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.
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Tagged: Superposition
333 articles
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.
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 Meta-Learning: Learning to Learn on Qubits
Quantum meta-learning uses classical or quantum models to learn good starting parameters for quantum circuits. Ideas, algorithms and challenges.
Quantum Markov Models and Hidden State Inference
Quantum Markov models and hidden quantum Markov models describe processes with quantum states. Transitions, inference and applications.
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 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.
