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: