Your team's name (if your team took part in the QHack Coding Challenge, the name here should match)
ixi
Project Description:
A brief description of your project (1-2 paragraphs).DBNSL's main goal is to obtain P(G|D), that means obtaining a direct acyclic graph (DAG) given a dataset consisiting of at least two events or moments in time. This solution can be applied to solve problems in different fields, like finance, biology and ecology to name a few. This kind of problem is super-exponential (O(n! 2^(n!/(2!(n-2)!)))) [1] due to a search over all DAGs possible. I reviewed different approaches, and I decided to implement three different approaches:
The first step is to implement these approaches with a tiny dummy dataset of 4 variables (8 nodes) and then, implement them with a real datasets of microarray studies on cell cycle regulated genes on braket.
References:
[1] Prashant S. Emani1, et al, Quantum Computing at the Frontiers of Biological Sciences
Which challenges/prizes would you like to submit your project for?
Please manually list all Open Hackathon challenges/prizes you would like to be considered for. In addition, please label this GitHub issue with all of the challenges you would like to be considered for. Additional eligibility conditions may apply for specific prizes. See the Open Hackathon README for further details.
Amazon Braket Challenge
Bio-QML Challenge
Hybrid Algorithms Challenge
Quantum Finance Challenge
Science Challenge
Team Name:
Your team's name (if your team took part in the QHack Coding Challenge, the name here should match) ixi
Project Description:
A brief description of your project (1-2 paragraphs). DBNSL's main goal is to obtain P(G|D), that means obtaining a direct acyclic graph (DAG) given a dataset consisiting of at least two events or moments in time. This solution can be applied to solve problems in different fields, like finance, biology and ecology to name a few. This kind of problem is super-exponential (O(n! 2^(n!/(2!(n-2)!)))) [1] due to a search over all DAGs possible. I reviewed different approaches, and I decided to implement three different approaches:
The first step is to implement these approaches with a tiny dummy dataset of 4 variables (8 nodes) and then, implement them with a real datasets of microarray studies on cell cycle regulated genes on braket. References:
[1] Prashant S. Emani1, et al, Quantum Computing at the Frontiers of Biological Sciences
Presentation:
A hyperlink to an explanatory presentation of your team’s hackathon project in a non-technical form (e.g., video, blog post, jupyter notebook, website, slideshow, etc.). https://github.com/ixime/qhack_2022/blob/main/report_qhack_2022.pdf
Source code:
A hyperlink to the final source code for your team's hackathon project (e.g., a GitHub repo). https://github.com/ixime/qhack_2022
Which challenges/prizes would you like to submit your project for?
Please manually list all Open Hackathon challenges/prizes you would like to be considered for. In addition, please label this GitHub issue with all of the challenges you would like to be considered for. Additional eligibility conditions may apply for specific prizes. See the Open Hackathon README for further details. Amazon Braket Challenge Bio-QML Challenge Hybrid Algorithms Challenge Quantum Finance Challenge Science Challenge