Qiskit tutorials: Machine learning

Click any link to open the tutorial directly in Quantum Lab.

  • Quantum-enhanced Support Vector Machine (QSVM) - This notebook provides an example of a classification problem that requires a feature map for which computing the kernel is not efficient classically. This means that the required computational resources are expected to scale exponentially with the size of the problem. This tutorial shows how this can be solved in a quantum processor by a direct estimation of the kernel in the feature space.

  • QSVM multiclass classification - Learn how to use a multiclass extension with quantum computing. A multiclass extension works in conjunction with an underlying binary classifier to provide classification where the number of classes is greater than two.

  • Variational Quantum Classifier (VQC) - This notebook shows a variational method using the VQC algorithm, as opposed to the QSVM notebook, which demonstrates a kernel based approach.

  • qGANs for loading random distributions Given k-dimensional data samples, we employ a quantum Generative Adversarial Network (qGAN) to learn the data’s underlying random distribution and to load it directly into a quantum state.