> ## Documentation Index
> Fetch the complete documentation index at: https://dynex.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Dynex Quantum Platform: A cloud-based, qubit-agnostic platform

# Dynex SDK

The Dynex SDK provides a unified programming interface for expressing optimization, probabilistic, and quantum-inspired workloads. Users interact with the platform through high-level representations such as optimization problem formulations, probabilistic models, graph-based structures, and circuit-derived abstractions. The SDK handles compilation, transformation, and backend adaptation internally, enabling portability across supported execution resources.

<CardGroup cols={2}>
  <Card title="Quickstart" icon="bolt" href="/quickstart">
    Run your first quantum computation in under 5 minutes
  </Card>

  <Card title="Installation" icon="download" href="/installation">
    Install the SDK and configure credentials
  </Card>

  <Card title="Annealing" icon="wave-sine" href="/annealing/models">
    Solve QUBO, Ising, CQM, and DQM problems
  </Card>

  <Card title="Gate Circuits" icon="microchip" href="/circuits/overview">
    Run PennyLane, Qiskit, and OpenQASM circuits
  </Card>
</CardGroup>

## What is Dynex?

Dynex provides a qubit-agnostic computing platform designed to unify diverse quantum and quantum-driven compute resources under a single execution and programming environment. The platform enables users to access heterogeneous compute modalities through a consistent workflow for optimization, simulation, and probabilistic workloads—without requiring direct exposure to device-specific implementations. The Dynex platform focuses on abstraction, orchestration, and interoperability, allowing end users to work at the problem level rather than the hardware level.

The platform excels at two complementary computing paradigms:

<CardGroup cols={2}>
  <Card title="Quantum Annealing" icon="wave-sine">
    Solve QUBO, Ising, and constrained optimization problems with BQM, CQM, and DQM model types. Compatible with the dimod framework, PyQUBO, and other QUBO tools.
  </Card>

  <Card title="Quantum Gate Circuits" icon="microchip">
    Run quantum gate circuits from PennyLane, Qiskit, Cirq, and OpenQASM directly on the Dynex platform. Run Grover's, Shor's, QFT and other algorithms without modification.
  </Card>
</CardGroup>

## How It Works

<Steps>
  <Step title="Formulate your problem">
    Define your problem as a BQM, CQM, or DQM model, or as a quantum gate circuit using PennyLane, Qiskit, or OpenQASM.
  </Step>

  <Step title="Choose a compute backend">
    Select LOCAL for testing, CPU/GPU for testnet, or QPU for quantum hardware access via `DynexConfig`.
  </Step>

  <Step title="Submit and sample">
    The SDK converts your problem into a neuromorphic circuit and submits it to the Dynex computing network.
  </Step>

  <Step title="Analyze results">
    Retrieve results as a dimod `SampleSet` and inspect samples, energies, and variable assignments.
  </Step>
</Steps>

## Unified Execution Environment

Dynex presents all supported compute backends as standardized execution resources within a common runtime environment. These resources include:

* proprietary Dynex compute systems,
* large-scale software-based emulation resources, and
* third-party quantum processing units operated by external providers.

A centralized orchestration layer manages workload submission, routing, execution coordination, and result handling. From the user’s perspective, workloads are expressed once and executed consistently, independent of the underlying compute modality.

## Hybrid and Heterogeneous Workflows

The platform is architected to enable hybrid computational workflows, allowing multiple computing paradigms to be combined seamlessly within the lifecycle of a single problem. Rather than binding a workload to a fixed execution model, the system supports flexible orchestration across heterogeneous compute substrates. As a result, different stages of a computation—such as preprocessing, probabilistic sampling, optimization, or refinement—can be executed on the hardware or simulation environment best suited to the specific task.

Depending on availability, suitability, and performance requirements, workloads can be dynamically mapped to different backend resources, including classical high-performance systems, quantum emulation environments, neuromorphic probabilistic processors, or emerging room-temperature quantum hardware. Backend selection mechanisms evaluate factors such as the mathematical structure of the problem, the required computational precision, latency or throughput constraints, and the current availability of resources within the platform.

Importantly, this orchestration layer is designed to remain transparent to the application developer. Developers interact with the platform through a unified programming interface and abstract problem formulations (e.g., QUBO, Ising, or probabilistic graphical models), while the platform handles the underlying execution strategy. This abstraction allows users to focus on modeling and algorithm design, while the system automatically determines the most effective execution pathway across the available heterogeneous computing infrastructure.

