Since the first error-corrected quantum computer chips began to emerge in 2025, the era of quantum computing has advanced in leaps and bounds. From interesting but esoteric experiments in the lab, quantum became the future of supercomputers.

It stands to reason. While classical computer bits can only be in one of two states – either a zero or a one – quantum bits, or qubits, can be in either state simultaneously, or any value in between. So it’s more like a probability than a discrete value. Which means you can do blazingly fast matrix calculations and simultaneous processing, rather than sequential computation.

But, because they work in probabilities rather than exact numbers, real-world applications for quantum computing have been limited to things like optimizing routes and resources, or forecasting based on multiple connected parameters, where speed is more important than accuracy.

Which is where AI comes in. To answer a query, AI models perform inference computing, essentially predicting the most likely set of tokens from billions of active parameters, to provide the correct answer. And if you’re doing facial recognition, or fraud detection, or just writing witty articles, speed is often more important than being absolutely 100% correct.

AI models and agents are more powerful and busier than ever, requiring enormous computing and energy resources. Quantum computers can slash those requirements by many orders of magnitude – billions of dollars – and still provide reliable results, within certain confidence limits. It’s a match made in computing heaven.

And AI is the Quantum killer app.

Get in touch

For more information on the potential for quantum computing to completely disrupt the future of machine learning and artificial intelligence, feel free to reach out to Jesse Declercq or Doug Vining.