> ## Documentation Index
> Fetch the complete documentation index at: https://dynex.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Quantum Machine Learning

> QSVM, QPCA, QBM, QNN, and feature selection on Dynex

# Quantum Machine Learning

Dynex supports a range of quantum machine learning algorithms. Because neuromorphic and quantum computing share similar features, these algorithms run without the limitations of limited qubits, error correction requirements, or hardware availability.

## Supported algorithms

<CardGroup cols={2}>
  <Card title="QSVM" icon="vector-square" href="/examples/ml/qsvm">
    Quantum Support Vector Machine — classification with quantum kernel functions
  </Card>

  <Card title="QRBM / QBM" icon="network-wired" href="/examples/ml/qrbm">
    Quantum (Restricted) Boltzmann Machine — generative models via quantum annealing
  </Card>

  <Card title="Neuromorphic Torch Layers" icon="fire" href="/examples/ml/torch-layers">
    Drop-in PyTorch layers backed by Dynex computation
  </Card>

  <Card title="Quantum Feature Selection" icon="filter">
    scikit-learn plugin for quantum-enhanced feature selection
  </Card>
</CardGroup>

## Algorithm Overview

### Quantum Support Vector Machine (QSVM)

QSVM uses a quantum kernel function for classification. It leverages quantum superposition and feature mapping to potentially provide computational advantages over classical SVM, especially on high-dimensional data.

* [QSVM notebook](https://github.com/Dynex-Development/awesome-dynex/blob/main/machine_learning/example_support_vector_machine.ipynb)
* [QSVM with PyTorch](https://github.com/Dynex-Development/awesome-dynex/blob/main/machine_learning/Example_SVM_pytorch.ipynb)
* Scientific background: Rounds & Goddard, "Optimal feature selection in credit scoring and classification using a quantum annealer" (2017)

### Quantum Principal Component Analysis (QPCA)

Quantum version of classical PCA using quantum linear algebra for dimensionality reduction. Can process high-dimensional feature spaces more efficiently than classical approaches.

### Quantum Neural Networks (QNN)

Quantum counterparts of classical neural networks. Leverage superposition and entanglement to process and manipulate data, learning complex patterns for classification and regression.

### Quantum Boltzmann Machines (QBM)

Quantum analogues of classical Boltzmann Machines. Use quantum annealing to sample from probability distributions and learn patterns in data for unsupervised learning.

* [QBM notebook](https://github.com/Dynex-Development/awesome-dynex/blob/main/machine_learning/example_quantum_boltzmann_machine_QBM.ipynb)

### Quantum K-Means Clustering

Quantum-inspired K-means using quantum algorithms to accelerate clustering. Explores multiple cluster assignments simultaneously via quantum parallelism.

* [k-Means notebook](https://github.com/Dynex-Development/awesome-dynex/blob/main/optimization/quantum_kmeans_clustering.ipynb)

### QBoost

Ensemble method inspired by Google & D-Wave's 2008 paper. Formulates binary classification as QUBO and uses quantum optimization to learn classifier weights that minimize training error.

* [QBoost implementation](https://github.com/Dynex-Development/awesome-dynex/blob/main/machine_learning/Dynex_Scikit-Learn_Plugin.ipynb)

## Quantum Natural Language Processing (QNLP)

End-to-end QNLP pipeline on Dynex: web data collection, model training, and real-time inference from a ChatGPT-style bot.

<Card title="QNLP on Dynex" icon="comments" href="https://www.youtube.com/watch?v=0S1EiFxhyO4">
  Video demo: quantum NLP model training and inference on the Dynex platform.
</Card>

## Quantum Transformer (QTransform)

Quantum analogue of the transformer attention mechanism for NLP and generative tasks. Combines the self-attention mechanism with quantum computing to improve sequential data processing.

<Card title="Quantum Transformer on Dynex" icon="bolt" href="https://www.youtube.com/watch?v=JDd8ViPU8eI">
  Video demo: quantum transformer implementation on Dynex for NLP tasks.
</Card>

## Publications

Key scientific papers backing these implementations:

* Dixit et al. (2021). "Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer." *Front. Phys.* 9:589626
* Manukian et al. (2020). "Mode-assisted unsupervised learning of restricted Boltzmann machines." *Communications Physics* 3:105
* Neumann (2024). "Advancements in Unsupervised Learning: Mode-Assisted QRBM Leveraging Neuromorphic Computing." *IJBIC* 3(1):91–103
* Rounds & Goddard (2017). "Optimal feature selection in credit scoring and classification using a quantum annealer"
* Bhatia & Phillipson (2021). "Performance Analysis of Support Vector Machine on the D-Wave Quantum Annealer." *ICCS 2021*