Inspiration
Our inspiration came from the challenge itself -- how can we maximize profits from insurance package offerings with different combinations of coverages? As the number of coverages increases, the number of possible combinations of coverages in packages increases exponentially--with just 20 coverages and 10 packages, there are 2^200 possibilities for profit outcomes! This would take an eternity on classical computers--thus, we are experimenting with quantum optimization algorithms, QAOA and DQI, to see if we can a) speed up the process and b) find higher quality solutions for maximized profit, especially with an increasing number of coverages.
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for A Unique Method to do Optimization
Built With
- guppy
- helios
- jupyter
- pulp
- python
- selene
Log in or sign up for Devpost to join the conversation.