Eve Count Quantum Systems · 01 · Start with an operational bottleneck. Select the architectural plate to inspect full size.

01 · Start with an operational bottleneck

Quantum advantage never begins with a qubit count; it begins with a coupled operational constraint.

Most enterprise quantum proofs-of-concept fail in their first week because teams pick a familiar analytics or reporting workflow, wrap it in a quantum SDK, and discover that standard SQL, linear programming, or gradient boosted trees already solve it orders of magnitude faster.

Identify coupled combinatorial structure

Where do hybrid quantum-classical formulations actually earn their keep? Look for discrete operational bottlenecks where choosing one option changes the feasibility or cost of many other options simultaneously:

• Maritime & Fleet Operations: Assigning vessels to berth windows and quay cranes where draft limits, tidal windows, and yard congestion couple every assignment.
• Mining & Bulk Resources: Pit-to-port ore blending and haulage scheduling under non-linear grade penalty thresholds.
• Financial & Treasury Operations: Intraday collateral allocation and multi-lateral netting under discrete bucket haircuts.

In our harness, we represent a candidate workload using WorkloadSpec before selecting any solver:

from examples.combinatorial_triage import WorkloadSpec, triage_workload

spec = WorkloadSpec(
    name="terminal_berth_crane_window",
    decision_variables=48,
    coupling_density=0.38,
    hard_constraints=14,
    sla_budget_ms=1500,
    telemetry_completeness=0.96,
)
decision = triage_workload(spec)
assert decision.route == "HYBRID_QUBO_CANDIDATE"
print(decision)

Notice what triage_workload checks:

1. Telemetry completeness (0.96): Are at least 85% of the required constraint inputs clean and available at decision time?
2. SLA budget (1500 ms): Does the operational window allow a hybrid solver round-trip?
3. Coupling density (0.38): Are the discrete variables densely coupled, or can the problem be relaxed into independent linear sub-problems?

Try with your AI coding assistant

Paste the snippet above into your assistant and ask: "Inspect WorkloadSpec in examples/combinatorial_triage.py. Propose three real operational workloads from our industry—one that routes to CLASSICAL_MIP_OR_GREEDY, one that routes to CLASSICAL_DETERMINISTIC_FAST_PATH, and one that qualifies as HYBRID_QUBO_CANDIDATE."

Checkpoint

Before advancing to mathematical formulation, your team should document:

• One named operational decision bottleneck with discrete choices,
• Why choices are mutually coupled (why greedy local picks create downstream conflicts), and
• The decision window SLA in milliseconds or minutes.