With each passing month, larger, more parameter-dense models appear and the scale of AI deployments expand in tandem. This year alone hyperscalers like Meta, plan tobe hedging its bets on quantum computing on the off chance it can. Last week, the AI juggernaut added Ben Bartlett, a former quantum systems architect at PsiQuantum to its ranks.
And that's today. And if Altman is right these models are only going to get bigger and more prevalent. As such, any technology that could let OpenAI increase the capability of its models without also increasing parameter count meaningfully could give it a leg up. "What our quantum computers are really good at is optimizing things where things are either happening or not happening: like someone being assigned a particular schedule or being assigned a particular delivery," Thom said."If those decisions were independent, that would be fine, and it would be easy for a classical computer to do, but they actually affect the other resources in the pool and there's sort of a network effect.
One of these use cases involves applying quantum computing to sampling. Sampling refers to how AI models, like LLMs, determine what the next word, or more specifically token, should be based on a distribution of probabilities. This is why it's often joked that LLMs are just autocomplete on steroids. However, before this can be achieved, quantum systems are going to have to get a lot bigger and a lot faster, Henriet explained.
Source: Tech Daily Report (techdailyreport.net)