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Cierra

Today’s issue includes:

• Quantum principles merge with classical transfer learning as a model for automated pneumonia detection from chest radiographs.

• A quantum multimodal neural network model for sentiment analysis integrates image and text data from social media posts.

• A quantum-assisted hierarchical deep generative model is used for simulating particle showers in calorimeters.

QUANTUM APPLICATION HEADLINES

🩺 Integrating Quantum and Classical Models for Pneumonia Detection in Chest X-Rays

APPLICATION: A study from Qausal AI integrates quantum principles with classical transfer learning approaches as a model for automated pneumonia detection from chest radiographs. The combination of classical deep learning methods with quantum neural networks may achieve higher accuracy in medical image classification.

SIGNIFICANCE: As noted in the study, pneumonia is one of the leading causes of mortality worldwide, especially in resource-limited settings. Early diagnosis is critical to saving lives but often hindered by the lack of trained radiologists. The authors propose hybrid classical-quantum machine learning as a solution to not only address data limitations but also enhance classification performance. Additionally, this is a framework that may have further relevance in healthcare applications where precision and reliability are paramount.

HOW: Classical transfer learning models like ResNet-18, AlexNet, Inception-V3, and VGG-16 were used to extract key features from chest radiograph images. Quantum neural network layers were then integrated into these models, creating hybrid architectures capable of advanced pattern recognition. By using variational quantum circuits and encoding data into quantum states through angle encoding, the system effectively captured complex data relationships. These quantum-enhanced features were combined with the classical model’s capabilities, resulting in a seamless hybrid framework. The approach was rigorously tested on a balanced dataset of 5,856 radiographs, demonstrating how quantum technology can improve performance in medical image classification.

BY THE NUMBERS:

• 97.79% accuracy – The hybrid AlexNet model significantly outperformed its classical counterpart, which achieved 95.42%. While seemingly small, this gain is critical in medical diagnostics, where even minor improvements can save lives.

• AUC of 0.99 – The “Area Under the Curve” score, nearing the perfect value of 1, indicates the model’s ability to distinguish between pneumonia-positive and pneumonia-negative cases.

• 6 qubits – The quantum model operated with six qubits, emphasizing its ability to perform meaningful computations on current, limited devices.

• 5,856 images – Total size of the dataset, balanced to ensure thorough and unbiased training and evaluation, necessary for deploying the model in real-world settings.

🤖 Quantum Multimodal Neural Network Integrates Text and Images for Efficient Sentiment Analysis

APPLICATION: A team from Beihang University and Australian National University present a quantum multimodal neural network model for sentiment analysis. The QMNN integrates image and text data to classify sentiment in social media posts, addressing the challenges of multimodal data processing with limited quantum hardware resources.

SIGNIFICANCE: Multimodal sentiment analysis is essential for understanding complex expressions in social media, where images and text often convey sentiments together. Classical methods struggle with scalability and interaction between modalities, whereas quantum computing offers unique capabilities like entanglement and exponential scaling. The QMNN demonstrates the potential to enhance feature extraction and fusion in low-dimensional data.

HOW: The QMNN processes data through a series of stages. Preprocessed images and text are encoded into quantum states using amplitude encoding. Unimodal features are extracted through quantum convolutional layers, which mimic classical convolution operations but with reduced parameters. These features are integrated in a multimodal feature fusion block that uses quantum entanglement to learn interactions between modalities. Finally, the fused data is optimized for sentiment classification through a quantum stochastic gradient descent algorithm, achieving notable performance with minimal qubits and parameters.

BY THE NUMBERS:

• 12 qubits – The number of qubits required for the QMNN model, demonstrating ability to be tested on current, limited devices.

• 66 parameters – The total number of parameters in the QMNN which was significantly lower than the millions required by classical methods, demonstrating the ability of QNNs to more effectively handle sparse data.

• 73.27% accuracy – Test accuracy achieved on the MVSA-Single dataset, outperforming classical baselines while using low-dimensional inputs.

⚛️ Quantum Assisted Model Accelerates Particle Shower Simulations for Calorimeters

APPLICATION: Researchers from the Perimeter Institute, the University of British Columbia, the National Research Council, and others introduce Calo4pQVAE, a quantum-assisted hierarchical deep generative model for simulating particle showers in calorimeters. The model integrates classical variational autoencoders with quantum annealing to address the computational bottlenecks of simulating high-energy particle collisions in calorimeters.

