Quantum-Aware Intelligence and Planning. An execution intelligence layer for quantum circuits.
The problem it solves
Choosing a quantum backend is not obvious. Two backends can have identical specs on paper: same qubit count, same architecture, yet produce completely different results depending on their calibration state that day.
Without calibration data, you're guessing. With QAIP, the right backend is chosen automatically, every time.
How it works
01
Circuit fingerprinting: classifies your circuit by what it demands: readout quality, coherence time, or topology match.
02
Calibration scan: fetches live calibration from IQM, IonQ, and IBM hardware APIs. Real data, not static specs.
03
Adaptive scoring: weights shift based on circuit type. A shallow circuit weights readout at 70%. A globally-entangled circuit weights topology at 30%.
04
Backend ranking: scores all available backends across four dimensions: readout quality, coherence margin, topology fit, capacity.
05
Explanation: plain language reasoning for why the top backend was chosen, and a confidence signal.
Beyond ranking
This page demonstrates QAIP's calibration-aware ranking primitive. The full QAIP system also handles qubit mapping, routing cost estimation, and execution strategy selection.
from qaip importQuantumAdvisor
advisor = QuantumAdvisor()
report = advisor.analyze(your_circuit) print(report.summary())