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> how is that project coming along?

It's actively in production now with our new Advantage chip with 5000+ qubits (exact yields vary as fabrication is truly a difficult thing to get perfect, but each chip that's been made available to customers is at least 5000 to my knowledge).

> I hope things are running smoothly over there, it was for sure one of the more promising approaches I saw.

We have a decent number of investors with NEC being our latest, and contributions from the Canadian government's various innovation funds are also helping. It's an expensive business to run, and we've been in it for longer than anyone, so it makes it clear that this is a long term play. I personally like to liken it to investing in a Babbage or Turing sort of project: it's clear the seeds of some amazing future technology are here, but the current scale of the machine is not yet at the level where it can unlock all the things we're dreaming about yet.

That's where the hybrid solvers come in: we can basically present a way larger capability in terms of our working graph for Ising problems than fits on the QPU, and chop it up and get great quality answers that really make use of the underlying quantum hardware while keeping up with the best classical software.

> I suspect the killer application could actually end up being neural networks.

Yes, that's long been one of our expected markets, and something we hope to see increase over the next few years. One issue is that a lot of ML developers approach things from the far other side of the stack - like most hackers, picking up a new toolkit and exploring it from the outside in, rather than academically learning every aspect of the model and how it works inside. Unfortunately, it's that latter skillset that is still required to do the work of mapping an ML model onto the Ising model - way above my own personal ability to even approach.

> You have some robustness to noise and actually you really don't care so much that the answer is not even the final answer, just that some progress towards the final answer is made.

This is exactly the strength of quantum annealing: getting lower energy state solutions in a shorter time than a classical method by using quantum-mechanical properties of the universe, like tunnelling.

> I took a very quick crash course in quantum computing some 5 years ago (maybe more) and worked on some toy problems. I should check it out again. I guess you're now at the stage where you have some re-programmable setup? Or is this a simulation QC machine?

No simulation at all! You fire up the IDE from your account at https://cloud.dwavesys.com/leap/ and you will have an API token that works with our Ocean Tools SDK to submit problems to the live QPU. We also have Jupyter Notebooks in our online training session that will submit problems live. No simulation, and it's totally "reprogrammable" in the sense that you can submit whatever QUBO / Ising problem you want, within limits, and it will be sampled and a solution will be returned to you within seconds.

> I would love to hear from you about what some people have been able to do in terms of applications.

Our big win recently was Volkswagen using the system to route busses in Lisbon live with the QPU as a back end. https://www.dwavesys.com/media-coverage/volkswagen-optimizes...

We also just got a paper in Nature with simulation of magnets: https://www.nature.com/articles/s41467-021-20901-5.epdf and have done other work with spinglasses in the past.

Current customer efforts revolve around anything from physics simulations, to logistics work, and anything else where optimization functions come into play. There's actually a really wide range of optimization work people are already doing that can be tied into our system; for now, it's a matter of creating the right customer middleware to bridge the gap.

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