How to formulate a machine learning
I am new to the quantum annealing concept and wondering how to formulate a supervised machine learning to a QUBO model:
- input feature vectors with 6 features with real values;
- two classes output
Appreciate any pointer or papers that explain such.
Thx
Comments
Hello,
We have a Jupyter Notebook that you could look at.
It might not be exactly what you are doing, but it will be a good place to start!
Here is a link for your convenience:
https://github.com/dwave-examples/qboost/tree/383ad6efd38192bb5a4493bfbc0338e9d5372f05
There is also this page from our documentation:
https://docs.dwavesys.com/docs/latest/handbook_problems.html#cb-probs-machine-learning
Here is one publication, which might not have enough details, but might be helpful:
https://www.dwavesys.com/resources/application/quantum-machine-learning-for-election-modeling/
This article links to some work done by NASA on Machine Learning:
https://support.dwavesys.com/hc/en-us/articles/360009751474-What-Applications-have-D-Wave-Customers-Developed-
A good way to search our resources is by using the help centre search bar:
https://support.dwavesys.com/hc/en-us
Please let us know if this helps or if you need any further information!
Thanks for the quick reply. Looks like a lot of readings to do :)
Will go thru them and see if things get cleared a bit. Likely more questions later.
Regards
Ray
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