Quantum Neural Networks (QNNs): How They Are Built
How quantum neural networks are built from parameterized quantum circuits, how they are trained and where they might be useful.
BasicsAlgorithmsHardwareError CorrectionCryptographyMachine LearningInformation TheoryPhysics & SimulationApplicationsIndustry & Careers
Tagged: Artificial Intelligence
110 articles
Tensor Network Methods for Many-Body Physics
Tensor networks represent many-body quantum states efficiently. Their history, mathematics, graphical notation and uses in physics and chemistry.
Machine Learning to Speed Up Quantum Simulation
How machine learning helps quantum simulation, from finding good circuit parameters to modeling quantum states, and the open challenges.
Quantum Causal Models: Cause and Effect With Qubits
Quantum causal models extend causal graphs to quantum systems and help explain correlations. The framework and what it clarifies.
Quantum Deep Learning: Depth on Real Hardware
What deep learning on quantum hardware looks like today: quantum neural networks, hybrid models, training algorithms and depth limits.
Quantum Concept Learning and Hypothesis Classes
Concept learning asks how a learner identifies a rule from examples. How quantum learning theory treats hypothesis classes and sample complexity.
Quantum Decision Forests and Ensemble Trees
Decision forests combine many decision trees. How quantum versions have been proposed, how they compare with random forests and possible uses.
Quantum Cross-Validation and Model Selection
How cross-validation and model selection work for quantum machine learning models, and the challenges caused by quantum noise.
Quantum Probabilistic Models and Inference
Quantum probabilistic models describe uncertainty with quantum states. Their structure, algorithms and uses in data analysis and machine learning.
Quantum Generative Models and What They Sample
Quantum generative models such as quantum circuit Born machines learn to sample from probability distributions. How they work and their uses.
Quantum Incremental Learning From Streaming Data
Incremental learning updates a model as new data arrives. How quantum machine learning models might do this and the current challenges.
Quantum Semi-Supervised Learning With Few Labels
Semi-supervised learning uses a few labeled and many unlabeled examples. How quantum algorithms might be applied to it and the open issues.
