Quantum-Secured AI-Driven Drone Logistics for Real-Time Healthcare Delivery

Abstract This paper presents a quantum-resilient autonomy stack for medical-drone delivery that elevates communications and cryptography to first-class, stateful variables within motion planning rather than downstream constraints. A three-layer Air–Ground–Communications architecture integrates beyond-6 G (FR3/THz) links with ultra-reliable low-latency communication (URLLC) fallback and couples BB84-style quantum key distribution (QKD) with principled post-quantum cryptography (PQC) switching to ensure cryptographic continuity under mobility and adverse weather. At the core, a hybrid AI–RKF45 controller fuses a lightweight neural policy with the integrator’s local error, co-adapting control aggressiveness and solver step size for stiffness-aware manoeuvres and rapid re-optimisation under disturbances. The planner directly ingests link SNR, URLLC queueing delay, QKD quantum bit error rate (QBER), secure key rate (SKR), key-buffer levels, meteorological risk, and battery state-of-health, shaping a multi-objective cost that jointly minimises energy and control-loop end-to-end latency (sensing–compute–communication round-trip), while enforcing geofencing and Beyond Visual Line of Sight (BVLOS) constraints. In large-scale regional simulations (200 km $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 200 km; up to 200 UAVs), the framework achieves a mean control-loop latency of 2.34 s (distinct from door-to-door delivery time), an 18% reduction in energy per sortie, and a 98.2% mission success rate, outperforming static planners, deep reinforcement learning (PPO/DQN), and model predictive control under matched compute budgets. The QKD $$\leftrightarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>↔</mml:mo> </mml:math> PQC state machine applies conservative thresholds (QBER $$\le $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≤</mml:mo> </mml:math> 8%, SKR $$\ge $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≥</mml:mo> </mml:math> 5 kbps with key-buffer hysteresis), yielding rare, millisecond-scale fallbacks that preserve latency guarantees. Complexity scales as $$\mathcal {O}\!\left( \frac{n}{\nu }T\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mspace/> <mml:mfenced> <mml:mfrac> <mml:mi>n</mml:mi> <mml:mi>ν</mml:mi> </mml:mfrac> <mml:mi>T</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> , maintaining 2–5 s update times for fleets exceeding 200 vehicles. Reproducible artefacts and a staged pathway from hardware-in-the-loop to BVLOS trials support near-term healthcare deployment.