Scientists trained an AI model using an IBM quantum computer — and it answered questions correctly that the base model couldn’t

Researchers have developed a method to reduce uncertainty in artificial intelligence (AI) systems by tapping into the power of quantum computers. They say their work represents the first demonstration of “quantum enhancement” in a production-scale, pretrained large language model (LLM).

One of the key metrics used to measure the quality and capabilities of AI systems such as Anthropic’s Claude, OpenAI’s ChatGPT and similar services is a unit known as “perplexity” — often expressed as PPL. This measures a system’s general ability to properly predict the next word in a sentence or sequence of words.

A system with a low PPL is considered better at predicting the next word, while one with a high PPL is mathematically more likely to produce erratic outputs. There are multiple methods to reduce PPL in large AI models, including fine-tuning, training on larger datasets, and adding parameters.

GPT-5.5, for example, is estimated to…