Classiq, the leading software platform for enterprise-grade quantum computing engineering and development, announced the availability of Classiq 1.0, a major version milestone. It brings together strategic platform improvements to provide a robust foundation for teams building, verifying and running quantum software in real-world environments.
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For years, quantum progress has been measured in qubits, papers and demos. As organizations move from experimentation to engineering, the challenge is turning that progress into software workflows that teams can trust over time. Until now, challenges included correctness, repeatability, cost control and the ability to adapt as hardware evolves.
Classiq 1.0 is designed for enterprise quantum R&D groups, algorithm developers, researchers and engineering teams that need to connect classical logic and constraints to quantum models and carry that intent through optimization and execution.
“Classiq 1.0 is a commitment to quantum software as an engineering discipline,” said Nir Minerbi, CEO and co-founder of Classiq. “Teams need a platform they can build on with confidence, where correctness is enforced by default, and the path from problem definition to execution is continuous. This release unifies what we have delivered across the platform into a production-ready framework for long-term quantum engineering.”
Classiq 1.0 consolidates advancements delivered across recent development cycles spanning language expressiveness, compiler correctness, execution, visualization and developer experience. The new release also integrates AI-driven guidance in the platform to help translate intent into correct, optimized quantum models while keeping workflows explicit and inspectable.
Key Features in Classiq 1.0
- Correct-by-construction enforcement – Uncomputation is applied by default, local variables are cleaned up automatically, and correctness violations are surfaced as hard errors that must be resolved before execution.
- Expanded expressiveness in Qmod – Updates include classical local variables, runtime conditionals and mid-circuit measurements; richer assignment semantics such as arrays and in-place updates; and built-in modular arithmetic primitives. Classiq 1.0 also supports generative quantum functions using familiar Python control flow, along with bidirectional conversion between Qmod and QASM for interoperability.
- Deeper visibility and debugging – Transparency is added across model structure and control flow, including views designed to help developers inspect how high-level logic maps to generated quantum programs and how data and state evolve during execution.
- Hardware-aware execution across simulators and QPUs – Models adapt to changing backend constraints while preserving logical intent, with execution and iteration integrated into the development workflow, including cost tracking and support for GPU-based simulation and HPC deployments.
“Quantum development teams need tooling that reduces fragile handoffs and makes complex programs easier to inspect, debug and maintain,” said Lior Gazit, Vice President of Product at Classiq. “With 1.0, we focused on correctness enforced by the platform, an expressive modeling and programming layer that matches real problem structure, and a development experience that keeps execution, analysis and transparency in the loop as teams scale their work.”
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