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Cierra

Today’s issue includes:

• A hybrid quantum–classical convolutional neural network is used for the early detection of Parkinson's disease through analysis of speech signals.

• Researchers developed adaptive quantum federated learning to improve vehicle license plate recognition in multi-drone surveillance networks.

• A quantum algorithm simulates the Fokker-Planck equation that describes a quantum radiation reaction.

QUANTUM APPLICATION HEADLINES

🎤 Hybrid Quantum AI for Early Parkinson’s Detection Through Voice Analysis

Note from the editor: The source article appears to have been translated and there are significant spelling and sentence structure errors throughout, though published with a reputable publisher (Springer Nature). However, I felt this study, if carried forward as hardware matures, has the potential for high impact, so I’ve done my best to translate what was done despite this.

APPLICATION: A team from Prince Sattam Bin Abdulaziz University uses a hybrid quantum–classical convolutional neural network for the early detection of Parkinson's disease through analysis of speech signals in order to improve accuracy and efficiency in diagnosing a condition often challenging to identify in its early stages.

SIGNIFICANCE: Parkinson's disease, a degenerative neurological disorder, impacts motor and non-motor functions. Early diagnosis can greatly improve patient outcomes. Traditional methods often lack precision and are resource-intensive. By integrating quantum computing with machine learning, the study addresses limitations in feature extraction and classification.

HOW: The study uses a dataset of 195 voice samples, including 147 from individuals with Parkinson's disease and 48 from healthy individuals, sourced from the UCI Machine Learning Repository. The data undergoes preprocessing with principal component analysis for dimensionality reduction, optimizing it for analysis and minimizing irrelevant information. The hybrid quantum–classical convolutional neural network integrates classical convolutional layers with quantum to provide more efficient feature mapping and faster processing. To further improve accuracy and efficiency, the study incorporates a gazelle optimization algorithm. Finally, the model's performance is benchmarked against traditional machine learning methods such as CNNs and KNNs, demonstrating increased accuracy and speed in detecting Parkinson's disease.

BY THE NUMBERS:

• 195 samples — Total voice recordings analyzed (147 Parkinson’s patients, 48 healthy individuals).

• 4 minutes — Estimated time for quantum processing, compared to an estimated number of years for classical computation.

• 98% — Average accuracy across various population sizes and iterations.

🚁 Adaptive Federated Learning Enhances License Plate Recognition in Multi-Drone Surveillance Networks

APPLICATION: Researchers from Sookmyung Women’s University, Ajou University, and others developed adaptive quantum federated learning to improve vehicle license plate recognition in multi-drone surveillance networks, using adaptive quantum neural networks to improve data processing and maintain performance under challenging conditions such as with complex data and unstable wireless channels.

SIGNIFICANCE: Surveillance drones can play a role in public safety, especially in scenarios requiring real-time identification of moving vehicles. Traditional federated learning models face limitations due to high communication demands and unreliable wireless channels. Through elements of quantum computing, AQFL reduces parameter utilization while maintaining learning accuracy.

HOW: In AQFL, each drone trains a local adaptive quantum neural network using collected data, such as license plate images. The AQNNs use a flexible architecture where they can adjust their depth to balance computational efficiency and learning accuracy. After local training, parameter data is transmitted to a central server using superposition coding to mitigate transmission losses. The central server aggregates these parameters to construct a global model, which is then redistributed to the drones for subsequent tasks. This process ensures resilience against data and communication variability while optimizing performance for large-scale surveillance.

BY THE NUMBERS:

• 6.5% improvement — Accuracy improvement demonstrated by AQFL over classical federated learning under challenging conditions (for example, non-iid data and varying SNR levels).

• 50% fewer parameters — Adaptive quantum neural networks used by AQFL require approximately half the parameters of classical neural networks.

• 15% gain in robustness — AQFL showed higher resilience compared to traditional federated learning in simulations with poor channel conditions (low SNR values).

• 50 drones — Hypothetical simulations modeled scenarios with up to 50 drones for patrolling and license plate recognition.

☢️ Quantum Computing Takes On Laser Physics: New Algorithm Models Electron Behavior in Extreme Fields

APPLICATION: Researchers from the GoLP/Instituto de Plasma e Fusão Nuclear have developed a quantum algorithm to simulate the Fokker-Planck equation that describes a quantum radiation reaction — the process of electrons losing energy through radiation when interacting with intense electromagnetic fields.

SIGNIFICANCE: Petawatt lasers are incredibly powerful, capable of producing a million billion watts of power in ultra-short pulses, allowing scientists to study extreme conditions that usually only exist around black holes or neutron stars. Several major facilities are coming online in 2024-2025 thanks to recent technological developments, providing researchers a tool to create and study these extreme conditions in controlled laboratory settings for the first time. As these next-generation petawatt laser facilities come online, understanding how particles behave in extreme electromagnetic fields is highly relevant. Traditional simulation methods struggle with the multi-scale nature of these interactions, where both quantum and classical effects matter. A quantum computing approach could enable more accurate modeling of these processes, with applications in laser-plasma physics, astrophysics, and fundamental physics. Additionally, this is one of the first applications of quantum computing to plasma physics involving quantum radiation processes, of which future quantum simulations of more complex laser-plasma interactions may come about.

