Run quantum and classical AI side‑by‑side without babysitting infrastructure. The Hybrid Orchestrator assigns work across QPU, GPU, and CPU pools with backoff/retry logic, lease‑based locking, and per‑job SLAs. It wraps quantum nodes (e.g., PennyLane/Qiskit) and deep‑learning layers (PyTorch/JAX) so teams can compose mixed pipelines that are observable and production‑ready.
What
- A light scheduler + resource manager for hybrid quantum/classical workloads.
- Dispatches jobs to QPU/GPU/CPU with queueing, backpressure, timeouts, and automatic retries.
- Pluggable adapters to wrap your QNodes/Torch layers for clean, testable orchestration.
Who it’s for (Industries)
- R&D labs running quantum experiments at scale
- Financial services quants and risk teams
- Bioinformatics and materials science groups
- Any team coordinating scarce QPU time with classical pre/post‑processing
Service forms
- Platform hardening with SLA’d run‑ops (we operate and monitor your stack)
- Licensed component embedded into your platform (self‑managed)
ROI levers
- Higher utilization of scarce QPU minutes via efficient queueing and batching
- Fewer failed jobs/re‑runs thanks to deterministic retries and timeouts
- Less engineer “babysitting” time through automation, alerts, and runbooks
Key capabilities
- Resource pools for QPU/GPU/CPU with quotas and fair scheduling
- Lease‑based locks to prevent duplicate execution; idempotent job semantics
- Per‑job SLAs: max runtime, max retries, circuit depth/shot limits
- Observability: structured logs, metrics (success/failure, wait time, runtime), traces
- Adapters: PennyLane, Qiskit; PyTorch/JAX for classical parts; REST/gRPC interfaces
- Execution modes: batch, streaming, and interactive notebooks
- Governance: audit logs, role‑based access, experiment lineage and artifacts
Architecture at a glance
- Ingress (REST/gRPC) → Job queue → Scheduler → Executors (QPU/GPU/CPU) → Results store/metrics
- Optional: vector DB for experiment metadata; S3/Blob for artifacts
Example flows
-
Feature engineering on GPU → quantum kernel evaluation on QPU → classifier head on GPU → metrics
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Parameter sweep for QNN: grid generates N jobs; scheduler fans out to QPU with depth/shot caps; results merged
Delivery outline
- Discovery (data, circuits, SLAs, environments)
- PoC: stand up orchestrator, wire 1–2 flows, define metrics and alerts
- Hardening: HA queue, retries/leases, dashboards, runbooks
- Rollout: CI/CD, permissions, cost controls, on‑call and SLOs
Success metrics (examples)
- QPU utilization ↑, average queue wait ↓, job success rate ↑
- Mean runtime and p95 latency ↓ for hybrid pipelines
- Engineer hours on run‑ops ↓; on‑call pages/week ↓
For a tailored workshop or a pilot, reach out via the contact page or the website.