- Quantum Computers Accelerate Complex Physics Simulations Using Neural Networks
- Quantum Circuits Evolve Themselves to Find Lowest Energy States
- Quantum Computers Promise Faster Machine Learning Via Spectral Analysis
- Beijing University of Posts and Telecommunications Fuses Cross-View Information in Quantum Multiview Kernel Learning
- Quantum Machine Learning Models Become Smaller with Knowledge Distillation Techniques
- Quantum Machine Learning Gains Robustness with Shallower Circuits
- Quantum Circuits’ Performance Limits Now Explained by Observable Concentration
- New Method Learns Quantum States with Far Fewer Measurements
- Machine Learning Extends Quantum Data Processing Beyond Current Limits
- MicroCloud Hologram Advances Deployable Quantum Recurrent Neural Network Technology
- Models Represent XOR with 100% Accuracy
- Lockheed Martin Joins Xanadu in Advancing Foundational Quantum Machine Learning Theory
- AI Model Boosts Molecular Property Prediction Accuracy
- Kipu Quantum Demonstrates Quantum Feature Extraction for Improved Satellite Image Classification
- Machine Learning Clarifies Elusive Quantum States in Material
- Quandela Unveils MerLin, Reproducing 18 State-of-the-Art Photonic QML Models
- Entanglement Boosts Machine Learning of Quantum Systems
- AI Learns to Compute Quantum System Properties
- Efficient Quantum Data Encoding Cuts Circuit Complexity
- Secure Quantum Encryption Protects Data during Remote Neural Network Training and Use
- Integrated Quantum Systems Combine Sensing and Computation with Indefinite Order
- Helium Traces in Solar Flares Unlock Secrets of Space Weather Prediction
- Machine Learning Framework Systematically Benchmarks 18 Quantum Models for Practical Gains
- Machine Learning Models Now Better Capture Electrostatic Forces in Materials
- Shorter Quantum Circuits Unlock Faster Data Processing for Future Computers
- Quantum Neural Networks Gain Limitless Power to Model Any Function Accurately
- Atom-By-Atom Simulations of Nanoscale Devices Now Possible with Boosted Algorithms
- Quantum Machine Learning Gains First Robust Data Privacy Shield
- AI Health Models Leak Patient Data Despite Privacy Safeguards, Research Reveals
- New AI Sees in 3D with Remarkable Efficiency, Beating Rivals by Five Per Cent
- Quantum Algorithm Speeds up Complex Calculations to N²log₂N, a New Record
- Quantum Computing’s ‘barren Plateau’ Problem Now Understood for Complex Circuits
- AI Simulates Battery Chemistry to Unlock Faster-Charging Lithium Metal Power
- Shows Geoopt-Net Predicts B3lyp/tzvp-Level Molecular Geometries in One Forward Pass
- Shows QSVM Generalisation Bounds under Local Depolarising Noise for NISQ Devices
- Shows Hybrid Quantum Network Improves Earth Observation Data Classification with Multitask Learning
- CNN-Bilstm Shows 99.97% Accuracy Classifying Entanglement with 100 Samples
- Mmd- Hierarchy Achieves Full Quantum Discrimination with 1000 Samples
- Geodite Achieves Accurate Equivariant Interatomic Potentials Without Tensor Products
- Lumos Achieves Efficient Fluorescent Molecule Design with Data-Physics Driven Generative Frameworks
- Neural Networks Advance Hadronic Physics Via Data-Driven Quantum Model Selection
- Quantum Software Testing Advances Quality Assurance for Complex Systems
- Protocol Achieves Efficient Estimation of Multivariate Traces with Two Photons
- Berkeley Lab Develops Quantum-Machine Learning Model for Electron Behavior in Water
- Explainable AI Achieves 83.5% Accuracy with Quantized Active Ingredients and Boltzmann Machines
- Robust Quantum Machine Learning Achieves Increased Accuracy on MNIST and FMNIST Datasets
- So3lr Force Field Achieves Unprecedented Accuracy Matching DFT for 23 Bio-Relevant Molecules
- Larger Label Prediction Variance Demonstrated in Regression Quantum Neural Networks
- Realistic Assessment Enables Quantum Contribution to Hybrid Neural Network Architectures
- Distribution-guided Quantum Machine Unlearning Enables Targeted Forgetting of Training Data
- Machine Learning Enables Accurate Modeling of Quantum Dissipative Dynamics with Complex Networks
- Noise-resistant Qubit Control with Machine Learning Delivers over 90% Fidelity
- Advances in Crystal Structure Prediction Unlock Superconducting Hydride Stability at 150 GPa
- Quantum Generative Models Achieve Fluid Dynamics Simulations with 7-Dimensional Latent Space Compression
- Schrödinger AI Achieves Robust Generalization with a Unified Spectral-Dynamical Framework
- Polymer Research Advances with OPoly26, a 6.57 Million Data Point Benchmark Dataset
- Beyond-diagonal RIS Achieves Greater Wave Control, Enabling Next-Generation 6G Networks
- Taylor-based Algorithm Achieves Superior Accuracy for Generative AI’s Matrix Exponential
- Nuclear Mass Predictions Achieve Improved Accuracy with Quantum-inspired Bayesian Algorithm
- Machine Learning Molecular Dynamics Advances Thermal Modelling of Graphene Oxide