HOW: The team used a hybrid quantum-classical algorithm called variational quantum imaginary time evolution. They developed a specialized quantum circuit architecture that can efficiently represent the electron distribution functions. The algorithm was benchmarked against classical Particle-In-Cell simulations and analytical solutions, showing good agreement in modeling how electron beams cool down through radiation emission.

BY THE NUMBERS:

• 6 qubits — Number of quantum bits used in their quantum circuit implementation

• 27 parameters — Number of variational parameters optimized in the quantum circuit

• 2024 — Current year with multiple petawatt laser facilities coming online (ELI, Apollon, CoReLS, FACET-II, LUXE, etc.)

RESEARCH HIGHLIGHTS

🔗 Researchers from Tsinghua University successfully transmitted quantum correlations between atoms and photons over a 12-kilometer fiber optic cable. Using a multiplexing technique that allowed them to handle 280 quantum states simultaneously, they were able to achieve transmission rates of 1.95 kHz during operation, much faster than previous approaches, relevant for building quantum repeaters that could eventually enable secure quantum communication networks spanning metropolitan distances.

🔬 A team from the University of Science and Technology of China and Xiamen University developed an improved quantum algorithm for simulating electron diffraction and scattering in transmission electron microscopy, which is computationally intensive using classical methods. The main improvement was reconstructing the phase-shifting quantum circuit using Walsh transforms to eliminate multi-controlled quantum gates, making it more suitable for real quantum hardware, while also introducing a truncation approach that reduces the number of required quantum gates by over 10x with only ~1% error.

🧭 Scientists from the Universidad de Chile and DEVCOM Army Research Laboratory developed an improved method for detecting spoofed electromagnetic signals (such as radar) by encoding them with quantum states. Using quantum state discrimination theory, they established fundamental limits for spoofing detection and demonstrated that using coherent quantum states could achieve optimal detection without requiring single-photon sources, making practical implementation more feasible. The team also showed that using squeezed quantum states could push detection probabilities close to 100%.

NEWS QUICK BYTES

👩‍⚖️ The Senate Energy and Natural Resources Committee has advanced a bipartisan $2.5 billion bill aimed at accelerating the Department of Energy's quantum information sciences research over the next five years. The Department of Energy Quantum Leadership Act of 2024, sponsored by Senators Durbin and Daines, focuses on quantum networking research, domestic foundry programs, and industry outreach. The bill's advancement during Congress's lame duck session could help maintain federal funding for quantum technology development, particularly as other quantum legislation like the National Quantum Initiative Reauthorization Act awaits House approval.

🌈 A recent WSJ article speaks on how the NATO-backed Italian startup called Ephos is leading the production of glass-based photonic quantum chips, making them more environmentally friendly and energy-efficient than traditional silicon-based or superconducting quantum chips since they can operate at room temperature. While quantum computing could potentially reduce global carbon emissions by 150 gigatons over 30 years and add $1.3 trillion in value to key industries, the technology is still in early development stages.

👩‍💻 Japan's NEDO (New Energy and Industrial Technology Development Organization) is launching a nationwide quantum computing competition in March 2025, offering 200 million yen (approximately $1.3 million) in prizes to develop solutions for societal challenges like natural disasters and aging populations. The contest is unique in that it's open to participants from all fields regardless of quantum computing experience, with NEDO providing free training to attract diverse talent and fresh perspectives, and final judging will take place in August 2026.

🚗 QuEra’s Yuval Boger speaks on the automotive industry and quantum computing, a combination that could impact everything from electric vehicle battery design and autonomous driving to manufacturing efficiency and traffic optimization. While many quantum computing applications are still in early research stages, major automakers like BMW and Volkswagen are already exploring its potential to solve complex problems like sensor placement and supply chain optimization. This technology could help create more efficient batteries, optimize vehicle designs, improve autonomous driving systems, and improve overall manufacturing processes.

🏫 Physics educators from across Europe have published new research showing that teaching quantum physics through two-state systems such as qubits is more effective than traditional historical approaches for helping students understand quantum concepts. The team demonstrated this effectiveness specifically around teaching quantum measurement processes to make modern quantum technologies more accessible to schoolchildren.

QUANTUM MEDIA

LISTEN

In the most recent episode of the Quantum Podcast with Jay Shah, D-Wave Vice President Murray Thom discusses how the company is helping customers implement quantum computing applications in production, particularly focusing on its practical uses in industry and potential impact on AI.

THAT’S A WRAP.