virkkunen, the EU's proposed commissioner for tech sovereignty, is advocating for a "Quantum Act" to unify fragmented quantum research efforts across member states, with the intention to consolidate Europe's strength in quantum technology. This act would establish a cohesive framewor to boost EU innovation and address the fragmented efforts currently led by individual nations. The initiative aligns with broader goals for EU tech sovereignty, emphasizing both dual-use research funding and supercomputing access for startups and researchers.

UBC Science appointed two new Canada Research Chairs in quantum computing and metabolomics as part of a $452 million federal investment to advance critical research fields. Dr. Daochen Wang will focus on applying quantum computing to real-world challenges, while Dr. Tao Huan will explore metabolomics to understand biological systems and environmental exposures.

QUANTUM MEDIA

LISTEN

In this episode of the Saad Truth, host Dr. Saad is joined by Dr. David Deutsh. The two discuss quantum computing, Turing machines, multiverses, interdisciplinarity, the state of academia, and more.

WATCH

Physicist Sean Carroll simplifies the complex ideas of modern physics, including the quantum revolution and the minds behind it:

THAT’S A WRAP.