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Cierra

Today’s issue includes:

• A FL-QDSNN framework integrates quantum dynamic spiking neural networks with federated learning to improve distributed learning and enhance privacy.

• The graph quantum walk transformer integrates quantum computing methodologies into graph neural networks for improved graph representation learning.

• The generator coordinate inspired method integrates hybrid quantum-classical approaches to overcome challenges in quantum chemistry simulations as an optimization-free alternative to traditional variational quantum eigensolver techniques.

QUANTUM APPLICATION HEADLINES

🔒️ Quantum Spiking Meets Federated Learning for Privacy and Scalability

APPLICATION: Scientists from eBRAIN Lab and the NYUAD Research Institute develop an FL-QDSNN framework that integrates quantum dynamic spiking neural networks with federated learning to improve distributed learning and enhance privacy, scalability, and adaptability in environments where data distributions vary significantly.

SIGNIFICANCE: As technology advances, the growing reliance on distributed systems for tasks like machine learning has brought privacy concerns to top-of-mind. Sensitive data, such as medical records or financial transactions, must often be shared across multiple locations to train effective models, raising the risk of data breaches and unauthorized access. Additionally, data distribution challenges arise when datasets are not evenly or identically distributed across nodes, as is often the case in real-world scenarios like hospitals collecting patient data with varying demographics or businesses operating in diverse markets. These non-identically distributed datasets can lead to biases or degraded model performance, complicating the learning process. Integrating quantum mechanics with neural network technologies may provide a solution. The FL-QDSNNs framework addresses performance variability, hardware constraints, and training complexity in quantum systems as well as achieves improved accuracy and efficiency in distributed scenarios.

HOW: The FL-QDSNNs framework uses several innovative techniques that improve upon distributed quantum learning. Central to its operation is a dynamic threshold mechanism that adjusts quantum gate activations in response to evolving data distributions, enabling the network to adapt to complex and variable datasets. Federated learning plays a key role by distributing computations across multiple clients, ensuring that privacy is preserved while leveraging localized data processing. Each client uses quantum spiking dynamics during local training, mimicking biological neuron behavior through a quantum-inspired spiking mechanism. After local updates, the results are aggregated centrally to refine a global model. This iterative process is designed to maximize learning efficiency while maintaining high levels of data security. The framework was tested on benchmark datasets, such as Iris, digits, and breast cancer, to validate the framework's scalability and ability to generalize across diverse scenarios.

BY THE NUMBERS:

• 94% — Accuracy achieved on the Iris dataset, outperforming existing quantum federated learning methods.

• 3 datasets — Benchmarks include Iris, digits, and breast cancer datasets.

• 5-25 clients — Scalability tested with varied client numbers to optimize accuracy and efficiency.

• 100 iterations — Required for training convergence on all datasets.

📈 Quantum Walks Meet Graph Neural Networks for Graph Learning

APPLICATION: Researchers at Zhejiang Lab developed the graph quantum walk transformer (GQWformer) which integrates quantum computing methodologies into graph neural networks for improved graph representation learning, and addresses challenges in graph classification tasks by incorporating quantum walks to encode structural and contextual information.

SIGNIFICANCE: Graph Transformers excel at capturing long-range dependencies, which refer to relationships or interactions between nodes in a graph that are far apart in terms of the graph's structure. However, they often neglect local structural nuances in graph data. GQWformer bridges this gap by using quantum walks to provide inductive bias, providing a better balance between global and local information. This approach increases performance in domains like biology, chemistry, and social networks, and effectively introduces a way to obtain more accurate graph-based predictions in complex datasets.

HOW: GQWformer combines quantum walk encoding with transformer and recurrent modules to capture comprehensive graph representations. Quantum walks generate dynamic structural encodings sensitive to both topology and attributes, which are integrated as attention biases in the transformer’s self-attention mechanism. A recurrent module processes quantum walk sequences to preserve temporal and directional dependencies, further refining the node representations. This combination ensures the model effectively captures both global structures and local details, enabling robust graph classification. Testing on benchmarks such as MUTAG and PROTEINS demonstrated consistent performance gains over state-of-the-art methods.

BY THE NUMBERS:

• 95.2% — Accuracy on MUTAG, a 0.5% improvement over the previous best.

• 5 datasets — Benchmarks include biology, chemistry, and social network data.

• 4 blocks — GQWformer architecture incorporates four graph quantum walk transformer blocks for optimal performance.

• 76.7% — Accuracy on the PTC dataset, with an improvement of 2.3% over baselines.

⚛️ Optimization-Free GCIM Redefines Simulation Efficiency in Quantum Chemistry

APPLICATION: A team from Pacific Northwest Laboratory, the University of Washington, and others present the generator coordinate inspired method, which integrates hybrid quantum-classical approaches to overcome challenges in quantum chemistry simulations as an optimization-free alternative to traditional variational quantum eigensolver techniques. This method simplifies quantum circuit requirements while enabling accurate energy computations for strongly correlated molecular systems. 

SIGNIFICANCE: Quantum chemistry relies on accurate computation of ground and excited states to understand molecular and material properties. Traditional wavefunction-based methods often face scalability and accuracy challenges in strongly correlated systems. GCIM bypasses these hurdles by constructing non-orthogonal many-body bases from Unitary Coupled Cluster excitation generators—mathematical operators used in quantum chemistry to describe electronic excitations within molecules. This allows for efficient subspace expansion and reduced computational overhead, which may lead to more accurate quantum simulations in areas such as catalysis, sensors, and quantum materials.

