Most executives think quantum computing is a 2030 problem.
Are you one of them too?
A prototype benchmark — 6 satellites, three algorithms, one uncomfortable result.
The Problem
The scheduling software running your constellation was designed for a fraction of today's scale.
The space economy is managing 10,000+ active satellites today. Every new launch makes the scheduling, routing, and collision-avoidance problem harder — not linearly harder. Combinatorially harder.
Classical optimisation handles this with heuristics. Good heuristics. But heuristics that, by design, trade solution quality for computational speed. That trade-off has a ceiling — and we can now measure it.
The Benchmark
I built a prototype for you.
Nothing exotic — just classical Python. Assign 6 satellites to two ground stations, minimising frequency conflicts across the constellation. Three approaches competed.
QAOA p=2 already breaks past the classical ceiling. QAOA p=3 reaches 90.7% — and keeps improving.
The greedy algorithm failed to find the best answer. Not because it's poorly engineered. Because the problem is structurally hard for sequential reasoning.
Why Now
The integration window is open. Not in 2030 — today.
The companies investing in quantum-classical hybrid systems today are not doing it because quantum is ready. They are doing it because the window is now — before constellations cross 100,000 satellites (ESA projection: before 2030), before the optimisation problem becomes intractable for anything classical, and before competitors have 5 years of proprietary quantum training data.
IBM, AWS, and IonQ are already offering cloud quantum access. ESA and NASA are running hybrid algorithm pilots. The question is not whether to invest. It's whether you want to be in the first cohort or the catch-up cohort.
Three Actions
Worth doing in 2026. Cost: almost nothing.
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01 —Map your hardest combinatorial problems. Routing, scheduling, resource allocation. These are your quantum candidates — the places where heuristics are already leaving value on the table.
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02 —Run a hybrid pilot. AWS Braket or IBM Quantum. Budget: under $50k. Learning: significant. The infrastructure is already cloud-accessible.
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03 —Put one technically literate person in the room. When quantum vendors come calling — not to buy, but to understand what questions to ask. That capability compounds.
The benchmark I ran took an afternoon.
The satellite routing problem it models represents billions in operational efficiency.
Let's explore how quantum computing can transform your complex challenges into an impact.
Methodology note — All results classically simulated via exact 2⁶=64-amplitude statevector. No quantum hardware used or implied. QAOA performance reflects a 6-node toy problem; scaling behaviour on real hardware remains an active research question. The classical greedy approach is capped at 79% on this instance. QAOA finds the optimum but returns a probability distribution — the optimal solution appears in ~56% of measurement shots, requiring classical postprocessing. This is standard practice in NISQ-era hybrid pipelines. Engineering step, not a fundamental limitation. Full simulation code available in the linked Jupyter notebook.