[Done] Adaptative VQA optimizer

[nelimee]

Project Name: Adaptative VQA optimizer

Team Name: my_favourite_team

Which challenges would you like to submit your project for?

• Quantum Chemistry Challenge (VQSE and quantum autoencoder examples are using chemistry data from Pennylane).
• Hybrid Quantum-Classical Computing Challenge (all examples are VQAs and the Refoqus optimizer is targeted at VQAs)
• Quantum computing today! (the Refoqus paper has been submitted on arXiv the 9 Nov 2022).
• QEC and Compilation Challenge (the variational quantum error correction example is a variational algorithm applied to QEC).
• NVIDIA Challenge (we illustrate in the last example how NVidia GPU can help accelerate research on VQAs by speeding up simulations).

Project Link: https://github.com/chMoussa/adaptative_vqa_optimizers/tree/6a3e76caec4a60f63dfef4e8201c40a54621cf47

Project description: We implement the adaptative optimizer Refoqus and apply it to different problems of interest, among which is a variational algorithm to devise new potential error-correction codes for quantum memory (i.e., able to extend the maximum idle time after which a quantum state cannot be recovered anymore). We also demonstrate the usage of NVidia cuQuantum through the use of the lightning.gpu device and show that it is able to speed-up the VQA optimisation on a consummer-grade laptop when compared to its CPU equivalent lightning.qubit.