Get started with quantum machine learning

Here are some resources to start your deep dive into the weird and wonderful world of quantum machine learning:

• Prerequisites Explore these before you take the plunge!

  • The quantum Fourier transform : A tutorial on the QFT from Qiskit.
  • Quantum phase estimation : A tutorial on QPE from Qiskit.
  • Solving linear systems using the HHL algorithm : A tutorial on the HHL algorithm from Qiskit.
  • Grover's algorithm and amplitude amplification : Learn about Grover's algorithm from the Qiskit quantum computing textbook.

• Quantum machine learning tutorials Build quantum machine learning models by following these guides

  • Hello, many worlds : get started with TensorFlow Quantum.
  • Distance estimation for k-means clustering
  • Inference on quantum Bayesian networks
  • Quantum neural networks

• Textbooks

  • Quantum machine learning for data scientists : A super helpful introduction to quantum machine learning organized in a way that makes it easy to understand, filled with step-by-step explanations and examples.

• Research papers Explore some of the coolest and most important developments in QML. Warning: might lead to immense frustration and feelings of inadeqaucy.

  • Create superpositions associated with discretized probability distributions
  • The original amplitude amplification and estimation paper
  • Rejection sampling using amplitude amplification
  • Learn how to tune quantum circuit parameters
  • Quantum neural networks
  • Learning discrete probability distributions using hybrid GANs
  • Quantum Wasserstein GANs: supplemental materials available [here].(http://papers.neurips.cc/paper/8903-quantum-wasserstein-generative-adversarial-networks)
  • More coming soon!