What's More Advanced Than AI? Quantum AI Is the Next Leap Forward

Yahoo

07-05-2025

Our daily workflows and routine tasks have been infiltrated by artificial intelligence, whether or not you've noticed it. Things like Gemini's integration across Google products work in the background, giving you suggestions. You might even be engaging more directly with chatbots and image generators like OpenAI's ChatGPT and Dall-E. And looming in the near future are more sophisticated virtual assistants.
As if AI itself weren't futuristic enough, there's a new leap forward on the horizon: quantum AI. It's a fusion of artificial intelligence with unconventional and still largely experimental quantum computing into a super-fast, highly efficient technology. Quantum computers will be the muscles, while AI will be the brains.
"My colleagues sometimes ask me why I left the burgeoning field of AI to focus on quantum computing," Hartmut Neven, founder of Google's Quantum AI lab, wrote in a December blog post introducing the Willow quantum chip. "My answer is that both will prove to be the most transformational technologies of our time, but advanced AI will significantly benefit from access to quantum computing."
Here's a quick breakdown of the basics to help you better understand quantum AI.
AI vs. 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.
One powerful type of AI is generative AI, which goes beyond simple data analysis or predictions. Gen AI models create new content like text, images and sounds based on their training data. Think ChatGPT, Dall-E, Midjourney, Gemini, Claude and Adobe Firefly, to name a few.
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These tools are powered by large language models trained on tons of data, allowing them to produce realistic outputs. But behind the scenes, even the most advanced AI is still limited by classical computing, the kind that happens in Windows and Mac computers, in the servers that populate data centers and even in supercomputers. But there's only so far that binary operations will get you.
And that's where quantum computing could change the game.
What is 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 (simultaneous calculations).
Another difference is in the basic processing units they use. Classical computers use bits as the smallest data unit (either a 0 or a 1). Quantum computers use quantum bits, aka qubits, based on the laws of quantum mechanics. Qubits can represent both 0 and 1 simultaneously thanks to a phenomenon called superposition.
Another property that quantum computers can leverage is entanglement. It's where two qubits are linked so that the state of one immediately influences the state of the other, no matter the 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 timeframe for achievement to merely hours. So why aren't they mainstream?
Quantum computers, running on purpose-built quantum chips, are incredibly delicate and must be kept at amazingly low temperatures to work properly. They're massive and not yet practical for everyday use. Still, companies like Intel, Google, IBM, and Microsoft are heavily invested in quantum computing, and the race is on to make it viable. While most companies don't have the funds or specialized teams to support their own quantum computers, cloud-based quantum computing services like and Google's Quantum AI could be options.
Is quantum AI realistic?
While the potential is enormous, the main criticism of quantum AI right now is that there's a lot of hype but not a lot of realistic applications. Quantum AI faces challenges like hardware instability and a need for specialized algorithms. However, improvements in error correction and qubit stability are making it more reliable.
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Current quantum computers, like IBM's Quantum System Two and Google's quantum machinery, can handle some calculations but aren't yet ready to run large-scale AI models. Additionally, quantum computing requires highly controlled environments, so scaling up for widespread use will be a big 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 at the start of 2024: "It is early days for quantum AI, and for many organizations, quantum AI right now might be overkill."
Quantum AI in the future
Quantum AI is still in the early trial stages but is a promising technology. Right now, AI models are limited by the power of classical computers, especially when processing big datasets or running complex simulations. Quantum computing could provide the necessary boost AI needs to process large, complex datasets at ultrafast speeds.
Although the future real-world applications are somewhat speculative, we can assume certain fields would benefit the most from this technological breakthrough, including financial trading, natural language processing, image and speech recognition, health care diagnostics, robotics, drug discovery, supply chain logistics, cybersecurity through quantum-resistant cryptography and traffic management for autonomous vehicles.
Here are some other ways that quantum computing could enhance AI: