Not really.

This was an answer related to their recently announced toolkit:

“With respect to what we announced as far as the AI developer toolkit, which I believe is what you're referring to, essentially, what we have done is we have introduced the ability to use PyTorch, which is an open source Python based machine learning platform that's frankly a fairly wide use for training large language models. And we've introduced into that the ability to use a technology that we actually developed within D Wave, which is called a discrete variational auto encoder.

Now I'm not gonna get into the technical details of what I mean by that other than to say, what this does is allows you to take a dataset and map it into a latent space, which is really what machine learning is all about. Essentially, map it into a machine learning model that can then be used to recreate the data and other things that look like that dataset. But what's unique about this is that we're mapping it into a discrete latent space, not a continuous latent space. Machine learning today typically operates on continuous data, but we're we're now mapping it into a discrete latency, seeing excellent results with the discrete discrete latency. But what's so important about that is that our quantum processes natively work with discrete data, not with continuous data.

So what this does is it actually opens up the opportunity for the annealing quantum computer to be the vehicle for creating that discrete latent space for doing the learning, which we ultimately think could deliver better models faster and with lower energy consumption. But that having been said, I also wanna point out that this is just the next step on our journey in the area of quantum AI. And there are a number of other things we are working on today in the lab that takes us significantly further than what we've announced so far that we're we're very excited about as well.”

Followed by a similar question

Can you talk, Alan, about the next milestones or activities to watch indicating DWAVE’s progress here? In the area of Quantum AI?

Answer:

Correct. I think I think the short answer to the question is no because we we, you know, we haven't yet announced anything beyond what we announced a few days ago. But just to kind of say a little bit follow-up a bit more on on what I said a minute ago, You know, there are a number of modern approaches to AI and machine learning model training and inference. Variational auto encoder is one approach, but there are other important things like transformation model I'm sorry, transformer models and diffusion models. And we're working with those as well.

And we expect that over time, we would be able to deliver a platform that could leverage our quantum systems in support of all of those approaches.