Artificial intelligence has been infiltrating our daily workflows and routine tasks for some time now. It could be AI working in the background, as in Twin‘s integration into Google products, or you can interact more directly with popular content generators like OpenAI’s ChatGPT and Dall-E. In the not too distant future, developments will be strengthened virtual assistants.
As if AI itself wasn’t futuristic enough, there’s now 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 overview of the basics to help you better understand quantum AI.
What are AI and generative AI?
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Artificial intelligence is a technology that mimics human decision-making and problem solving. It is software that can recognize patterns, learn from data and even ‘understand’ language enough to communicate with us, via chatbots, to recommend movies or to identify faces or things in photos.
One powerful type of AI is generative AIwhich goes beyond simple data analysis or predictions. Gen AI models create new content based on their training data, such as text, images and sounds. Think ChatGPT, Dall-E, Halfway through the journeyTwin, Claude And Adobe Fireflyjust to name a few.
These tools are powered by major language models trained on tons of data, allowing them to produce realistic results. But behind the scenes, even the most advanced AI is still limited by classical computing, such as that found on Windows and Mac computers, on the servers that populate data centers, and even on supercomputers. But there’s only so far that binary operations can get to you.
And that’s where quantum computing could change the game.
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Quantum computers
Classical and quantum computers differ in several ways, including processing. Classical computing uses linear processing (incremental 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 data unit (a 0 or 1). Quantum computers use quantum bits, also called 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 take advantage of is entanglement. Here, two qubits are connected together so that the state of one immediately affects the state of the other, regardless of distance.
Thanks to superposition and entanglement, quantum computers can solve complex problems much faster than traditional computers. While classical computing can take weeks or even years to solve some problems, quantum computing reduces the time frame to achieve this to just a few hours. So why aren’t they mainstream??
Quantum computers are incredibly delicate and must be kept at astonishingly low temperatures to work properly. They are huge and not yet practical for everyday use. Still, companies like it Intel, Googling, IBM, Amazon And Microsoft There is heavy investment in quantum computing, and the race is on to make it viable. While most companies don’t have the money or specialized teams to support their own quantum computing, there are cloud-based quantum computing services such as Amazon Braket and Google’s Quantum AI could be options.
Although its potential is enormous, quantum AI faces challenges such as hardware instability and the need for specialized algorithms. However, improvements in error correction and qubit stability make it more reliable.
Current quantum computers, such as IBM’s Quantum System Two And Google’s quantum machinecan handle some calculations, but are not yet ready to run large-scale AI models. Furthermore, quantum computing requires highly controlled environments, so scaling up for widespread use will be a major challenge.
That’s why most experts think we’re probably years away from fully realized quantum AI. As Lawrence Gasman, president of LDG Tech Advisors, wrote Forbes at the start of 2024: “It’s early days for quantum AI, and for many organizations quantum AI might be overkill at this point.”
The what-if game
Quantum AI is still in the early stages of a trial, but it is a promising technology. Currently, AI models are limited by the power of classical computers, especially when processing large data sets or running complex simulations. Quantum computing could provide the necessary boost to AI needs to process large, complex data sets at ultra-fast speeds.
While future real-world applications are somewhat speculative, we can assume that certain areas will benefit most from this technological breakthrough, including financial tradingnatural language processing, image and speech recognition, healthcare diagnostics, roboticsdrug discovery, supply chain logistics, cybersecurity by quantum-resistant cryptography and traffic management for autonomous vehicles.
Here are some other ways quantum computers can improve AI:
- Training large AI models, such as LLMs, takes enormous amounts of time and computing power. It’s one of the reasons why AI companies need massive 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 images, text or numbers. The power of quantum computing to process many possibilities at once could lead to faster, more accurate pattern recognition. This would be especially beneficial in areas where AI needs to take many factors into account at once, such as financial forecasting for trading.
- While impressive, generative AI tools still have limitations, especially when it comes to creating realistic, nuanced results. Quantum AI could enable generative AI models to process more data and create content that is even more realistic and advanced.
- In decision-making processes that require balancing multiple factors, such as drug discovery or climate modeling, quantum computers can enable AI to test numerous possible scenarios and outcomes simultaneously. This could help scientists find optimal solutions in a fraction of the time they currently need.