Maybe just starting with the Advantage system, we just put out our performance update of the Advantage system, which we launched about a year ago. It’s really important to us at D-Wave that we’d constantly be iterating and taking customer feedback and making our products more useful. Even within a certain product generation like the Advantage product, it’s really important that we continue to improve that and drill down to the best possible version of that product.
One year later, here we are with our performance update, and it came with a lot of important improvements to its ability to contribute to problems, even when you’re using the quantum computer directly. Most notably, it has better noise characteristics. It solves these low-level sampling problems better, and it also has more qubits and more connectivity in the sense that it has a better working graph, which allows us to access bigger and more complex problems than we were able to solve a year ago. Both of those have had an impact on our ability to use this quantum computer both directly and in the context of our hybrid algorithms.
Actually, I’m really proud of myself for that segue, because that brings me into our new hybrid solver, which is our new constrained quadratic model solver that’s available in Leap right now. As I mentioned before — but it’s worth emphasizing, even though it sounds like you’ve talked about this before — our hybrid solvers are solvers that combine both classical and quantum resources. One of the benefits of doing that is that it allows you to solve more abstract problem classes than the quantum computer can solve directly.
Our quantum computer solves a problem called an Ising problem, also known as a QUBO, also known as a binary quadratic model. That problem class is very flexible and very powerful, but frankly, most industrial-optimization engineers aren’t thinking about QUBOs. They’re thinking about constrained problems — problems like, “I want to route my trucks, but my trucks have a total mileage of 100 kilometers before they run out of gas” or “I need to do maintenance at least every three days.” These are hard requirements on the problem class that if you’re outside of that regime — if you return a route that a truck goes 101 kilometers — it’s not valid. It’s not useful to anybody.
These sorts of hard constraints show up all the time in practical problems, and our new constrained quadratic model solver allows users to specify these hard constraints natively, which is to say that they can say them — well, at least, by computer science standards — in plain English, as opposed to before, where you had to do some convolutions in order to express your constraints in the formalism of the Ising problem.
This has had an amazing, immediate impact on our customer engagements. I can tell you that all of our customers have been excited about this, and many of them are already making use of it, and it’s been a huge benefit both in terms of their ability to express the problems they want to solve and then, also, subsequently, in the performance that they’re getting on those problems. For instance, that multivehicle routing problem, you can actually get better performance on that problem than we were getting six months or nine months ago, by using this constrained quadratic model solver.
The other cool new thing that’s coming in Ocean, we have released a road map for our new gate-model system and our updates to the Advantage product going forward, and that’s been really useful for us to be able to now engage with customers and show them this is where we’re heading, and you can start positioning the different approaches we’re taking to take that into account. That’s been a really useful thing for us to finally be able to announce to the world some of the cool stuff that we’re working on.