- 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
- Fermionic Born Machines: Classical Training Enables Quantum Generative Models with 160 Qubits
- Chemistry-enhanced Diffusion Generates Large Molecular Conformations from Small Molecules, Enabling Rapid Structure Prediction
- Improved Quantum Anonymous Notification Protocol Enhances Network Security Against Common Channel Noises
- Quantum Machine Learning Via Contrastive Training Achieves Higher Image Classification Accuracy with Reduced Reliance on Labeled Data
- Resource-efficient Variational Quantum Classifier Enables Near-Deterministic Predictions with Reduced Circuit Executions
- Physics-informed Neural Networks Enable High-Fidelity Quantum Gate Design Using Optimal Control
- Quantum Artificial Intelligence Enables Reliable, Low-latency Decision-making for Mission Critical Systems
- Density Quantum Neural Networks Boost Trainability & Performance
- Quantum-centric Machine Learning Predicts Molecular Wavefunctions, Enabling Efficient Ab Initio Molecular Dynamics
- Quantum-enhanced Learning Framework Improves Accuracy from 0.31 to 0.87, Enabling Practical Hybrid Workflows
- Transmon Qubit Rings Achieve Enhanced Fidelity Sweet Spots under Noise, Enabling Error-Correction Thresholds
- Hybrid Quantum-Classical Selective State Space AI Achieves 24.6% Performance Gain, Enabling Faster Temporal Sequence Classification
- Grover Search Algorithm’s Success Depends on Coherence Fraction, Quantifying Fidelity Between States
- Machine Learning Automatically Tunes Silicon Quantum Devices, Achieving over 99% Fidelity in Minutes
- Machine Learning Predicts Quantum Circuit Parameters, Transferring to Larger Electronic Structure Instances
- Normalized Descriptor Enables Unbiased Screening of Second-Order Nonlinear Optical Materials across Wide Band Gap Energies
