- 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
- Quantum Machine Learning Achieves Bayesian Inference with 32 Qubits
- Quantum Diffusion Models Achieve 76% Improvement in Earth Observation Data Synthesis
- Quantum Kernels Achieve Enhanced Classification in Radial Basis Function Networks
- Quantum Machine Learning Achieves Effective Unlearning across Iris, MNIST, and Fashion-MNIST Datasets
- Surface Hopping Advances Nonadiabatic Dynamics, Enabling Benchmarking of Photochemical Processes
- Deepquantum Achieves Closed-Loop Integration of Three Quantum Computing Paradigms
- Foundation Model Achieves Unified Characterization of Complex Optical Quantum States
- Quantum Machine Learning Achieves Cloud Cover Prediction Matching Classical Neural Networks
- Gradient-enabled Pre-Training Achieves Scalable Quantum Circuit Training Beyond Classical Simulation
- Function Representability Framework Enables Quantum Advantage in Machine Learning
- Quantum Canaries Detect Privacy Vulnerabilities in Models Trained on Sensitive Data
- Accurate Carbon Dioxide Line Lists with 12 Isotopologues Enable Improved Radiative Transfer Codes
- Quanvolutional Neural Networks Achieve Multi-Task Peak-Finding for Complex Molecular Spectra
- Predicting SWCNT Bundle Thermal Conductivity Enables New Materials Design with Machine Learning
- Quantum Language Models Achieve Generative Performance on Real Hardware
- Quantum Entanglement with Machine Learning Enables High-precision Rényi Entropy Estimates for Large Three-dimensional Lattices
- Machine Learning Discovers Minimal Representations of Fermionic Ground States in -site Models with Latent Dimensions
- FRQI Pairs Method Using Quantum Recurrent Neural Networks Reduces Image Classification Complexity
- Quantum Kernels with Multimode Bulk Acoustic Resonators Demonstrate Enhanced Computational Efficiency
- Machine Learning Optimizes BEGe Detector Event Selection, Achieving Efficiency for 10 keV Radiation Detection
- Graph-based Bayesian Optimization Discovers Variational Quantum Circuits for Cybersecurity Data Analysis
- Quantum Algorithms Efficiently Extract Viscosity Solutions to Nonlinear Hamilton-Jacobi Equations Via Entropy Penalisation
- Atomistic Analysis Reveals Hidden Structural Variants in NbN Superconducting Trilayers Limiting Circuit Performance
- Photonic Quantum-Accelerated Machine Learning Achieves Robust Performance with Twenty Times Less Training Data
- Machine Learning Corrects 2.4km Free-Space Optical Link Wavefront Errors, Reducing Phase Error Variance by 2/3
- Pvls: Machine Learning Predicts Quantum Linear Solver Parameters, Achieving 2.6x Optimisation Speedup
- Quantum Resources of Fluid Dynamics Data Assess Computational Complexity in Shear Flow Simulations
- Quantum Topological Graph Neural Networks Detect Complex Fraud, Ensuring Stable Training on Noisy Intermediate-Scale Quantum Devices
- Spiking-quantum Convolutional Neural Network Achieves 86% Accuracy with 0.5% Data Re-upload and Joint Training
- Quantum Vanguard: Federated Intelligence with Quantum Key Distribution Maintains Accuracy across Vehicle Datasets
- Boltzmann Machines Unlock Parallelizable Sampling with Langevin SB, Maintaining Accuracy Comparable to Markov Chain Monte Carlo
- Evolved Quantum Boltzmann Machines Enable Efficient Generative Modeling of Complex Probability Distributions for Challenging Simulations
- Time-series Forecasting Utilizes Multiphoton Quantum States and Integrated Photonics for Reconfigurable, Adaptive Processing
- Quantum Classifiers Benefit from Reduced Representations, Achieving 40% Performance Improvement with Sinkclass Autoencoders
- Tensor Ring Decomposition Achieves Exact Solutions with a Deterministic, Finite-Step Algorithm
- 2d Materials: Exchange-Correlation Functionals Predict Structural, Optoelectronic, Magnetic, and Thermal Properties
- Machine Learning Model Recreates Solvated Electron Chemistry with 3.2 Kcal Mol Activation Energy
- Machine-learned Potentials Model Self-Assembling Topological Solitons, Enabling Large-Scale Liquid Crystal Simulations
- Graphene Quantum Hall Devices Exhibit Scale-Dependent Carrier Density Variation below 400 Um
- Quantum Learning Intrinsically Preserves Plasticity over Long Timescales, Maintaining Consistent Capabilities across Diverse Data Modalities
- Machine Learning Navigates Quantum Entropy Vector Space, Generating Ingleton-Violating States with Tunable Violation
- Quantum Machine Learning Overview Highlights Tensions and Nuances in Emerging Field
- Sandia Lab Uses Machine Learning to Improve Quantum Computing
- Optimus-q: Federated Learning and Quantum Cryptography Enable Adaptive Robots for Nuclear Plant Monitoring
- Quantum Machine Learning Framework Achieves 95% Confidence in Double Higgs Searches with 10% and 50% Uncertainty Control
- Qsentry Detects Quantum Neural Network Backdoors with 93.2% Accuracy, Even at 1% Poisoning Rates
- Quantum Machine Learning Achieves 93% Adversarial Robustness Against Attacks across Diverse Datasets and Threat Models
