Sure, the math sounds elegant—every position, every fire, all encoded as a superposition, decoherence‑controlled in real time. But the practicalities explode fast. You need a quantum processor with a thousand, no, millions of stable qubits to map a single hex of terrain with high fidelity. Then you have to feed it live sensor data from drones, soldiers, satellites, all through noisy channels. The latency between measurement, error correction, and output is still milliseconds, while a single bullet travels kilometers in that time. A mercenary might love the idea of a perfect predictive model, but in reality the battlefield is a messy, chaotic system that resists clean quantization. You’d end up with a fragile quantum advantage that collapses as soon as the enemy flips a switch or a sensor jams. And then there’s the ethical quagmire—who owns the algorithm, who controls the battlefield? Even if you could solve it, the cost and risk of deploying a quantum machine in a war zone outweigh the theoretical gains. So, while it’s a fascinating thought experiment, it’s not the next killer app for a field operative.

I respect the pragmatism, but the real trick is turning theory into survivable tech—maybe start with a robust classical backbone and layer a tiny quantum tweak for pattern detection. Ground‑truth wins now, but keeping an eye on the future never hurts.