Connecting interpretable logic with IBM Quantum execution to solve complex business decisions.
We deconstructed our QAOA paper into 4 critical milestones.
The process begins by transforming raw, unstructured business data into a clean, model-ready state. We utilize Databricks to bridge the gap between classical cloud architecture and quantum-compatible logical structures.
We encode business constraints into a weighted graph and formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Feasibility is enforced through penalty terms that discourage constraint violations.
Our pedagogic QAOA model optimizes an artificial neuron in a binary decision context. We execute high-depth circuits (p=4) on IBM hardware, navigating the noise typical of NISQ-era computing.
By scanning penalty weights (λ=22) it identifies the optimal regime where success probability reaches 100%. We establishing a noise-free reference against hardware-executed bitstring distributions.
QAOA provides stable, transparent decision-making tools for small business. By identifying optimal parameter regimes, KentryOps bridges the gap between complex algorithms and trustworthy practical results.
Nodes → locations
Edges → travel cost
1 → 2 → 3
Route through node 2 is
selected.
The algorithm avoids the direct route (1–3) and selects a lower-cost path through node 2.
Nodes → energy units (solar, battery, load)
Edges →
transfer cost / efficiency
1 → 2 → 3
Energy is routed through
storage.
Energy is routed through storage to minimize loss, instead of direct inefficient transfer.
Nodes → care units
Edges → time / priority cost
1 → 2 → 3
Patient is routed through
intermediate unit.
The system routes the patient through an intermediate step to improve outcome and reduce overall cost.
Detailed variation of penalty weight λ vs success probability (Quantum Coherence). The Emerald Green peaks represent the optimal stability region for business-grade decisions.
We help businesses model their complexity for the next generation of computing.