## Dynex Compute Systems

Dynex-operated compute systems are optimized for probabilistic and energy-based problem formulations commonly used in optimization, sampling, and inference. These systems are integrated into the Dynex platform as native execution resources and are accessed through the same APIs and tooling as other supported backends. From a platform architecture perspective, these systems can be understood as specialized accelerators for probabilistic and combinatorial computation, designed to complement rather than replace existing classical and quantum hardware infrastructures. Instead of operating as isolated computing paradigms, they form part of a heterogeneous compute stack in which different physical substrates are leveraged according to their strengths in solving optimization, sampling, and stochastic inference problems.

Within this framework, the available computational resources span several technological layers. At one end, high-performance quantum emulation systems implemented on CPU/GPU nodes enable the simulation of large-scale quantum circuits and quantum-inspired algorithms with substantial scalability. In parallel, quantum-driven neuromorphic computing systems based on analog CMOS technology (Apollo Series) provide energy-efficient hardware substrates for probabilistic computing, enabling massively parallel exploration of energy landscapes typical of Ising and QUBO formulations. At the hardware frontier, a proprietary room-temperature quantum computing system built on nitrogen-vacancy (NV) diamond spin qubits (Zeus series) introduces a genuine quantum spin system that operates without cryogenic infrastructure, opening a pathway toward hybrid architectures where classical, neuromorphic, and quantum spin-based devices coexist within a unified computational ecosystem.

## Supported Integrations

<CardGroup cols={3}>
  <Card title="dimod" icon="cube">
    Native BQM, CQM, DQM model support
  </Card>

  <Card title="PennyLane" icon="atom">
    Quantum ML circuits on Dynex
  </Card>

  <Card title="Qiskit" icon="q">
    IBM Qiskit circuits on Dynex
  </Card>

  <Card title="PyTorch" icon="fire">
    Neuromorphic Torch layers
  </Card>

  <Card title="scikit-learn" icon="chart-scatter">
    Quantum feature selection plugin
  </Card>

  <Card title="TensorFlow" icon="brain">
    Neuromorphic TF layers
  </Card>

  <Card title="PyQUBO" icon="function">
    Build QUBO/Ising from math expressions
  </Card>

  <Card title="AutoQUBO" icon="wand-magic-sparkles">
    Fujitsu Research — auto-convert Python functions to QUBO
  </Card>

  <Card title="Qubolite" icon="square-root-variable">
    Lamarr Institute — lightweight NumPy QUBO toolbox
  </Card>
</CardGroup>

## Resources

### Book

**Neuromorphic Computing for Computer Scientists** — a complete guide to neuromorphic computing on the Dynex platform. 249 pages, 2024. Available on [Amazon.com](https://www.amazon.com/s?k=Neuromorphic+Computing+for+Computer+Scientists+Dynex), [Amazon.co.uk](https://www.amazon.co.uk/s?k=Neuromorphic+Computing+for+Computer+Scientists+Dynex), and [Amazon.de](https://www.amazon.de/s?k=Neuromorphic+Computing+for+Computer+Scientists+Dynex).

### Benchmarks & Publications

* [Benchmarks](https://dynex.co/learn/benchmarks)
* [Scientific Publications](https://dynex.co/learn/scientific-publications)
* [Use Cases](https://dynex.co/learn/partnerships-customers)

### Medium Guides

Step-by-step articles covering real-world implementations on the Dynex platform:

* [Quantum Self-Attention Transformer on Dynex](https://medium.com/dynex)
* [13-bit Full Adder Quantum Circuit on Dynex](https://medium.com/dynex)
* [Grover's Algorithm on Dynex](https://medium.com/dynex)
* [Shor's Algorithm on Dynex](https://medium.com/dynex)
* [Stock Portfolio Optimisation with Quantum Algorithms on Dynex](https://medium.com/dynex)
* [Image Classification on the Dynex Neuromorphic Platform](https://medium.com/dynex)
* [IBM Qiskit 4-Qubit Full Adder Circuit on Dynex](https://medium.com/dynex)
* [Benchmarking the Dynex Neuromorphic Platform with the Q-Score](https://medium.com/dynex)
* [Enhancing MaxCut Solutions: Dynex's Benchmark on G70](https://medium.com/dynex)