QuSparse: Quantum Approaches to Sparse Signal Reconstruction
Introduction
Sparse signals, although appearing simple, hide complex challenges in their reconstruction, especially when data is limited or noisy. Classical methods of sparse signal reconstruction, while effective, have inherent limitations in quickly and accurately reconstructing sparse signals. This project aims to increase the efficiency and accuracy of classical sparse signal reconstruction methods by transforming classical sparse signals into quantum states introducting Quantum Machine Learning algorithms such as Quantum Fourier Transform to reconstruct these signals through modern quantum algorithm and quantum circuit development platforms.
We are looking for developers with:
- A strong foundation in physics, computer science, or mathematics.
- Proficiency in machine learning, with a keen interest in diving deep into quantum algorithms.
- An analytical mind, ready to tackle the intricacies of signal processing, both classical and quantum.
If you have any queries about the project, reach out to asadk.khan@mail.utoronto.ca or DM “Asad Khan” on the UTMIST Discord.
