On Tuesday, February 11, I participated in a panel discussion on Quantum and AI at the LEAP/DeepFest conference in Riyadh, Saudi Arabia. Below, I’ve expanded on my thoughts from the event.

Quantum computing and AI are two of the most discussed topics in the deep tech space. Whether from researchers at major corporations, academics-turned-entrepreneurs, investors, CEOs, analysts, or even social media influencers, these fields have captured the attention of many.

While we can discuss them separately, what happens when we combine their capabilities or at least use them in tandem? Let’s explore three ways they might work together.

Quantum for AI

If both AI and quantum computing are impressive on their own, why not combine them to make AI even better?

Quantum computing offers a different approach compared to classical computing. Traditional computing, which dates back to the 1940s, uses bits (0s and 1s) to process information on devices like phones, laptops, and servers. Quantum computers, however, rely on qubits, which are much more powerful. Each qubit can hold two pieces of data, and every time you add a qubit, the amount of information it can process doubles. For example, with two qubits, you can process 4 pieces of data, with three qubits, you can process 8, and so on. This exponential growth is exciting.

This has led some to claim that quantum computers will soon be able to process far more data for AI applications than classical systems can. However, there’s a catch—how do you get all that data into the qubits? Several methods exist, but they’re still quite slow. So, be cautious when you hear about “Quantum for AI” innovations, especially if they’re described as “small scale” or “prototype.” The reality is, most of the progress so far addresses smaller problems, and quantum computers aren’t yet powerful enough for commercial use.

Without error correction, qubits are extremely fragile. They only work for a very short time before they lose their data. If loading AI data takes too long, there may not be enough time left to actually process it. This makes it clear that classical AI is likely to remain more practical for many use cases in the near term.

That said, machine learning involves finding patterns in data, and quantum computing’s model could potentially help us discover new patterns or speed up the process. At this stage, it’s more about potential than actual performance. Vendors will often demonstrate “Quantum for AI” on their hardware, but I’d prefer to see a demonstration involving quantum chemistry, as that’s likely to be the first successful real-world use case for quantum computing.

AI for Quantum

I find this aspect far more intriguing at the moment. Can we use AI to help improve quantum computers?

Quantum computers rely on qubits as their basic units of information. But how many qubits do we actually need? Some experts claim that dozens to a few thousand qubits will be enough, but I believe we’ll need around 100,000 physical qubits for quantum computers to be fully functional. These qubits can be either manufactured (like superconducting or silicon spin qubits) or natural (such as trapped ions, neutral atoms, or photons).

The reason we’re so interested in quantum computing is that nature itself works using quantum mechanics. Atoms, electrons, photons, and molecules all operate within this framework, so it makes sense to emulate nature’s methods to solve tough problems. However, there’s a big challenge: while nature’s quantum systems work, they don’t always cooperate with our calculations. Noise from the environment interferes with qubits, causing errors in their operations. Think of it like a calculator that miscalculates due to electrical static, or a phone call with audio interference.

Researchers have started using machine learning to detect patterns in this noise to improve quantum systems. This is a promising area of progress, and AI could help us make quantum computing more stable and effective. We might very well use AI to improve quantum computers, which could then help us realize the full potential of Quantum for AI in the future.

Quantum and AI in the Same Workflow

In some cases, scientists in quantum computing and AI may not always be aware of how industrial processes work. While it’s essential to focus on hardware and algorithms, it’s also crucial to understand the broader context in which these technologies will be used. Instead of trying to merge quantum and AI, consider them working in separate but complementary processes.

For example, Microsoft demonstrated a workflow in 2024 that integrated high-performance computing (HPC), AI, and quantum computing for a chemistry problem. Other companies, such as IonQ and Quantinuum, have also shown similar workflows. The idea isn’t new—IBM had already discussed in 2020 that the future of computation would involve classical bits, quantum qubits, and AI neurons working together.

What Are the Timeframes?

AI for Quantum has been valuable for years and will continue to be useful as we develop quantum computing systems. Understanding how HPC, quantum, and AI can work together is becoming clearer, and we should see practical results by the end of this decade.

However, Quantum for AI is still in the early stages. Currently, it’s mainly about demonstrating that quantum systems can perform better than classical AI on small problems. I believe we’ll need large-scale, error-corrected quantum computers before this becomes a significant reality, and this will likely take another 7 to 10 years, extending into the 2030s.

As we continue to evolve quantum computing, AI will also progress. The approaches we use today might not dominate either field a decade from now, and the integration of both will need to adapt to these ongoing changes.