Quantum Active Learning: Choosing What to Label
Quantum active learning chooses which data points to label next to train a quantum model efficiently. Methods, benefits and challenges.
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Quantum Machine Learning
Quantum neural networks, kernels, clustering and variational classifiers — where quantum computing meets AI, and what is still hype.
62 articles
Quantum Adversarial Learning and Robustness
Adversarial attacks can fool quantum classifiers just as they fool classical ones. Types of attacks, defenses and robustness evaluation.
Quantum Federated Learning Across Devices
Quantum federated learning trains models across devices without sharing raw data, using quantum or quantum-safe methods. How it works.
Quantum Few-Shot Learning From Little Data
Quantum few-shot learning aims to adapt quantum models to new tasks with very little data. Approaches, hardware limits and applications.
Quantum Transfer Learning Between Circuits
Quantum transfer learning reuses a trained classical or quantum model for a new task on quantum hardware. How it works and its challenges.
Quantum Stochastic Gradient Descent (QSGD)
Stochastic gradient descent in quantum machine learning: how gradients of quantum circuits are estimated from samples and the challenges involved.
Quantum Graph Neural Networks and Their Circuits
Quantum graph neural networks encode graph structure into quantum circuits. Architecture, training, benchmarks and prospects.
Quantum Latent Space Models and Representation
Latent space models learn compact representations of data. How quantum versions would work, possible uses and comparison with classical models.
Quantum Feature Spaces: Where the Advantage Hides
Quantum feature spaces are where quantum machine learning is supposed to find its advantage. Encodings, feature maps and current limitations.
Quantum Instance-Based Learning and Similarity
Instance-based learning with quantum computers: how similarity between data points might be computed quantumly and possible applications.
Quantum Kernel Methods and Feature Maps
Quantum kernel methods map data into quantum feature spaces and use them in classifiers such as SVMs. How they work and where limits lie.
Quantum Annealing for Machine Learning Tasks
How quantum annealers are used for machine learning tasks such as training and feature selection, and how they compare with classical methods.
