Fermioniq
Calculates samples and observables for quantum circuits with adjustable tensor-network compression.
Netherlands · Research reviewed 11 Sept 2026Initial record: initial assessment recorded.
Review history 1 record
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.
Initial assessment recorded
Assessment reviewed 11 Sept 2026 · Research reviewed 11 Sept 2026Sources in this record (3)
What it does. Where it fits.
GPU-accelerated matrix-product-state classical emulation.
Who should look closer
Algorithm developers whose circuits permit controlled tensor-network approximation.
QubitWire’s editorial assessment of practical fit.Token-based emulator service
Request credentials and follow the versioned quick start; confirm supported features for the chosen native or CUDA-Q interface.
Check the current access route ↗What has been demonstrated
Vendor benchmarks cover Ising-model circuits with a declared bond dimension. NVIDIA provides remote-backend examples, corroborating integration rather than application superiority.
What remains unresolved
Matrix-product-state cost grows with entanglement; truncated simulations trade accuracy for resources. The CUDA-Q backend does not expose every native feature.
The next question to watch
External benchmark studies reporting accuracy and cost across circuit structures and entanglement regimes.
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
- Ava — Fermioniq ↗Organization-originated source · Publication date not stated
Company reports matrix-product-state simulation benchmarks for 36–100-qubit Ising circuits on H100 hardware at a stated bond dimension, with an accuracy/resource trade-off.
- Fermioniq emulator — Quick Start ↗Organization-originated source · Publication date not stated
Versioned documentation covers credentials, Qiskit/Cirq circuits, observables, GPU/noise settings, bond dimension, jobs and returned results.
- CUDA-Q Tensor Network Simulators — Fermioniq ↗Organization-originated source · Publication date not stated
NVIDIA documents the remote Ava backend, credentials and code examples; this integration exposes a simplified feature set without noise simulation.
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