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

QSVM

Quantum Support Vector Machine — classification with quantum kernel functions

QRBM / QBM

Quantum (Restricted) Boltzmann Machine — generative models via quantum annealing

Neuromorphic Torch Layers

Drop-in PyTorch layers backed by Dynex computation

Quantum Feature Selection

scikit-learn plugin for quantum-enhanced feature selection

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
• QSVM with PyTorch
• 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

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

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

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.

QNLP on Dynex

Video demo: quantum NLP model training and inference on the Dynex platform.

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.

Quantum Transformer on Dynex

Video demo: quantum transformer implementation on Dynex for NLP tasks.

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