Artificial intelligence has infiltrated our daily workflows and routine tasks for a long time. It can be AI running in the background, like with Gemini’s integration into Google products, or you can engage more directly with popular content generators like ChatGPT and OpenAI’s Dall-E. In the not too distant future will come augmented virtual assistants.
As if AI itself wasn’t futuristic enough, now there’s a new leap forward on the horizon: quantum AI. It is a fusion of artificial intelligence with unconventional and still largely experimental quantum computing in a super-fast and highly efficient technology. Quantum computers will be the muscles, while AI will be the brains.
Here is a quick overview 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, recommend movies, or identify faces or things in photos.
A powerful type of AI is generative AI, which goes beyond simple data analysis or predictions. AI generation models create new content based on their training data – such as text, images and sounds. Think ChatGPT, Dall-E, Midjourney, Gemini, Claude and Adobe Firefly, to name a few.
These tools are powered by large language models trained on tons of data, allowing them to produce realistic results. But behind the scenes, even the most advanced artificial intelligence is still limited by classical computing — the kind that happens on Windows and Mac computers, on the servers that populate data centers, and even on supercomputers. But there is only so far that binary operations will take you.
And this is 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 computing uses parallel processing (multiple calculations at once).
Another difference is in the core processing units they use. Classical computers use bits as the smallest unit of data (either 0 or 1). Quantum computers use quantum bits, known as qubits, based on the laws of quantum mechanics. Qubits can represent both 0 and 1 thanks to a phenomenon called superposition.
Another property that quantum computers can use is entanglement. It is where two qubits are connected 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 just hours. So why aren’t they mainstream?
Quantum computers are incredibly delicate and must be kept at extremely low temperatures to work properly. They are massive and still not practical for everyday use. However, companies such as Intel, Google, IBM, Amazon and Microsoft have invested heavily in quantum computing, and the race is on to make it viable. While most companies do not have the funds or specialized teams to support their quantum computers, cloud-based quantum computing services such as Amazon Brakes and Google’s Quantum AI may be options.
While the potential is great, quantum AI faces challenges such as hardware instability and the need for specialized algorithms. However, improvements in error correction and qubit stability are making it more reliable.
Current quantum computers, such as IBM’s Quantum Two system and Google’s quantum machine, can handle some calculations, but are 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.
This is why most experts believe that we are many 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 may now be overkill.”
The what-if game
Quantum AI is still in the early stages of testing, but it is a promising technology. Currently, AI models are limited by the power of classical computers, especially when processing large data sets or when running complex simulations. Quantum computing can provide the necessary AI boost to process large, complex data sets at ultra-fast speeds.
Although future real-world applications are somewhat speculative, we can assume that certain fields would benefit most from this technological breakthrough, including financial trading, natural language processing, image and speech recognition, healthcare diagnostics, robotics, drug discovery, supply chain logistics. , cybersecurity through quantum-resistant cryptography and traffic management for autonomous vehicles.
Here are some other ways quantum computing could improve AI:
- Training large AI models such as LLMs requires massive amounts of time and computing power. It’s one reason why AI companies need large data centers to support their tools. Quantum computing can speed up this process, allowing models to learn more quickly and efficiently. Instead of taking weeks or months to train, quantum AI models can be trained in days.
- Artificial intelligence 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 useful in areas where AI needs to consider many factors at once, such as financial forecasting for trading.
- While impressive, AI generation tools still have limitations, especially when it comes to creating realistic and nuanced results. Quantum AI can 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 could help scientists find optimal solutions in a fraction of the time it takes now.