Dynex

Industries

Compute on Dynex

Dynex

Industries

Compute on Dynex

Dynex

Industries

Compute on Dynex

# Beginner Guides

### Beginner Guides

#### Beginner Guides

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.

##### Documentation

##### Guides

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

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

##### Beginner Jupyter Notebooks

> 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

##### 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:

##### 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.

##### Advanced Examples

Example implementations of RNA folding, Quantum-Boltzmann-Machines (QBM), Quantum-Support-Vector-Machines (QSVM), Feature Selection and optimization problems can be found here:

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.

##### Documentation

##### Guides

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

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

##### Beginner Jupyter Notebooks

> 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

##### 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:

##### 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.

##### Advanced Examples

Example implementations of RNA folding, Quantum-Boltzmann-Machines (QBM), Quantum-Support-Vector-Machines (QSVM), Feature Selection and optimization problems can be found here:

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.

##### Documentation

##### Guides

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

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

##### Beginner Jupyter Notebooks

> 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

##### 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:

##### 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.

##### Advanced Examples

Example implementations of RNA folding, Quantum-Boltzmann-Machines (QBM), Quantum-Support-Vector-Machines (QSVM), Feature Selection and optimization problems can be found here: