Artificial intelligence has advanced on the back of one simple trend: more compute, more data, bigger models. That trajectory has been remarkably productive — but it is also running into physical and economic limits. This is why quantum computing has moved from a physics curiosity to a serious topic in the AI conversation.
Why classical compute is hitting a wall
Modern AI systems are astonishingly hungry. Training a frontier model can consume as much energy as a small town uses in weeks, and the hardware needed to keep scaling is getting harder and more expensive to build. Meanwhile, some of the problems we most want AI to solve — optimising logistics across millions of variables, simulating molecules for new materials and medicines, or searching enormous combinatorial spaces — grow so quickly that no classical machine can brute-force them.
Where quantum could change the game
Quantum computers do not simply run classical programs faster. They exploit superposition and entanglement to represent and manipulate information in fundamentally different ways. For AI, three areas look especially promising:
Optimisation. Many machine-learning and operations problems reduce to finding the best configuration among an astronomical number of possibilities. Quantum and quantum-inspired algorithms offer new ways to explore these landscapes.
Sampling and simulation. Generative models rely on sampling from complex probability distributions; quantum systems are naturally good at this, and at simulating the quantum chemistry behind new drugs and materials.
Quantum machine learning. A growing field studies models that run partly or wholly on quantum hardware, potentially learning patterns that are hard to capture classically.
A realistic timeline
It is worth being honest about the hype. Today’s machines are noisy and small — the so-called NISQ era — and a fault-tolerant quantum computer capable of broadly outperforming classical AI is still years away. The near-term value is more likely to come from hybrid systems, where a classical model does most of the work and a quantum processor handles a narrow, well-suited sub-problem. Organisations that treat quantum as a research track to watch, rather than a switch to flip, will be best positioned when the technology matures.
What to do today
The most important preparation for a quantum future is, paradoxically, getting the classical fundamentals right: clean data pipelines, strong evaluation, and — above all — people who understand both the mathematics and the engineering. The organisations that will benefit first are those already investing in AI capability building and applied research, because the skills that make teams effective with today’s AI are the same ones that will let them adopt quantum acceleration when it arrives.
Frequently asked questions
Will quantum computing replace classical AI? No. The realistic future is hybrid: classical systems handling most workloads, with quantum processors accelerating specific problems such as optimisation and simulation.
Is quantum machine learning useful today? It is mostly at the research stage. Practical, production-grade quantum advantage for mainstream AI tasks has not yet arrived, but the groundwork being laid now will matter a great deal later.
Explore more on building applied AI capability and applied AI research.
About the Author
Dr Faizan Ur Rehman is a technology consultant, applied AI researcher and technology leader based in Saudi Arabia, working across artificial intelligence, crowd intelligence, computer vision and large-scale digital transformation. An IEEE Senior Member and award-winning innovator — winner of the KAUST Challenge Grand Prize and a Bronze Medal at the Geneva International Exhibition of Inventions — he is widely recognised as one of the leading applied AI researchers and expatriate technology leaders in Saudi Arabia, with 50+ publications and multiple patents in crowd intelligence and applied AI. Explore his work, research & patents, experience and awards.