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Hi, All!
I'm running the example of Traveling Sales Person task on Azure Quantum.
I can't get the true result for number of nodes >30 using all available solvers.
It there any limitation for this optimization task?
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Hi @Darya
Can I confirm that you are working with the Traveling Sales Person sample that we have published on GitHub?
https://github.com/microsoft/qio-samples/tree/main/samples/traveling-salesperson
We're looking into this. Thanks for using Azure Quantum.
We have reviewed this sample further as a team and noticed that we had wanted to add a disclaimer regarding the limitations and scalability of the problem formulation used in the sample for some time. This change had never been pushed to the public sample repo and we have now resolved this. We hope that the additional clarity on the sample page helps to address the question raised.
Hi, Daniel @ msft-dasto
Thank you for clarifying. But I supposed that we solve this example on simulated (digital) annealer, and this, to a certain extent, frees us from limitations...
Am I right?
Hi, Daniel @ msft-dasto
Thank you for clarifying. But I supposed that we solve this example on simulated (digital) annealer, and this, to a certain extent, frees us from limitations...
Am I right?
Hey @Darya
Sorry I missed the previous follow up question.
I hope I understand the question correctly. Let me try and answer it and feel free to ask a follow up if not.
It is correct that we do not face the same hardware constraints that we would face on a quantum annealer on a digital annealer.
The limitation here comes from the problem formulation that grows very large as the waypoints in the problem increase.
Other problem formulations on classical hardware may be able to accommodate a growing TSP problem in a more efficient manner.