This repository contains Python code to simulate and reconstruct emission tomography images with quantum computing based on our paper: "Exploring Limitations of Hybrid Adiabatic Quantum Computing for Emission Tomography Reconstruction". We have added two examples in the form of a Jupyter notebook for the reconstruction of binary images on the quantum annealer as well as code to reconstruct integer valued images. The tomography_radon.py file contains all functions to simulate the images and reconstruct them using conventional algorithms.
To run the project:
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Create a Virtual Environment and Set Up D-Wave Account:
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Create a virtual environment for your project to manage dependencies.
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Sign up for a D-Wave Ocean account if you haven't already (https://docs.ocean.dwavesys.com/en/stable/overview/install.html). Obtain the required API keys and configure them on your local machine (https://docs.ocean.dwavesys.com/en/stable/overview/sapi.html).
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Install Dependencies:
Install the necessary Python packages by running the following command within your virtual environment:
pip install -r requirements.txt
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Run the examples in the notebooks:
- Use a real quantum annealer to reconstruct your image
- Compare with conventional algorithms