SIGNIFICANCE: Simulating particle interactions in calorimeters—detectors that measure the energy of particles—particularly for experiments like the High-Luminosity Large Hadron Collider, requires enormous computational resources, with current methods projected to demand millions of CPU-years annually. Calo4pQVAE presents a hybrid quantum-classical approach to reduce simulation times while maintaining fidelity. This framework represents a step toward leveraging quantum simulations for practical applications in high-energy physics, which may lead to faster and more efficient experimental analysis.

HOW: Calo4pQVAE combines a classical VAE with a 4-partite restricted Boltzmann machine (RBM) as its latent prior, conditioned by particle energy. The RBM topology mimics D-Wave’s Zephyr architecture, leveraging quantum annealing for efficient sampling. The hierarchical decoder reconstructs particle showers layer by layer, simulating physical propagation in calorimeters. Training involved classical optimization for 150 epochs on NVIDIA RTX A6000 GPUs, followed by quantum annealing on D-Wave’s Advantage2 prototype to generate synthetic showers. The framework achieves speedups compared to traditional Monte Carlo-based methods like Geant4.

BY THE NUMBERS:

• 6480 voxels per event – Data resolution, capturing energy distributions in a voxelized cylindrical detector. A voxel is a 3D equivalent of a pixel, representing a small volume in the detector. High voxel resolution allows detailed tracking of energy deposition patterns from particle interactions. 

• 100,000 events – Dataset used for training and validation, spanning energy ranges from 1 GeV to 1 TeV. Each event represents a simulated particle interaction in the detector. The dataset size ensures statistical reliability, while the energy range covers scenarios from low-energy physics to extreme high-energy collisions.

• 1399.48 FPD – Fréchet Physics Distance score, measuring the similarity between synthetic and real data. FPD is a metric for comparing the statistical distributions of generated and experimental data. A lower score indicates closer alignment, and the result suggests Calo4pQVAE's outputs are reasonably faithful to real simulations. 

• 500x faster – Calo4pQVAE’s GPU-based simulation speed compared to Geant4. This speedup, if accurate, highlights the potential of the hybrid approach to reduce computational demands. 

RESEARCH HIGHLIGHTS

🖥️ Scientists from Cisco’s Quantum Lab and the University of California focused on optimizing quantum communication within quantum data centers by addressing latency and qubit decoherence challenges in distributed quantum computing. The proposed compiler integrates techniques to parallelize cross-rack communications, minimize reconfigurations, and enhance communication efficiency using entanglement swapping and scheduling strategies.

📐 Researchers from the University of Sydney, the University of Bristol, and others demonstrate a quantum-enhanced method for simultaneous measurement of multiple physical parameters, such as position and momentum, by using backaction-evading grid states and novel number-phase states. The experiments show precision improvements over classical limits, achieving a metrological gain of up to 5.1 dB for position-momentum measurements and 3.1 dB for number-phase measurements.

💡 A team from the University of Ottawa, Hamburg University of Technology, and others presents a photonic method for engineering qubit-environment interactions using synthetic lattices to simulate arbitrary noise processes. By coupling qubits with a discrete lattice environment modeled through patterned metasurfaces, the team implemented various quantum noise channels, including phase flip and depolarization. The results demonstrate high accuracy and potential applications in quantum error correction, thermodynamics, and the study of open quantum systems.

NEWS QUICK BYTES

⚕️ China's first institute dedicated to quantum computing and medicine, the Hefei Institute of Quantum Computing and Data Medicine, has been established by Bengbu Medical University and Origin Quantum. The institute focuses on integrating domestic quantum computing technologies with medical datasets to advance applications like breast mammography detection and small-molecule drug development.

🤝 Pasqal has signed an MoU with Sungkyunkwan University’s Quantum Information Research Support Center to advance quantum computing in South Korea and globally. The partnership includes workforce training, use of Pasqal’s cloud platform for R&D, seminars on practical quantum applications, and community-building activities to strengthen the global quantum ecosystem.

💸 Haiqu and HSBC collaborated to encode the largest financial distributions to date on IBM QPUs using tensor network techniques. Their algorithm constructs shallow quantum circuits for data encoding, which are memory-efficient, scalable, and capable of approximating functions like Levy distributions significant in financial modeling​.

🔒️ AWS is transitioning to post-quantum cryptography to secure customer data against future threats from quantum computers capable of breaking current public-key cryptography. The migration will focus on encryption in transit, long-term root of trust for signing, and session-based authentication, leveraging newly standardized algorithms like ML-KEM and ML-DSA. AWS's phased approach includes integrating PQC into services and open-source tools.

QUANTUM MEDIA

LISTEN

Christopher Bishop narrates the journey of quantum computing from concept to logical qubits as detailed in Alice & Bob’s whitepaper “Think Inside The Box” in the first of four installments.

THAT’S A WRAP.