Dynex is the world’s first neuromorphic supercomputing blockchain based on the DynexSolve chip algorithm, a Proof-of-Useful-Work (PoUW) approach to solving real-world problems. The Dynex SDK is used to interact and compute on the Dynex Platform. All examples require the DynexSDK for Python as well as a valid API key. Thes repositoriy is continously updated, come back to check on updates.
Dynex SDK documentation:
To get familiar with the computing possibilities on the Dynex Platform, we have prepared a number of Python Jupyter Notebooks. Here are some of our beginner guides demonstrating the use of the Dynex SDK.
- Example: Computing on the Dynex Platform with Python - BQM
- Example: Computing on the Dynex Platform with Python - BQM K4 Complete Graph
- Example: Computing on the Dynex Platform with Python - Logic Gates
- Example: Computing on the Dynex Platform with Python - QUBO
- Example: Computing on the Dynex Platform with Python - Anti-crossing problem
- Example: Computing on the Dynex Platform with Python - Maximum Independent Set
- Example: Computing on the Dynex Platform with Python - SAT
- Example: Computing on the Dynex Platform with Python - NAE3SAT
Here are some advanced code examples and notebooks to be used to compute them on the Dynex neuromorphic computing platform:
- Example: RNA Folding of the Tobacco Mild Green Mosaic Virus
- Example: Placement of Charging Stations
- Example: Breast Cancer Prediction using the Dynex scikit-learn Plugin
- Example: Quantum Integer Factorization
Quantum computing algorithms for machine learning harness the power of quantum mechanics to enhance various aspects of machine learning tasks. As both, quantum computing and neuromorphic computing are sharing similar features, these algorithms can also be computed efficiently on the Dynex platform – but without the limitations of limited qubits, error correction or availability:
Quantum Support Vector Machine (QSVM): QSVM is a quantum-inspired algorithm that aims to classify data using a quantum kernel function. It leverages the concept of quantum superposition and quantum feature mapping to potentially provide computational advantages over classical SVM algorithms in certain scenarios.
Quantum Principal Component Analysis (QPCA): QPCA is a quantum version of the classical Principal Component Analysis (PCA) algorithm. It utilizes quantum linear algebra techniques to extract the principal components from high-dimensional data, potentially enabling more efficient dimensionality reduction in quantum machine learning.
Quantum Neural Networks (QNN): QNNs are quantum counterparts of classical neural networks. They leverage quantum principles, such as quantum superposition and entanglement, to process and manipulate data. QNNs hold the potential to learn complex patterns and perform tasks like classification and regression, benefiting from quantum parallelism.
Quantum K-Means Clustering: Quantum K-means is a quantum-inspired variant of the classical K-means clustering algorithm. It uses quantum algorithms to accelerate the clustering process by exploring multiple solutions simultaneously. Quantum K-means has the potential to speed up clustering tasks for large-scale datasets.
Quantum Boltzmann Machines (QBMs): QBMs are quantum analogues of classical Boltzmann Machines, which are generative models used for unsupervised learning. QBMs employ quantum annealing to sample from a probability distribution and learn patterns and structures in the data.
Quantum Support Vector Regression (QSVR): QSVR extends the concept of QSVM to regression tasks. It uses quantum computing techniques to perform regression analysis, potentially offering advantages in terms of efficiency and accuracy over classical regression algorithms.
Here are some example of these algorithms implemented on the Dynex Platform:
- Example: Quantum-Support-Vector-Machine Implementation on Dynex
- Example: Quantum-Boltzmann-Machine Implementation on Dynex
- Example: Feature Selection - Titanic Survivals
- Example: Breast Cancer Prediction using the Dynex scikit-learn Plugin
The Dynex Neuromorphic Torch layer can be used in any NN model. Welcome to hybrid models, neuromorphic-, transfer- and federated-learning with PyTorch
This package provides a scikit-learn transformer for feature selection using the Dynex Neuromorphic Computing Platform. It is built to integrate seamlessly with scikit-learn, an industry-standard, state-of-the-art ML library for Python.
The Dynex scikit-learn Plugin makes it easier to use the Dynex platform for the feature selection piece of ML workflows. Feature selection – a key building block of machine learning – is the problem of determining a small set of the most representative characteristics to improve model training and performance in ML. With this new plug-in, ML developers need not be experts in optimization or hybrid solving to get the business and technical benefits of both. Developers creating feature selection applications can build a pipeline with scikit-learn and then embed the Dynex Platform into this workflow more easily and efficiently.
The D-Wave quantum computer has been widely studied as a discrete optimization engine that accepts any problem formulated as quadratic unconstrained binary optimization (QUBO). In 2008, Google and D-Wave published a paper, Training a Binary Classifier with the Quantum Adiabatic Algorithm, which describes how the Qboost ensemble method makes binary classification amenable to quantum computing: the problem is formulated as a thresholded linear superposition of a set of weak classifiers and the D-Wave quantum computer is used to optimize the weights in a learning process that strives to minimize the training error and number of weak classifiers
The Dynex QBoost Implementation provides a QBoost algorithm plugin to use the Dynex Neuromorphic Platform.
Dimod is a shared API for samplers. It provides classes for quadratic models—such as the binary quadratic model (BQM) class that contains Ising and QUBO models used by samplers such as the Dynex Neuromorphic Platform or the D-Wave system—and higher-order (non-quadratic) models, reference examples of samplers and composed samplers and abstract base classes for constructing new samplers and composed samplers:
PyQUBO allows you to create QUBOs or Ising models from flexible mathematical expressions easily. It is Python based (C++ backend), fully integrated with Ocean SDK, supports automatic validation of constraints and features placeholder for parameter tuning.
Released under the Apache License 2.0. See LICENSE file.