Have you ever wondered what runs AI?

Today, AI runs on specialized computing power (GPUs, TPUs, and AI accelerators) and high-speed networks. To deliver real-time AI-driven outcomes, compute is placed as close as possible to where AI is needed—whether in data centres, the cloud, or at the edge (like in self-driving cars or factory robots).

As AI models get bigger, faster, and more complex, even the most advanced AI infrastructure is reaching its limits. So… what’s next? Enter Quantum Computing.

Quantum computing is an evolving area of computing that processes information differently from traditional systems. Instead of using bits (which are either 0 or 1), quantum computers use qubits, which can exist in multiple states at once. This allows them to solve complex problems much faster than classical computers.

This will transform AI by enabling:

 🚀 Faster AI training – Cutting down model training from months to hours

 🔬 Smarter problem-solving – Tackling challenges like drug discovery and climate modelling that classical AI struggles with

 ⚡ More energy-efficient AI – Reducing the huge compute power required for AI today

Have a look at two exciting quantum AI projects in development today:

🔹 Google’s "Willow" – A superconducting quantum processor designed to push the boundaries of AI computing, with a focus on increasing quantum coherence and scaling up qubit reliability. https://quantumai.google 

 🔹 IBM’s "Mid" – A next-generation quantum processor built to improve quantum error correction and enable more stable AI-driven quantum computations. https://quantum.ibm.com 

As research accelerates, projects like Willow and Mid are bringing us closer to a future where Quantum AI could unlock entirely new possibilities.

Of course, Quantum infrastructure isn’t just about computing power—it also requires next-generation networking to connect these advanced systems across the globe. More on that next week!