Qonscious is a runtime framework designed to support the conditional execution of quantum circuits based on resource introspection. It helps you build quantum applications that are aware of backend conditions — such as entanglement, coherence, or fidelity — before execution.

Why Qonscious?

In the NISQ era, quantum hardware is noisy, resource-limited, and variable over time. Static resource assumptions lead to unreliable results. Qonscious makes quantum programs introspective and adaptive.

For a deeper discussion on the motivation behind Qonscious, read our article

Key Features

• Figures of Merit evaluation (e.g., get CHSH score, T1, T2, ...)
• Conditional execution on compliance with figures of merit checks
• One circuit, many backends: abstract backends and hide complexity behind adaptors (currently available for SampleV2, Aer Simulator, IBM Backends, IBM Simulators, IONQ backends)
• Inversion of control: pass a callback, not only a circuit
• Rich, uniform results from all backends, including backend configuration, and any figures of merit you need as conditional context
• Built-in logging, extensibility, and fallback logic

Use cases

These are some scenarios where you may use Qonscious:

• Run a circuit conditional on your target computer (or simulator) checking some figures of merit (e.g., number of qubits, CHSH score, etc.)
• Benchmark a computer (or simulator) in terms of a collection of figures of merit.
• Explore correlations between experiment results and figures of merit of a given computer (or simulator)
• Explore correlations between figures of merit on a given computer (or simulator)
• ...

Installation

We encourage installing Qiskit via pip to make sure you have the latest released version:

pip install qonscious

If you preffer working on the source code (or you'd like to contribute to the development of Qonscious read the instructions for contributos)

Examples

The notebooks folder contains several examples of using Qonscious in different use cases.

We suggest you start with chsh_test_demo.ipynb which is also available as a Google Colab Notebook. There is even a youtube tutorial covering this specific usage example.

Documentation

Up-to-date documentation is available on github pages

The API reference's home page provides a good overview of all important elements and their relationships.