• Suvranu De, Rensselaer Polytechnic Institute, USA
• Vikram Gavini, University of Michigan, Ann Arbor, USA
• Veera Sundararaghavan, University of Michigan - Ann Arbor, USA
• Eiji Tsuchida, National Institute of Advanced Industrial Science and Technology (AIST), Japan
• Amartya Banerjee, University of California, Los Angeles, USA

The theme of this mini-symposium is related to computational algorithms at the intersection of quantum mechanics, quantum computing, and finite elements (and other discretization techniques). While powerful computational techniques are being developed to accelerate electronic structure calculations, there is significant interest in quantum computing to solve problems in computational science and engineering. In the mix are machine learning methods that are being developed for both kinds of problems.

Topics of interest include, but are not limited to:
- Recent developments in electronic structure calculations
- Algorithms for large-scale electronic structure calculations
- Symmetry adapted electronic structure calculations
- Machine learning ideas applied to electronic structure calculations
- Quantum computing for the solution of partial differential equations
- Quantum computing with unstructured meshes and meshless discretization schemes
- Spectral finite elements and quantum Fourier transforms
-Quantum algorithms for inverse problems
- Sparsity preserving preconditioning techniques
- Acceleration algorithms for noisy intermediate-scale quantum (NISQ) devices