Implementation of adaptative optimizers for variational quantum algorithms (Qhack 2023 submission) We reimplement an optimizer that has been designed for quantum machine learning applications from the paper Resource frugal optimizer for quantum machine learning . The optimizer is mainly defined in refoqus.py.
We provide 3 application examples as notebooks:
- Variational Quantum State Eigensolver vqse_example.ipynb
- Quantum Autoencoder quantoencoder_example.ipynb
- Variational Quantum Error Correction vQEC.ipynb
A last notebook (vqse_example-lightning-gpu-vs-cpu.ipynb) illustrates how using a NVidia GPU accelerator to simulate the quantum circuits improves the runtime of our VQSE application by nearly 40%.
Slides in a PDF format explaining our submission can be found in the presentation_Qhack_2023.pdf file.
You can install the required dependencies with
python -m pip install -r requirements.txtIf you have any issue installing cuQuantum, please follow https://docs.nvidia.com/cuda/cuquantum/getting_started.html#install-cuquantum-python-from-conda-forge.
The my_favourite_team team is composed of 2 members: