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XanaduAI / QHack2021

Official repo for QHack—the quantum machine learning hackathon
https://qhack.ai
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[Power Up] QNN-for-Thermodynamic-correlation #30

Closed alejomonbar closed 3 years ago

alejomonbar commented 3 years ago

Team Name:

Coherence

Project Description:

This work proposes a Quantum neural network-based methodology to estimate frictional pressure drop during boiling in mini-channels of non-azeotropic mixtures including nitrogen, methane, ethane, and propane. The methodology can assist in thermal analysis or design of heat exchangers used in cryogenic applications. The architecture of the proposed model includes the local quality, roughness, mass flux, and Reynolds number as inputs and frictional pressure drop as outputs. It will compare with one paper where my colleagues and I use the same data to create an ANN-based correlation for pressure drop estimation in microchannels [1].

[1] Barroso-Maldonado, J. M., Montañez-Barrera, J. A., Belman-Flores, J. M., and Aceves, S. M. (2019). ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling. Applied Thermal Engineering, 149(August 2018), 492-501. https://doi.org/10.1016/j.applthermaleng.2018.12.082

Source code:

https://github.com/alejomonbar/QNN-for-Thermodynamic-correlation

Resource Estimate:

My project consists of two stages, the first is to explore different circuit configurations to encode the inputs and the number of parameters used to determine the pressure drop correlation. Then, I need to run at least 20 different configurations which should be 2 hours of SV1 ($9). Second, I would like to see how noise affects the model then I need to use one of the quantum devices available in bracket. Training is an impossible task because I have almost 5000 sample data. Therefore, I'm going to use the test set that is composed of 693 samples to compare the solution using one of the quantum devices and the ideal pressure drop once the parameters are optimized. This means (693*100shot*0.01$/shot) + 693*0.3Task = 0.01*693*1000 + 0.3*693 = $900.9.

My first training using a gives me better results (error 8%) than those we obtained in the paper presented above (error 9-9.5%). This gives me the intuition that with the correct layer configuration we can outperform the ANN results.

glassnotes commented 3 years ago

Hi @alejomonbar thanks for your submission! Could you please up the issue with your project title?

alejomonbar commented 3 years ago

Hi @alejomonbar thanks for your submission! Could you please up the issue with your project title?

Thank you!

co9olguy commented 3 years ago

Thanks for your Power Up Submission @alejomonbar !

To help us keep track of final submissions, we will be closing all of the [Power Up] issues. We ask you to open a new issue for your final submission. Please use this pre-formatted [Entry] Issue template. Note that for the final submission, the Resource Estimate requirement is replaced by a Presentation item.