Dynex SDK

Dynex SDK

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 on GitHub
> 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 Dynex
> 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
> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70

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


Learning by examples

Whether you're just starting out or looking to deepen your understanding of quantum computations, our examples are tailored to help you effectively harness the power of Dynex technology. Explore step-by-step tutorials that demonstrate the platform's capabilities, and gain valuable insights into how to apply these techniques to solve real-world problems. This resource is your gateway to mastering the revolutionary potential of neuromorphic quantum computing with Dynex.

> Examples


Dynex' Scientific Papers

Dynex's technology is underpinned by a series of scientific papers authored by our esteemed researchers, which have been published in prestigious journals and academic platforms. These publications include cutting-edge studies such as "Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform" and "HUBO & QUBO and Prime Factorization," both featured in the International Journal of Bioinformatics & Intelligent Computing. Additional significant works are "Framework for Solving Harrow-Hassidim-Lloyd Problems with Neuromorphic Computing using the Dynex Cloud Computing Platform" and "Quantum Frontiers on Dynex: Elevating Deep Restricted Boltzmann Machines with Quantum Mode-Assisted Training," which highlight the versatility and power of the Dynex platform in addressing complex computational challenges. These publications not only contribute to the scientific community but also enhance the credibility and applicability of the Dynex technology in solving real-world problems through innovative quantum and neuromorphic computing techniques.

> Dynex Publications


Quantum Algorithm Development

Dynex's platform supports a variety of tools for creating and working with QUBO/Ising models. Dimod offers a shared API for samplers, providing classes for quadratic and higher-order models, along with reference examples and abstract base classes for constructing new samplers. PyQUBO enables the easy creation of QUBOs or Ising models from flexible mathematical expressions, with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, enabling the use of Ising machines like those on the Dynex Platform to solve combinatorial optimization problems. Qubolite, developed by the Lamarr Institute, is a lightweight toolbox in NumPy for creating, analyzing, and solving QUBO instances, incorporating cutting-edge research algorithms. Additionally, IBM Qiskit circuits can now be directly translated to run on Dynex Neuromorphic chips, thanks to groundbreaking research by Richard H. Warren, demonstrating the seamless integration of these advanced computational tools on the Dynex platform.

> Quantum Algorithm Development


Quantum Machine Learning

The Dynex platform integrates neuromorphic computing with popular machine learning frameworks such as TensorFlow and PyTorch, enabling the creation of hybrid models and advanced learning techniques like transfer and federated learning. Examples include the Quantum-Support-Vector-Machine (QSVM) and Mode-assisted unsupervised learning of restricted Boltzmann machines (MA-QRBM), showcasing how neuromorphic dynamics can solve complex problems in optimization, sampling, and machine learning. The platform also supports Quantum-Boltzmann-Machines (QBMs) for generative models and quantum annealing to sample from probability distributions. These integrations are backed by significant scientific research, demonstrating the potential of neuromorphic computing to enhance machine learning and optimization tasks.

> Quantum Machine Learning


Special Purpose Quantum Computing

The Dynex platform leverages neuromorphic quantum computing to enhance a range of computational problems. The Q-CFD project integrates quantum computing into Computational Fluid Dynamics (CFD) using OpenFoam, while Q-SISR formulates super-resolution as a sparse coding optimization problem, demonstrating improved accuracy. Q-FOLDING utilizes quantum optimization for RNA and protein folding tasks. The Scikit-Learn plugin integrates seamlessly with the Dynex platform to enhance feature selection in machine learning workflows. Additionally, the QBoost algorithm, inspired by a collaboration between Google and D-Wave, optimizes binary classifiers using quantum adiabatic methods on the Dynex Neuromorphic Platform. These projects highlight the versatility and advanced capabilities of Dynex in tackling complex computational challenges across various domains.

> Special Purpose Quantum Computing







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 on GitHub
> 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 Dynex
> 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
> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70

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


Learning by examples

Whether you're just starting out or looking to deepen your understanding of quantum computations, our examples are tailored to help you effectively harness the power of Dynex technology. Explore step-by-step tutorials that demonstrate the platform's capabilities, and gain valuable insights into how to apply these techniques to solve real-world problems. This resource is your gateway to mastering the revolutionary potential of neuromorphic quantum computing with Dynex.

> Examples


Dynex' Scientific Papers

Dynex's technology is underpinned by a series of scientific papers authored by our esteemed researchers, which have been published in prestigious journals and academic platforms. These publications include cutting-edge studies such as "Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform" and "HUBO & QUBO and Prime Factorization," both featured in the International Journal of Bioinformatics & Intelligent Computing. Additional significant works are "Framework for Solving Harrow-Hassidim-Lloyd Problems with Neuromorphic Computing using the Dynex Cloud Computing Platform" and "Quantum Frontiers on Dynex: Elevating Deep Restricted Boltzmann Machines with Quantum Mode-Assisted Training," which highlight the versatility and power of the Dynex platform in addressing complex computational challenges. These publications not only contribute to the scientific community but also enhance the credibility and applicability of the Dynex technology in solving real-world problems through innovative quantum and neuromorphic computing techniques.

> Dynex Publications


Quantum Algorithm Development

Dynex's platform supports a variety of tools for creating and working with QUBO/Ising models. Dimod offers a shared API for samplers, providing classes for quadratic and higher-order models, along with reference examples and abstract base classes for constructing new samplers. PyQUBO enables the easy creation of QUBOs or Ising models from flexible mathematical expressions, with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, enabling the use of Ising machines like those on the Dynex Platform to solve combinatorial optimization problems. Qubolite, developed by the Lamarr Institute, is a lightweight toolbox in NumPy for creating, analyzing, and solving QUBO instances, incorporating cutting-edge research algorithms. Additionally, IBM Qiskit circuits can now be directly translated to run on Dynex Neuromorphic chips, thanks to groundbreaking research by Richard H. Warren, demonstrating the seamless integration of these advanced computational tools on the Dynex platform.

