Artificial intelligence has infiltrated our daily workflows and routine tasks for a while now. It could be AI working in the background, like with Geminis integration between Google products, or you might interact more directly with popular content generators like OpenAI’s ChatGPT and Dall-E. Looming in the not-too-distant future is reinforced virtual assistants.

As if AI itself wasn’t futuristic enough, now there’s a whole new leap forward on the horizon: quantum AI. It is a fusion of artificial intelligence with unconventional and still largely experimental quantum computing into a super-fast and highly efficient technology. Quantum computers will be the muscles, while AI will be the brains.

Here’s a quick summary of the basics to help you better understand quantum AI.

What is AI and generative AI?

Artificial intelligence is a technology that mimics human decision making and problem solving. It’s software that can recognize patterns, learn from data, and even “understand” language enough to interact with us, via chatbots, to recommend movies, or to identify faces or things in photos.

A powerful type of AI is generative AIthat goes beyond simple data analysis or predictions. Gen AI models create new content based on their training data – such as text, images and audio. Think ChatGPT, Dall-E, MidjourneyGemini, Claude and Adobe Fireflyto name a few.

These tools are powered by major language models trained on lots of data, so they can produce realistic results. But behind the scenes, even the most advanced AI is still limited by classic computing — the kind that happens in Windows and Mac computers, on the servers that fill data centers, and even in supercomputers. But that’s only as far as binary operations will get you.

And that’s where quantum computing can change the game.

Quantum computing

Classical and quantum computing differ in several ways, one of which is processing. Classical computing uses linear processing (step-by-step calculations), while quantum uses parallel processing (multiple calculations at the same time).

Another difference is in the basic processing units they use. Classical computers use bits as the smallest unit of data (either 0 or 1). Quantum computers use quantum bits, aka qubits, based on the laws of quantum mechanics. Qubits can represent both 0 and 1 at the same time thanks to a phenomenon called superposition.

Another property that quantum computers can exploit is entanglement. It is where two qubits are linked so that the state of one immediately affects the state of the other, regardless of distance.

Superposition and entanglement allow quantum computers to solve complex problems much faster than traditional computers. Where classical computing can take weeks or even years to solve some problems, quantum computing reduces the time frame for achievement to mere hours. So why aren’t they mainstream?

Quantum computers are incredibly sensitive and must be kept at incredibly low temperatures to function properly. They are massive and not yet practical for everyday use. Still like companies Intel, Google, IBM, Amazon and Microsoft are heavily invested in quantum computing, and the race is on to make it profitable. While most companies do not have the money or specialized teams to support their own quantum computers, cloud-based quantum computing services such as Amazon brake and Google’s Quantum AI may be an alternative.

Although the potential is huge, quantum AI faces challenges such as hardware instability and a need for specialized algorithms. However, error correction improvements and qubit stability makes it more reliable.

Current quantum computers, for example IBM’s Quantum System Two and Google’s quantum machinerycan handle some calculations but is not yet ready to run large-scale AI models. Additionally, quantum computing requires highly controlled environments, so scaling up for widespread use will be a major challenge.

That’s why most experts believe we’re likely years away from fully realized quantum AI. As Lawrence Gasman, president of LDG Tech Advisors, wrote for Forbes in early 2024: “It’s early days for quantum AI, and for many organizations, quantum AI right now may be overkill.”

The what-if game

Quantum AI is still in the early testing stages, but it is a promising technology. Right now, AI models are limited by the power of classical computers, especially when processing large data sets or running complex simulations. Quantum computing can provide the necessary boost AI needs to process large, complex data sets at ultra-fast speeds.

Although future real-world applications are somewhat speculative, we can hypothesize that certain areas would benefit the most from this technological breakthrough, including financial tradingnatural language processing, image and speech recognition, healthcare diagnostics, roboticsdrug discovery, supply chain logistics, cyber security through quantum-resistant cryptography and traffic management for autonomous vehicles.

Here are some other ways quantum computing can improve AI:

• Training large AI models, such as LLMs, takes enormous amounts of time and computing power. That’s one reason AI companies need huge data centers to support their tools. Quantum computing could speed up this process, allowing models to learn faster and more efficiently. Instead of taking weeks or months to train, quantum AI models can be trained in days.
• AI thrives on pattern recognition, whether it’s in images, text or numbers. The power of quantum computing to process many possibilities simultaneously can lead to faster and more accurate pattern recognition. This would be particularly beneficial in areas where AI needs to consider many factors simultaneously, such as financial forecasting for trading.
• While impressive, generative AI tools still have limitations, especially when it comes to creating realistic, nuanced output. Quantum AI could enable generative AI models to process more data and create content that is even more realistic and sophisticated.
• In decision-making processes where multiple factors must be balanced, such as drug discovery or climate modeling, quantum computers can allow AI to test countless possible scenarios and outcomes simultaneously. This can help researchers find optimal solutions in a fraction of the time it takes them now.