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COMPANY RESEARCH · Software & applications

Classiq

Turns high-level quantum program models into circuits, with browser tools and a Python SDK for synthesis, execution and result inspection.

Official website ↗
Israel / United States · Research reviewed 7 Sept 2026

Latest record: 2 evidence sources added · axis ratings unchanged.

Review history 2 records

Record dates show when an assessment or clarification was saved. Review dates show the evidence check. Earlier records were recovered from QubitWire’s source history; no earlier score movement is inferred.

  1. 2 evidence sources added · axis ratings unchanged

    Assessment reviewed 6 Sept 2026 · Research reviewed 7 Sept 2026
    What changed
    Research brief
    PreviouslyNone recorded
    NowInitial technology, access and evidence brief
    Evidence source
    PreviouslyNone recorded
    Nowhttps://docs.classiq.io/getting-started/registration_installations
    Evidence source
    PreviouslyNone recorded
    Nowhttps://arxiv.org/abs/2606.01067
    Sources in this record (6)
TECHNOLOGY & ACCESS

What it does. Where it fits.

Quantum software: high-level modeling, circuit synthesis and compilation

Who should look closer

Developers exploring quantum algorithms and engineering teams comparing circuit resource requirements.

QubitWire’s editorial assessment of practical fit.

Request platform access

The documentation is public. Running the platform requires an invited account; the current registration page says free noncommercial access is unavailable.

Check the current access route ↗
DOCUMENTED EVIDENCE

What has been demonstrated

A Classiq–Rolls-Royce preprint studies a hybrid fluid-flow solver through numerical simulation and compiled resource estimates. In its tested case, approximation reduces single-qubit rotation requirements by more than an order of magnitude while preserving convergence.

What remains unresolved

The fluid-flow result is a small numerical study and resource estimate. It does not establish industrial quantum speedup or successful execution of that application on useful fault-tolerant hardware.

The next question to watch

Will the resource savings survive realistic measurement, data-loading and fault-tolerance costs at industrial problem sizes?

A research question, not a promised milestone.

QubitWire coverage

No published QubitWire stories currently match this organization. The evidence sources below provide the starting point.

CHECK THE ORIGINALS

Sources & evidence

  1. Hello World — Classiq DocsOrganization-originated source · Publication date not stated

    Documentation demonstrates modeling, synthesis, execution and analysis through browser tooling or a Python SDK, and states account access is invitation-only.

  2. Quantum Linear Solvers for CFD: From Algorithmic Promise to Practical PerformanceOrganization-originated source · 15 Jun 2026

    Classiq and Rolls-Royce authors describe a numerical hybrid CFD study with approximate quantum linear solvers. The small-scale study excludes measurement errors and reports resource/convergence trade-offs.

  3. NVIDIA, Rolls-Royce and Classiq Announce Quantum Computing Breakthrough for CFD in Jet EnginesExternal source (may be a collaborator) · 21 May 2023

    NVIDIA confirms a collaboration that designed and GPU-simulated a 39-qubit circuit with 10 million layers; it was not an execution of that circuit on a quantum processor.

  4. Classiq 1.0Organization-originated source · Publication date not stated

    Classiq describes automatic uncomputation, compiler correctness checks, execution workflows and platform stabilization in its 1.0 release.

  5. Registration — Classiq Docstechnical · Publication date not stated

    States that platform access is invitation-only and that free access for noncommercial purposes is not currently available.

  6. Approximate Quantum Linear Solvers for Hybrid CFD: End-to-End Analysis with a Chebyshev-LCU Approachpaper · 31 May 2026

    Preprint reports numerical convergence studies and explicit compilation/resource comparisons for approximate quantum linear solvers within a hybrid CFD workflow.

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