Dynex SDK

Customers can run computations on the decentralised Dynex n.quantum computing cloud, which is empowered by a growing number of contributing workers. These are miners who are running the proprietary Proof-of-Useful-Work (PoUW) algorithm DynexSolve. Dynex’s proprietary job management and scheduling system Dynex Mallob ensures that computing jobs are being distributed and computed in the fastest way possible. The Dynex SDK is a Python package which is used to compute on the Dynex platform:

Dynex SDK

The Dynex SDK provides a n.quantum Ising/QUBO sampler which can be called from any Python code. Developers and application developers already familiar with the Dimod framework, PyQUBO or the Ocean SDK will find it very easy to run computations on the Dynex neuromorphic computing platform: The Dynex Sampler object can simply replace the default sampler object which typically is used to run computations on, for example, the D-Wave system – without the limitations of regular quantum machines. The Dynex SDK is a suite of open-source Python tools for solving hard problems with neuromorphic computing which helps reformulate your application’s problem for solution by the Dynex computing platform. It also handles communication between your application code and the Dynex neuromorphic computing platform automatically.

Download and install the Dynex SDK with the following command:

pip install dynex

Then follow the steps explained in Installing the Dynex SDK to configure the SDK. We suggest to download the Dynex SDK Hello World Example for the first steps of using the Dynex Neuromorphic Platform.

Documentation

> Dynex SDK Wiki

> Dynex SDK Documentation

Video Tutorials

> Tutorial: Compute on Dynex: "Hello, world" (using Github CodeSpace)

> Tutorial: Compute on Dynex: "Hello, world" (using pip install dynex)

Guides

> Medium: Real World Use Case: Stock Portfolio Optimisation with Quantum Algorithms on the Dynex Platform

> Medium: Computing on the Dynex Neuromorphic Platform: Image Classification

> Medium: Computing on the Dynex Neuromorphic Platform: IBM Qiskit 4-Qubit Full Adder Circuit

> Medium: Benchmarking the Dynex Neuromorphic Platform with the Q-Score

Neuromorphic Computing for Computer Scientists: A complete guide to Neuromorphic Computing on the Dynex Neuromorphic Cloud Computing Platform, Dynex Developers, 2024, 249 pages, available as eBook, paperback and hardcover

> Amazon.com
> Amazon.co.uk
> Amazon.de

Dynex' Scientific Papers

> Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform; Adam Neumann, Dynex Developers; International Journal of Bioinformatics & Intelligent Computing. 2024; Volume 3(1):91- 103, ISSN 2816-8089
> HUBO & QUBO and Prime Factorization; Samer Rahmeh, Cali Technology Solutions, Dynex Developers; International Journal of Bioinformatics & Intelligent Computing. 2024; Volume 3(1):45-69, ISSN 2816-8089
> Framework for Solving Harrow-Hassidim-Lloyd Problems with Neuromorphic Computing using the Dynex Cloud Computing Platform; Samer Rahmeh, Cali Technology Solutions, Dynex Developers; 112871175; Academia.edu; 2023
> Quantum Frontiers on Dynex: Elevating Deep Restricted Boltzmann Machines with Quantum Mode-Assisted Training; Adam Neumann, Dynex Developers; 116660843, Academia.edu; 2024

Pricing

> Pricing model for compute on Dynex

Learn more

> Beginner Guides

> Advanced Examples

> Machine Learning Examples

Packages

> Dynex SDK on GitHub

> Dynex Qiskit Package on GitHub

Next Generation Algorithms for Machine Learning

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.

> Machine Learning

Dynex IBM Qiskit Package

Thanks to groundbreaking research from Richard H. Warren, it is possible to directly translate Qiskit quantum circuits into Dynex Neuromorphic chips. The concept behind is a direct translation of Qiskit objects, but instead of running on IBM Q, the circuits are executed on the Dynex Neuromorphic platform. Here is an example of a one-qubit adder circuit using this approach.

> Dynex Qiskit Package

AutoQUBO: Automated Conversion from Python functions to QUBO

AUTOmated QUBO Generator (by Fujitsu Research) is an automatic tool for converting a high-level description of an optimization problem, written in Python, into an equivalent QUBO representation. It is doing this by using a novel data driven translation method that can completely decouple the input and output representation. The QUBO framework provides a way to model, in principle, any combinatorial optimization problem and enables the use of Ising machines, like available on the Dynex Platform, to solve it. It introduces symbolic sampling, which provides QUBO formulations for entire problem classes.

> AutoQUBO on Dynex

Dimod: A shared API for QUBO/Ising samplers

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.

> Dimod documentation

PyQubo: QUBOs or Ising models from flexible mathematical expressions

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.

> PyQUBO documentation

Qubolite: light-weight toolbox for working with QUBO instances in NumPy

Quantum Computing (QC) has ushered in a new era of computation, promising to solve problems that are practically infeasible for classical computers. One of the most exciting applications of quantum computing is its ability of solving combinatorial optimization problems, such as Quadratic Unconstrained Binary Optimization (QUBO). This problem class has regained significant attention with the advent of Quantum Computing. These hard-to-solve combinatorial problems appear in many different domains, including finance, logistics, Machine Learning and Data Mining. To harness the power of Quantum Computing for QUBO, The Lamarr Institute introduced qubolite, a Python package comprising utilities for creating, analyzing, and solving QUBO instances, which incorporates current research algorithms developed by scientists at the Lamarr Institute. Qubolite is a light-weight toolbox for working with QUBO instances in NumPy. This fork showcases the use of Qubolite to compute on the Dynex Neuromorphic computing platform.

> Qubolite on Dynex

Dynex Scikit-Learn Plugin

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 Scikit-Learn plugin provides this QBoost algorithm to use the Dynex Neuromorphic Platform.

> Dynex QBoost

> Dynex Scikit-Learn Plugin