01 / QPU_FABRIC
Quantum Fabric
Unified access to superconducting, trapped-ion and photonic processors. Automatic backend selection per job.
The quantum-classical ML platform trusted by research labs and frontier AI teams. Hybrid algorithms, distributed training, and a developer surface that doesn't feel like 1998.
Every quantum-classical building block you need to train, serve and observe models — in a single, type-safe SDK.
Unified access to superconducting, trapped-ion and photonic processors. Automatic backend selection per job.
Distributed training across H100 clusters. Gradient checkpointing and offload handled by the runtime.
Schedule quantum + classical workloads in the same job graph. Failover, retry and cost-budgeting built in.
Columnar storage optimised for ML — petabyte datasets streamed with sub-millisecond latency.
End-to-end traces for hybrid jobs — from classical preprocessing through the quantum circuit and back.
Policy-as-code for data access, model deployment, and cost caps. SOC 2 and ISO 27001 certified.
Quantum-classical pipelines are easy to start, hard to operate. QML's runtime handles the hard parts so your researchers can focus on the model — not the scheduler.
Circuit transpilation targeted to the specific backend, with error-correction strategies baked in.
Jobs routed to the cheapest QPU meeting your SLA. Queue-aware. Budget-enforcing.
Hybrid execution with lock-step classical preprocessing and postprocessing on adjacent GPUs.
Live traces with fidelity, duration and cost per shot. Exported to your stack via OTLP.
"We used to wait forty minutes for a single hybrid job to schedule. On QML, it's twelve seconds — and the tracing tells us exactly where the time went."Dr. Yuki TanakaHead of Quantum ML · Anthropic Research
Request production access, or jump straight into a free research tier with 8 QPU-hours per month.