> Quantum Algorithm Development


Quantum Machine Learning

The Dynex platform integrates neuromorphic computing with popular machine learning frameworks such as TensorFlow and PyTorch, enabling the creation of hybrid models and advanced learning techniques like transfer and federated learning. Examples include the Quantum-Support-Vector-Machine (QSVM) and Mode-assisted unsupervised learning of restricted Boltzmann machines (MA-QRBM), showcasing how neuromorphic dynamics can solve complex problems in optimization, sampling, and machine learning. The platform also supports Quantum-Boltzmann-Machines (QBMs) for generative models and quantum annealing to sample from probability distributions. These integrations are backed by significant scientific research, demonstrating the potential of neuromorphic computing to enhance machine learning and optimization tasks.

> Quantum Machine Learning


Special Purpose Quantum Computing

The Dynex platform leverages neuromorphic quantum computing to enhance a range of computational problems. The Q-CFD project integrates quantum computing into Computational Fluid Dynamics (CFD) using OpenFoam, while Q-SISR formulates super-resolution as a sparse coding optimization problem, demonstrating improved accuracy. Q-FOLDING utilizes quantum optimization for RNA and protein folding tasks. The Scikit-Learn plugin integrates seamlessly with the Dynex platform to enhance feature selection in machine learning workflows. Additionally, the QBoost algorithm, inspired by a collaboration between Google and D-Wave, optimizes binary classifiers using quantum adiabatic methods on the Dynex Neuromorphic Platform. These projects highlight the versatility and advanced capabilities of Dynex in tackling complex computational challenges across various domains.

> Special Purpose Quantum Computing







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 on GitHub
> 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 Dynex
> 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
> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70

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


Learning by examples

Whether you're just starting out or looking to deepen your understanding of quantum computations, our examples are tailored to help you effectively harness the power of Dynex technology. Explore step-by-step tutorials that demonstrate the platform's capabilities, and gain valuable insights into how to apply these techniques to solve real-world problems. This resource is your gateway to mastering the revolutionary potential of neuromorphic quantum computing with Dynex.

> Examples


Dynex' Scientific Papers

Dynex's technology is underpinned by a series of scientific papers authored by our esteemed researchers, which have been published in prestigious journals and academic platforms. These publications include cutting-edge studies such as "Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform" and "HUBO & QUBO and Prime Factorization," both featured in the International Journal of Bioinformatics & Intelligent Computing. Additional significant works are "Framework for Solving Harrow-Hassidim-Lloyd Problems with Neuromorphic Computing using the Dynex Cloud Computing Platform" and "Quantum Frontiers on Dynex: Elevating Deep Restricted Boltzmann Machines with Quantum Mode-Assisted Training," which highlight the versatility and power of the Dynex platform in addressing complex computational challenges. These publications not only contribute to the scientific community but also enhance the credibility and applicability of the Dynex technology in solving real-world problems through innovative quantum and neuromorphic computing techniques.

> Dynex Publications


Quantum Algorithm Development

Dynex's platform supports a variety of tools for creating and working with QUBO/Ising models. Dimod offers a shared API for samplers, providing classes for quadratic and higher-order models, along with reference examples and abstract base classes for constructing new samplers. PyQUBO enables the easy creation of QUBOs or Ising models from flexible mathematical expressions, with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, enabling the use of Ising machines like those on the Dynex Platform to solve combinatorial optimization problems. Qubolite, developed by the Lamarr Institute, is a lightweight toolbox in NumPy for creating, analyzing, and solving QUBO instances, incorporating cutting-edge research algorithms. Additionally, IBM Qiskit circuits can now be directly translated to run on Dynex Neuromorphic chips, thanks to groundbreaking research by Richard H. Warren, demonstrating the seamless integration of these advanced computational tools on the Dynex platform.

> Quantum Algorithm Development


Quantum Machine Learning

The Dynex platform integrates neuromorphic computing with popular machine learning frameworks such as TensorFlow and PyTorch, enabling the creation of hybrid models and advanced learning techniques like transfer and federated learning. Examples include the Quantum-Support-Vector-Machine (QSVM) and Mode-assisted unsupervised learning of restricted Boltzmann machines (MA-QRBM), showcasing how neuromorphic dynamics can solve complex problems in optimization, sampling, and machine learning. The platform also supports Quantum-Boltzmann-Machines (QBMs) for generative models and quantum annealing to sample from probability distributions. These integrations are backed by significant scientific research, demonstrating the potential of neuromorphic computing to enhance machine learning and optimization tasks.

> Quantum Machine Learning


Special Purpose Quantum Computing

The Dynex platform leverages neuromorphic quantum computing to enhance a range of computational problems. The Q-CFD project integrates quantum computing into Computational Fluid Dynamics (CFD) using OpenFoam, while Q-SISR formulates super-resolution as a sparse coding optimization problem, demonstrating improved accuracy. Q-FOLDING utilizes quantum optimization for RNA and protein folding tasks. The Scikit-Learn plugin integrates seamlessly with the Dynex platform to enhance feature selection in machine learning workflows. Additionally, the QBoost algorithm, inspired by a collaboration between Google and D-Wave, optimizes binary classifiers using quantum adiabatic methods on the Dynex Neuromorphic Platform. These projects highlight the versatility and advanced capabilities of Dynex in tackling complex computational challenges across various domains.

> Special Purpose Quantum Computing







Copyright © 2024
Dynex Development Est..
All rights reserved.

Copyright © 2024
Dynex Development Est..
All rights reserved.

Copyright © 2024
Dynex Development Est..
All rights reserved.