Squarewell Fabric orchestrates quantum experiments across IBM, Google, and AWS—while automatically preserving every result, parameter, and insight in your infrastructure.
Every experiment, parameter tweak, and result is automatically logged and versioned. When your lead researcher leaves, the knowledge stays in your infrastructure—not on their laptop.
# Experiment automatically tracked
from squarewell import FabricRun
run = FabricRun(
project="molecular-simulation",
experiment="vqe-optimization-v2"
)
# Every parameter, circuit, and result
# is versioned and stored centrally
result = run.execute(circuit, params)
# → View full history in W&B dashboardStop manually submitting jobs to IBM, Google, and AWS queues. Fabric orchestrates variational workloads across all major backends—automatically routing for cost, queue depth, and noise.
Cost-aware backend selection
Minimize wait times across providers
Automatically select best hardware
# Airflow DAG with Fabric operators
from airflow import DAG
from mahout_providers import QiskitOperator, CirqOperator
with DAG("quantum_pipeline") as dag:
# Fabric handles routing to best backend
vqe_step = QiskitOperator(
task_id="run_vqe",
circuit=variational_circuit,
optimizer="SPSA"
)
# Results flow to W&B automatically
vqe_step >> WandbCallback(
project="quantum-research"
)No new proprietary tools to learn. Fabric integrates with Apache Airflow, Apache Mahout, and Weights & Biases—the same infrastructure your data scientists already use. Quantum experiments become just another node in your ML pipeline.
Orchestrate quantum jobs as DAG tasks
Variational quantum optimization engine
Experiment tracking and visualization
Leverage our infrastructure for running hybrid classical-quantum algorithms. Seamlessly orchestrate workloads between classical compute and quantum processors.
Variational Quantum Eigensolver
Quantum Approximate Optimization
Quantum Machine Learning