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# Dynex SDK

### Dynex SDK

#### Dynex SDK

The Dynex SDK natively supports both n.quantum gate-based circuits and n.quantum annealing-based sampling, seamlessly integrating with any Python code. Programmers who are familiar with quantum gate circuit languages such as Qiskit, Cirq, Pennylane, OpenQASM, or quantum annealing tools like the Dimod framework, PyQUBO, and other QUBO frameworks, will find it easy to run computations on the Dynex neuromorphic computing platform. The Dynex SDK supports both quantum circuits and quantum annealing, but without the typical constraints associated with conventional 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 Data Flow Whitepaper

> Dynex ODE Equations Whitepaper

##### Video Tutorials

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

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

##### Guides

> Medium: How to Implement a Quantum Self-Attention Transformer on Dynex

> Medium: How to Implement a 13-bit Full Adder Quantum Circuit on Dynex

> Medium: How to Implement Grover's Algorithm on Dynex

> Medium: How to Implement Shor's Algorithm on Dynex

> 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.

##### Quantum Algorithm Development on Dynex

Dynex's platform supports a variety of tools for creating and working with both n.quantum gate circuits and n.quantum annealing models:

For quantum gate circuits, support for tools such as Qiskit, Cirq, Pennylane, and OpenQASM are seamlessly integrated into the Dynex platform, enabling users to leverage these advanced quantum gate circuit languages. This enables the execution of well-known quantum algorithms like Shor's algorithm for efficient problem-solving in number theory, Grover's algorithm for database search, Simon's algorithm for finding hidden periods, or the Deutsch-Josza algorithm for determining the parity of a function.

For n.quantum annealing, tools like Dimod offer a shared API for samplers and provide classes for quadratic and higher-order models. PyQUBO allows the easy creation of QUBOs or Ising models from flexible mathematical expressions, complete with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, facilitating the use of Ising machines 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.

This comprehensive support ensures that both quantum gate circuits and quantum annealing models can be efficiently executed on the Dynex neuromorphic computing platform.

> Tools for Quantum Algorithm Development on Dynex

##### 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.

##### 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 natively supports both n.quantum gate-based circuits and n.quantum annealing-based sampling, seamlessly integrating with any Python code. Programmers who are familiar with quantum gate circuit languages such as Qiskit, Cirq, Pennylane, OpenQASM, or quantum annealing tools like the Dimod framework, PyQUBO, and other QUBO frameworks, will find it easy to run computations on the Dynex neuromorphic computing platform. The Dynex SDK supports both quantum circuits and quantum annealing, but without the typical constraints associated with conventional 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 Data Flow Whitepaper

> Dynex ODE Equations Whitepaper

##### Video Tutorials

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

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

##### Guides

> Medium: How to Implement a Quantum Self-Attention Transformer on Dynex

> Medium: How to Implement a 13-bit Full Adder Quantum Circuit on Dynex

> Medium: How to Implement Grover's Algorithm on Dynex

> Medium: How to Implement Shor's Algorithm on Dynex

> 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.

##### Quantum Algorithm Development on Dynex

Dynex's platform supports a variety of tools for creating and working with both n.quantum gate circuits and n.quantum annealing models:

For quantum gate circuits, support for tools such as Qiskit, Cirq, Pennylane, and OpenQASM are seamlessly integrated into the Dynex platform, enabling users to leverage these advanced quantum gate circuit languages. This enables the execution of well-known quantum algorithms like Shor's algorithm for efficient problem-solving in number theory, Grover's algorithm for database search, Simon's algorithm for finding hidden periods, or the Deutsch-Josza algorithm for determining the parity of a function.

For n.quantum annealing, tools like Dimod offer a shared API for samplers and provide classes for quadratic and higher-order models. PyQUBO allows the easy creation of QUBOs or Ising models from flexible mathematical expressions, complete with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, facilitating the use of Ising machines 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.

This comprehensive support ensures that both quantum gate circuits and quantum annealing models can be efficiently executed on the Dynex neuromorphic computing platform.

> Tools for Quantum Algorithm Development on Dynex

##### 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.

##### 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 natively supports both n.quantum gate-based circuits and n.quantum annealing-based sampling, seamlessly integrating with any Python code. Programmers who are familiar with quantum gate circuit languages such as Qiskit, Cirq, Pennylane, OpenQASM, or quantum annealing tools like the Dimod framework, PyQUBO, and other QUBO frameworks, will find it easy to run computations on the Dynex neuromorphic computing platform. The Dynex SDK supports both quantum circuits and quantum annealing, but without the typical constraints associated with conventional 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 Data Flow Whitepaper

> Dynex ODE Equations Whitepaper

##### Video Tutorials

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

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

##### Guides

> Medium: How to Implement a Quantum Self-Attention Transformer on Dynex

> Medium: How to Implement a 13-bit Full Adder Quantum Circuit on Dynex

> Medium: How to Implement Grover's Algorithm on Dynex

> Medium: How to Implement Shor's Algorithm on Dynex

> 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.

##### Quantum Algorithm Development on Dynex

Dynex's platform supports a variety of tools for creating and working with both n.quantum gate circuits and n.quantum annealing models:

For quantum gate circuits, support for tools such as Qiskit, Cirq, Pennylane, and OpenQASM are seamlessly integrated into the Dynex platform, enabling users to leverage these advanced quantum gate circuit languages. This enables the execution of well-known quantum algorithms like Shor's algorithm for efficient problem-solving in number theory, Grover's algorithm for database search, Simon's algorithm for finding hidden periods, or the Deutsch-Josza algorithm for determining the parity of a function.

For n.quantum annealing, tools like Dimod offer a shared API for samplers and provide classes for quadratic and higher-order models. PyQUBO allows the easy creation of QUBOs or Ising models from flexible mathematical expressions, complete with automatic constraint validation and parameter tuning. AutoQUBO by Fujitsu Research automates the conversion of Python functions to QUBO representations, facilitating the use of Ising machines 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.

This comprehensive support ensures that both quantum gate circuits and quantum annealing models can be efficiently executed on the Dynex neuromorphic computing platform.

> Tools for Quantum Algorithm Development on Dynex

##### 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.

##### 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