Ab initio full cell dynamical mean-field theory (DMFT) and GW+DMFT for solids
-
Updated
Aug 23, 2026 - Python
Ab initio full cell dynamical mean-field theory (DMFT) and GW+DMFT for solids
Python code designed for projection-based embedding calculations
Solve strongly correlated condensed matter systems. Fast and simple.
Data repository for paper https://arxiv.org/abs/2112.09735
Trainable, scalable quantum embedding kernels with variational parameters
Fragment-based quantum chemistry on NH3 and water clusters (6-31G): embedding + SQD on a simulator, a gateway emulator and IBM hardware, tested against free classical baselines. A worked, reproducible study with negative results.
Quantum Representation Learning with contrastive SWAP test using TensorFlow Quantum and Cirq.
To associate your repository with the quantum-embedding topic, visit your repo's landing page and select "manage topics."