Depthmetric Aviation

Disruption-aware routing with verification and bounded self-healing

Management brief
Aviation Customer
The management idea: when weather, closures, conflict, or capacity constraints change the network, do not trust yesterday's cheapest route. Reprice the affected network, solve again, and verify every recommendation before it reaches operations.

What the system does

1. ReadAirport, route, cargo, weather, closure, and capacity evidence.
2. RepriceRaise the cost of edges exposed to disruption.
3. SolveKeep an exact Classical result and compare a Quantum research lane.
4. VerifyCheck route completeness, constraints, cost traceability, and provenance.
5. ControlAllow, warn, repair once, ask a person, or block.

Evidence already produced

Paper hardware scope8QPU backends across superconducting and trapped-ion systems
Paper optimal result87.3%63 controlled 16-qubit hardware trials
Local Aer experiment4,096Measured shots on a genuine 16-qubit p=2 warm-start circuit
Aer valid shots99.37%4,070 decoded valid routes in the seeded four-airport golden run

Classical and Quantum have different jobs

Classical solver: operational authority

For the current small mission it checks every required hub order, returns the exact least-cost disrupted route, and provides the reference used by Depthmetric Aviation verification.

Quantum solver: controlled research comparison

It receives the same four-airport cost matrix. The present value is reproducible cross-method evidence and learning about harder optimization, not a claim that it is faster or cheaper today.

What is real today, and what is not

Real and executable

Cost-warp mathematics, exact Classical solving, Depthmetric Aviation checks, self-healing drill, Qiskit Aer worker, shot decoder, provenance hashes, and a historical NOAA Schiphol fixture.

Research evidence

The paper reports +81.8%, +85.7%, and +83.4% disruption-cost changes across simulator/IBM, Rigetti Cepheus-1, and IonQ Forte-1 configurations.

Still a demo boundary

The six map disruptions and cargo cost-per-kilometre values are transparent proxies. The web Quantum lane demonstrates workflow behavior; Aer currently runs as a separate reproducible worker.

Data requested from the Aviation Customer

Network and operations
  • Airport and lane catalogue, schedules, aircraft and capacity
  • Historical and planned route legs with timestamps
  • Delay, cancellation, diversion, closure, and ATC events
  • Approved alternate airports and operational constraints
Cargo, cost, and outcomes
  • Shipment class, priority, cold-chain and service commitments
  • Booked capacity and shipment-level route outcomes
  • Fuel, handling, delay, diversion, penalty, and recovery costs
  • Decision logs needed to compare recommendations with reality

Proposed customer pilot

Eight to twelve weeks, one corridor, one historical disruption windowReplay the event with customer-approved data; compare normal planning, disruption-aware Classical planning, and the Quantum research lane; verify every output; report cost, feasibility, recovery time, and evidence gaps.
Customer receivesA reproducible benchmark, auditable route evidence, data-quality findings, an honest quantum/no-quantum comparison, and a go/no-go recommendation for operational integration.