Esperanza Cuenca-Gomez

Two solutions using NVIDIA technology and tools are great! We encourage using CUDA-Q and we are happy to see other NVIDIA tools too. Also, please note that for this challenge CUDA-Q is our recommendation. Hope this helps!

You mean about the cudaq.vqe Phyton API? Where do you see ambiguity and how could that ambiguity be solved? Maybe this VQE example can be helpful: https://nvidia.github.io/cuda-quantum/latest/applications/python/vqe.html, as well as the VQE documentation in our recently released CUDA-QX libraries: https://nvidia.github.io/cudaqx/components/solvers/introduction.html#variational-quantum-eigensolver-vqe Please feel free to reach out to me through a discussion thread in this platform. Thanks a lot for your questions!

Thanks for the clarification! Just start a discussion on the discussion channel in this platform, tag me and I'll be happy to help :-)

Can you share more details of the challenges you are facing? I am happy to review those with our engineers if needed, though a meeting with them can be hard to schedule. However, if you share more details, maybe we can discuss those through an off-line channel. I am also happy to take that meeting myself if that's helpful. Thanks!

Thanks a lot for your question, Kees. I am not sure I understand your question. You mean in the context of this event or in any other context? Thanks!

It's something that we haven't really think about but it is an interesting idea for sure. Thanks a lot for sharing that!

Thanks a lot for the follow up question, Kirill. We are not providing cloud computing resources for this challenge, but I encourage you to explore cloud access by other means (Google Colab for example)

That's a great question and one to which I really don't have an answer. I personally believe that those scoring systems and metrics are hard to define at this stage beyond the clasical metrics for qubit and gate fidelity, coherence, and so on. Anything beyond that it's very hard for me to answer at this point.

Sure, happy to Kirill! Thanks for your question. We are not providing data for the challenge and we don't have any indications or preferences for open-source providers, so we are happy to leave that to your best judgement - which I am sure it will be good.

Thanks a lot for the question. I believe that all of those are great initiatives to drive the quantum computing ecosystem forward. At NVIDIA, we are following an open-source strategy with CUDA-Q, our open-source platform for accelerated quantum supercomputing. CUDA-Q is an open-source quantum development platform orchestrating the hardware and software needed to run useful, large-scale quantum computing applications. The platform’s hybrid programming model allows computation on GPU, CPU, and QPU resources in tandem from within a single quantum program. You can learn more here: https://developer.nvidia.com/cuda-q and here: https://nvidia.github.io/cuda-quantum/latest/index.html