Essential Mathematics for Quantum Computing
The mathematics needed for quantum computing: linear algebra, vector spaces, complex numbers, the Bloch sphere, operators and probability.
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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 Variational Methods: The Workhorse of NISQ
Variational quantum methods, such as VQE and QAOA, are the main algorithms for today’s noisy hardware. The variational principle and uses.
The Quantum Expectation-Maximization Algorithm
The expectation-maximization algorithm fits models with hidden variables. How a quantum version works and what speedup it could offer.
Cyber Security After Quantum: What to Prepare
What organizations should do to prepare for cryptographically relevant quantum computers: inventory, post-quantum standards and migration.
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 Ensemble Learning and Model Averaging
Ensemble methods combine several models to improve predictions. How quantum ensembles and model averaging work and what they could offer.
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.
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.