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Released on 2026-02-02

Classiq 1.0

Classiq 1.0 consolidates capabilities introduced in recent releases into a stable, production-ready baseline.

This release brings major gains in Qmod expressiveness, stricter correctness guarantees around uncomputation, broader algorithm coverage, and new developer tooling. It introduces Classiq Studio, the Classiq AI code assistant, model-based visualization, and meaningful performance and usability improvements across the platform. Stability and reliability were strengthened, simulators were scaled, and integrations with new quantum hardware were formalized.

Highlights

Language and compiler features

Algorithmic components

Key algorithmic building blocks, along with their full Qmod implementations, are available as part of the Classiq 1.0 open-library:

Execution

  • Run-via-Classiq support across multiple quantum simulator and hardware providers.
    • One-click execution from a unified platform.
    • Execution cost limiting and tracking.
  • Streamlined onboarding of new quantum backends through a new integration protocol.
  • State-vector filtering logic for scalable state-vector simulation.
  • Stable, GPU-based simulators, including configurations optimized for hybrid (QML-focused) workflows.
  • Deployment of simulators on Cineca’s HPC infrastructure.
  • Improved post-processing using pandas DataFrames.

Studio

The Classiq Studio continues to evolve as a dedicated environment for quantum development. It includes built-in visualization, debugging tools, and AI assistance for model generation, optimization, and execution. Workspace loading, environment management, version control, and memory and CPU usage were improved. Your work is automatically saved, dependencies are managed, and you can run all required tasks from a single environment.

AI assistant tool

Classiq 1.0 includes two paths to AI-assisted quantum development:

  • The Classiq AI code assistant, integrated directly into Classiq Studio, provides in-context help for quantum program creation without additional setup.
  • An initial prompt for local workflows, enabling local AI agents to use Classiq context when generating quantum code.

Visualization

Classiq provides model-based visualization of quantum programs, representing synthesized programs in terms that reflect both their high-level algorithmic intent and their low-level implementation, bridging abstraction layers intuitively.

  • Quantum variable lifetimes, quantum expressions, and controlled operations are presented graphically, supporting reasoning about correctness, resource usage, and functionality.
  • Hierarchical interactive navigation enables easy exploration across abstraction layers, mapping gate/qubit-level implementation decisions, including those of the synthesis engine, back to higher-level model elements.
  • Visualization of synthesized programs is available via the Python SDK, IDE, and Studio, and can be shared for debugging, reviews, and collaboration.

General improvements

  • Added profile and organization settings to improve user and admin control.
  • Documentation improvements:
  • IDE usability improvements:
    • Improved appearance of result histograms and bar plots in the platform result page.
    • Improved native Qmod library usability.
    • Enhanced the formatting of labels for slice operations in QP Visualization.
  • Classiq Studio:
    • Added light-mode support for the Quantum Program (QP) visualizer. Useful for publications and articles.
  • C12 carbon-based QPU emulators are now available - see the Cloud Providers section in the user guide.
  • Bug Fixes:
    • QP Visualization - Fixed an issue where variable labels were not displayed for "Bind" operations of type "Split" and "Merge". These labels now appear correctly, making it easier to understand the operation flow in your quantum programs.