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Variational quantum classifier on heart attack

rodney osodo

rodney osodo

Nairobi, Nairobi County

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This is my quantum open-source foundation project on building a quantum variational classifier using a heart attack dataset. The purpose of this project was to help me gain insight into the actual construction of a quantum model, applied to real data. ...learn more

Project status: Published/In Market

Artificial Intelligence

Groups
Student Developers for AI, Portland Machine Learning Meetup

Intel Technologies
DevCloud, Intel Opt ML/DL Framework, Intel Python

Code Samples [1]Links [3]

Overview / Usage

This is my quantum open-source foundation project on building a quantum variational classifier using a heart attack dataset. The purpose of this project was to help me gain insight into the actual construction of a quantum model, applied to real data. By sharing these insights, I hope to help many of you understand and learn much of the dynamics accompanied with quantum machine learning, which I grasped whilst doing this project. Ultimately, we will:

  • Learn how a variational circuit works
  • Explore the different types of optimizers in Qiskit
  • Get a firmer understanding of quantum machine learning (hopefully! šŸ˜)

Methodology / Approach

My project plan is to:

  1. Explore a specific dataset and pre-process it. For this project, we decided to use the heart attack data as our baseline. This is because, in medical aspects, a heart attack is a leading disease that causes death. In the computation aspect, the data was rather small and could easily be fitted on NISQ computers. We also used the iris dataset and wine datasets for validation.
  2. Create a quantum neural network (AKA variational classifier) by combining a featuremap, variational circuit and measurement component (donā€™t worry, I will explain what these components mean in detail).
  3. Explore different types of optimizers, featuremaps, depths of featuremaps and depths of the variational circuit.
  4. Explain my observations based on the best 10 model configurations.
  5. Try to understand why these models performed the best and see if they are able to generalize well on new data.

Technologies Used

  • ipython==7.16.1
  • jupyterlab==2.2.9
  • Keras-Preprocessing==1.1.2
  • matplotlib==3.3.3
  • networkx==2.5
  • notebook==6.1.5
  • numba==0.52.0
  • numpy==1.18.5
  • pandas==1.1.4
  • pandas-profiling==2.9.0
  • qiskit==0.23.1
  • qiskit-aer==0.7.1
  • qiskit-aqua==0.8.1
  • qiskit-ibmq-provider==0.11.1
  • qiskit-ignis==0.5.1
  • qiskit-terra==0.16.1
  • scikit-learn==0.23.2
  • scipy==1.4.1
  • seaborn==0.11.0
  • sklearn==0.0
  • tensorboard==2.4.0
  • tensorflow==2.3.0

Repository

https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack

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