- INTRODUCTION
Quantum computing is often seen as the future, especially for solving complex problems in science like drug development or creating new materials. It works well in modeling quantum systems, which are hard for regular computers to simulate. But now, some reports, including one from the MIT Technology Review, suggest that Artificial Intelligence (AI) running on classical computers might already be doing a better job than quantum computers in some areas—at least for now.
Experts believe that AI and quantum computing are not competing technologies but can actually work together. Quantum AI, the fusion of these two powerful fields, holds the potential to tackle problems that were previously unsolvable. AI is already very powerful and solves many problems quickly, but it struggles with extremely complex ones. That’s where quantum computing could help. For example, companies like EY are using quantum tools to improve things like financial planning and chemical pollution control.
Chris Ballance from Oxford Ionics explains that AI and quantum computing are made for different tasks. While AI is great for speeding up work and solving unclear problems, quantum computing can deal with issues too complicated for any regular computer.
Rahul Tyagi from SECQAI says combining classical and quantum systems is smart. His team is using powerful new chips from NVIDIA to build hybrid systems that can simulate large quantum systems better than classical or quantum computers alone.
Yuval Boger of QuEra Computing agrees. He says that while AI is more practical right now, quantum computing could one day solve problems that AI simply can’t handle—like modeling superconductors. He believes that the best path forward is to use AI for simpler tasks and quantum computing for very complex ones.
Stefan Leichenauer from SandboxAQ thinks quantum computing will only be used for the hardest problems in the next few years. AI and classical computers will still handle most tasks, especially in areas like drug discovery and materials science.
Eleanor ‘Nell’ Watson from Singularity University adds that AI is already solving tough problems, such as protein folding, with lower costs and fewer challenges than quantum computing. She notes that many benefits of quantum machine learning are still only theoretical.
Some, like Gev Balyan from Ucraft, believe AI is the best option for now. But he also agrees that quantum computing could become useful in the future for complex systems that AI struggles with.
Other experts, like Cache Merrill and Thomas Balogun, say classical AI is more reliable and ready to use today. It handles tasks in physics and chemistry well and doesn’t suffer from the technical issues that quantum computing still faces.
In the end, most experts agree that the future will not be about choosing between AI and quantum computing. Instead, it will be about using both together—each doing what it does best—to solve the most difficult problems in science and technology.
