Quantum-AI and The Road to a Million Qubits: Why the Hard Part Is Now Systems Engineering
Published:
Quantum isn’t “a qubit problem.” It’s a systems + infrastructure problem.
When people picture quantum computing, they imagine a refrigerator-sized machine in a lab.
A useful, fault-tolerant quantum computer looks a lot more like a data center campus — because at million-qubit scale, the hard parts aren’t just physics. They’re power, cooling, wiring, control, and operations … just like Manhattan-size AI datacenters with power and cooling to match!
In my new paper, I map the quantum race end-to-end, applying lessons from recent AI infrastructure development to quantum infrastructure:
Infrastructure reality: a million-qubit system likely demands data-center-class power (on the order of 10–20 MW, by rough estimate) plus industrial cooling and cryogenics.
Control is the bottleneck: “one million coaxial cables is impossible” — so the field is being forced into multiplexing, photonic links, and cryo-CMOS control stacks.
Why this matters: recent resource estimates suggest ~1M physical qubits running ~1 week could be sufficient to break RSA-2048 under realistic assumptions (remember the Gidney paper?).
Who’s leading (and how): IBM, Google, Microsoft, Intel, Quantinuum, IonQ, Rigetti, D-Wave, PsiQuantum, Xanadu—each betting on a different path to scale.
I also argue we’re entering a new phase: frontier AI systems (Gemini/ Claude/ ChatGPT-class) will compress the research loop — accelerating literature synthesis, hardware/software co-design, control-code generation, and experiment planning — so “time to insight” becomes a strategic advantage.
All of this information is publically available. My paper just summarizes hundreds of research papers, corporate and academic websites, and market analysis.
Read it here: Quantum_AI_Deep_Dive.pdf
Final Thoughts:
If quantum’s finish line is a campus-scale build (not a lab demo), who is your organization betting on — and what would it take to place a credible, defensible bet now?