Leap™ Quantum Cloud Service

I was using the hybrid quantum computer from D-Wave Leap for solving big and complex QUBO tasks.

I did not utilize D-Wave Leap's Hybrid Solver Service as I was working in code mostly.

I integrated the Ocean SDK in my workflow, though I do not remember the exact details of what we were using it for.

I would describe the benefits of using D-Wave Leap for optimizing supply chain operations by saying we had no such tasks.

I did not use the machine learning enhancements in D-Wave Leap.

What I appreciate about D-Wave Leap is that it is very easy to use. On Python, it is straightforward and easy to understand how to use, and easy to modify anything.

The key benefits that D-Wave brings to the table really help to structure everything and make for better organization. I was working with it from the start, so I cannot imagine how it would be without it, but without it, it would have been much harder to have an easy and organized scheme of the code and of the project.

The only problem that I encountered with D-Wave Leap is that you must send a whole task in one object. I understand that this was not the first priority for them, and I hope one day they will change this or provide documentation about the maximum object size, because my personal problem was that my QUBO task was about 500 gigabytes, and it is impossible to make 500 gigabytes as one object for computers since it is a QUBO task. However, it should be easy to divide a QUBO task into multiple objects.

Apart from the object and task constraints that I mentioned, I really enjoyed using D-Wave Leap and quantum computers. This was the only issue I could identify because my task was very large.

The course I received was not very informative because it covered basic computing concepts that one can learn in an hour through online resources.

I have been working with D-Wave Leap for about a year and a half.

Currently, I am not working with D-Wave Leap because I am waiting for my project to be approved, but I worked with it previously on a startup.

My experience with D-Wave Leap was about half a year ago.

I had a question for the technical support team at D-Wave Leap, and they answered me in approximately an hour, so it was very fast. I had a technical question about how large of tasks the model can accept, and the response was fast and included information that was not available on the website.

On a scale of 1 to 10, I would rate the technical support of D-Wave Leap 100 out of 100 because it was excellent.

Prior to adopting D-Wave Leap, I have no experience working with any other solutions of the same kind.

The deployment part of the initial setup took approximately 10 minutes to wait until the computing would be completed on the quantum computer and on the hybrid computer. There was always some queue, but it was acceptable. At one point, we discovered that it was better to run the processes at night, and this became easier and faster.

I do not have information about the pricing, setup cost, or licensing aspects of D-Wave Leap, as my company paid for it. The only thing I received was a course from D-Wave, which I believe was for Leap 2, about quantum computing. The course was not very informative, but I now have a certificate.

We purchased everything from D-Wave directly.

Given my experience working with D-Wave Leap, I do not have any specific advice or recommendation that I can share with other organizations considering D-Wave Leap. If they are considering D-Wave Leap, they need it for their project, and for using D-Wave Leap, it is better to use the best libraries and programs to work faster.

I would rate my overall experience with D-Wave Leap at 9.5 out of 10.

I chose to use D-Wave Leap for classifying post-COVID problems because of university support.

I normally want to apply it in machine learning as generative AI, but currently, I'm still not sure which application I can use because I only have twenty minutes per month and need to make sure the dataset is small enough for D-Wave Leap.

What stands out to me about the optimization feature is that it utilizes quantum annealing, which I believe is better than simulated annealing or tabu search. That is why I selected this option, although I remain uncertain about its superiority in the future.

Regarding D-Wave Leap's AI accuracy and reliability, it can track back and recheck using their energy output, so I can trust the results based on the energy, which is satisfactory.

Regarding D-Wave Leap's AI governance and security, I think the security is good, but it is very difficult to select the right key since I keep renewing it and have many keys in the system. I am unsure which one to use.

As far as I know, Florida Atlantic University provides D-Wave Leap computing inside the university, so I am uncertain if this is a public cloud or private cloud.

My advice for others looking into using D-Wave Leap is that if you want to solve optimization problems, selecting D-Wave Leap is a good option. Otherwise, you can use other methods such as AI calculations, machine learning, or mathematical optimization, as D-Wave Leap is still a small computation machine and high-priced. My overall rating for D-Wave Leap is nine out of ten.