Classiq
Turns high-level quantum program models into circuits, with browser tools and a Python SDK for synthesis, execution and result inspection.
Israel / United States · Research reviewed 7 Sept 2026Latest 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.
2 evidence sources added · axis ratings unchanged
Assessment reviewed 6 Sept 2026 · Research reviewed 7 Sept 2026What 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)
- Hello World — Classiq Docs ↗
- Quantum Linear Solvers for CFD: From Algorithmic Promise to Practical Performance ↗
- NVIDIA, Rolls-Royce and Classiq Announce Quantum Computing Breakthrough for CFD in Jet Engines ↗
- Classiq 1.0 ↗
- Registration — Classiq Docs ↗
- Approximate Quantum Linear Solvers for Hybrid CFD: End-to-End Analysis with a Chebyshev-LCU Approach ↗
Initial assessment recorded
Assessment reviewed 6 Sept 2026
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 ↗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.
Sources & evidence
- Hello World — Classiq Docs ↗Organization-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.
- Quantum Linear Solvers for CFD: From Algorithmic Promise to Practical Performance ↗Organization-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.
- NVIDIA, Rolls-Royce and Classiq Announce Quantum Computing Breakthrough for CFD in Jet Engines ↗External 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.
- Classiq 1.0 ↗Organization-originated source · Publication date not stated
Classiq describes automatic uncomputation, compiler correctness checks, execution workflows and platform stabilization in its 1.0 release.
- Registration — Classiq Docs ↗technical · Publication date not stated
States that platform access is invitation-only and that free access for noncommercial purposes is not currently available.
- Approximate Quantum Linear Solvers for Hybrid CFD: End-to-End Analysis with a Chebyshev-LCU Approach ↗paper · 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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