HOW: GCIM uses UCC excitation generators to develop an adaptive, automated subspace expansion framework. This strategy projects system Hamiltonians into effective Hamiltonians, sidestepping issues like barren plateaus commonly encountered in VQE methods. The method includes gradient-based adaptive selection of basis sets, combining subspace expansion with ansatz optimization. Tested on molecular systems such as H4 and H6, GCIM demonstrated faster convergence and improved scalability compared to traditional VQE, significantly reducing simulation time without compromising accuracy.

BY THE NUMBERS:

• 10 seconds — Time required by GCIM to compute ground states for strongly correlated H6 systems, compared to hours for VQE.

• 20% — Reduction in computational complexity by automating basis selection.

• 2 configurations — Basis expansion per iteration, ensuring robust convergence.

• 1 optimization-free framework — Eliminates the need for classical parameter tuning, enhancing scalability for larger molecular systems.

RESEARCH HIGHLIGHTS

🔪 A team from the University of California, Rensselaer Polytechnic Institute, AWS Quantum Technologies, and others introduces CaliScalpel, a framework designed for in-situ and fine-grained qubit calibration integrated with surface-code-based quantum error correction. The framework addresses error drift in quantum systems, which undermines computational reliability during extended operations. Results show that it reduces retry risks by up to 85% compared to traditional methods, potentially enabling more reliable long-term quantum computation.

🤖 KAIST, National Synchrotron Radiation Research Center, and National Center for High-Performance Computing researchers introduce quantum pointwise convolution, a hybrid neural network architecture that integrates quantum circuits into convolutional layers for enhanced feature extraction. It addresses limitations in classical pointwise convolution by leveraging quantum entanglement and amplitude encoding to capture complex feature relationships efficiently. Evaluated on the Fashion-MNIST and CIFAR10 datasets, the quantum-enhanced models demonstrated improved accuracy and reduced parameter requirements compared to classical counterparts.

🧠 A study from SAP AI and Indiana University implements a quantum version of a perceptron, a foundational unit of neural networks, and demonstrates its ability to classify patterns efficiently. The quantum perceptron uses quantum gates and superposition to process inputs and weights, achieving faster convergence compared to classical perceptrons. Results show that even a single quantum perceptron can classify simple patterns and highlights the potential for developing more complex quantum networks in the future.

NEWS QUICK BYTES

👩‍💻 Entropica Labs and Xanadu have partnered to advance fault-tolerant quantum computing by integrating Xanadu's quantum software tools, PennyLane and Catalyst, with Entropica's EKA, a data structure for quantum error correction. The collaboration intends to optimize qubit utilization, reducing logical qubit overhead and simplifying the error correction process, a critical step toward scalable and accessible quantum computing.

🔬 The University of Missouri has established the state's first IBM Quantum Innovation Center, joining the IBM Quantum Network to access advanced quantum computing via IBM’s cloud-based platform. This initiative, led by Mizzou’s Colleges of Arts and Science and Engineering, intends to support research in areas such as energy, artificial intelligence, quantum programming, and computational biophysics while preparing students and faculty to innovate in the quantum field. The partnership also provides access to IBM's quantum systems, Qiskit software, and educational resources.

🤝 Jij Inc. and ORCA Computing have formed a strategic Japan-UK partnership to advance photonic quantum computing, combining Jij's algorithm development expertise with ORCA's room-temperature quantum systems. This collaboration focuses on industrial applications like logistics, energy planning, and manufacturing optimization, aiming to address classical computational limitations.

💰️ The University of Tennessee at Chattanooga (UTC) has received $3.5 million from NIST to establish a Quantum Center focused on infrastructure, education, use case-driven R&D, and business development for quantum technology. The center will expand quantum education with new degrees and outreach to K-12 and underserved communities while advancing research in quantum computing, sensing, and networking with applications in energy, urban systems, and smart cities.

✨ Equal1 has announced a development in silicon-powered quantum computing, achieving world-leading metrics for a silicon qubit array with gate fidelities of 99.4% for single qubits and 98.4% for two-qubit gates, along with gate speeds of 84ns and 72ns, respectively. They also unveiled the first multi-tile Quantum Controller Chip, operating at 300 millikelvin with AI-enabled Qubit Adaptive Error Correction, integrated with Arm Cortex processors.

🌴 The 8th edition of Q2B Silicon Valley, hosted by QC Ware, will take place from December 10–12, 2024, focusing on “The Roadmap to Quantum Value.” Featuring industry leaders, academics, and startups, the event will cover advancements in quantum computing hardware, software, error correction, and commercialization. Highlights include a Quantum Hackathon, a startup pitch competition, and keynotes from figures like John Preskill and Scott Aaronson.

QUANTUM MEDIA

WATCH

Quantum constraint satisfaction problems explore computational and physical insights through frameworks like the Local Hamiltonian problem and Nonlocal Games, revealing complexities in quantum systems. This talk discusses their divergence, recent advances, and open problems, with the intention to unify these quantum notions.

THAT’S A WRAP.