Classiq has launched a Fault Tolerance Engine that translates optimized logical quantum programs into fault-tolerant execution plans tailored to specific hardware, extending its architecture-aware design-automation approach into fault-tolerant compilation and planning.

The engine estimates essential physical-resource metrics—qubit needs, error-correction cycles, code distance, runtime, routing, scheduling, and accumulated error—while accounting for costly fault-tolerant operations like T gates and magic-state resources.

It describes a broader workflow from high-level algorithms (Qmod) to fault-tolerant execution planning, integrating error-correction considerations early in development.

Supporting media materials and contact information are provided.

The tool enables evaluating physical implementability on specific hardware by considering machine-specific noise characteristics, identifying bottlenecks and necessary improvements.

It aims to serve a broad range of stakeholders—from developers and enterprises to researchers—by enabling reasoning across the algorithm-to-physical realization gap under error correction.

This capability extends Classiq’s existing modeling and synthesis workflow to assess physical feasibility and resource needs of quantum applications on real machines.

Classiq, founded in 2020 and based in Tel Aviv, positions itself as bridging quantum algorithms and their physical realization through a model-first approach and strategic partnerships.

Nir Minerbi notes that fault tolerance shifts the software stack’s role and expands the gap between algorithm and physical realization, a gap the Engine is designed to bridge.

The platform remains architecture-aware and integrates fault-tolerant compilation and execution planning without creating a separate environment.

Early evaluation of hardware requirements is crucial in fault-tolerant computing to determine feasibility and necessary improvements due to the complex mapping from logical to protected physical implementations.

The Engine helps answer practical questions about required physical qubits, error-correction cycles, runtime, bottlenecks, and necessary hardware or software improvements for practicality.