We have developed a quantum field theory (FIT) approach and its quantum computing implementation to systems of Drude oscillators interacting with Coulomb potential. This has allowed us to classify all interactions known from the theory of molecular interactions and identify new phenomena, arising from non-trivial coupling between matter and the electromagnetic field. These developments have been published in PRR 5, 043072 (2023) and JCP 160, 094309 (2024) and several additional publications are under preparation.
We also realized that the understanding of excited states of quantum fields and possible transitions between ground and excited states in QFT remains puzzling and needs to be addressed. Hence, we have started to develop a convincing methodology to do this, resulting in two publications: PRD 110, 014503 (2024) and arXiv:2401.17938 (in review). These developments will ultimately allow to study coupled matter and fields in a non-perturbative fashion, which is crucial for a complete understanding of these complex and important systems.
We have developed a novel quantum embedding method that allows us to treat coupled nuclei, electrons, quantum oscillators, and positrons (and any other particles in principle) in a fully quantum mechanical fashion. These developments have been implemented in an in-house quantum Monte Carlo code QMeCha. This code will be made available as an open-source package in 2025. This work has been published in PRL 131, 228001 (2023) and two additional publications are under preparation. In addition, a second quantization approach to the many-body dispersion (MBD) Hamiltonian has been developed and published in Nature Comms 14, 8218 (2023) that allows to define projected many-body states for any composite systems of oscillators and this will form the basis for coupling to quantized fields and for effective machine learning approaches for the MBD Hamiltonian.
To accelerate our computationally intensive simulations, we additionally focused on developing powerful machine-learning approaches that are able to describe long range many-body interactions in a systematic fashion. This has been accomplished by using the newly developed SO3krates neural network architecture and coupling it to unified long-range physical force fields. The resulting method, called SO3LR, is under consideration for publication in PNAS (
https://doi.org/10.26434/chemrxiv-2024-bdfr0(opens in new window)). Importantly, the SO3LR model being trained on millions of organic molecules, is a fully fledged force field that can be applied to modelling large biomolecular systems.