
qtealeaves of the Quantum TEA library with version v1.5.8 containing tensor network methods to simulate many-body quantum systems. The work is based on the following references, please consider citing them in addition to the software package depending on your use case: 1) Pietro Silvi et al., "The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems", SciPost Physics Lecture Notes, 008 (2019) 2) Simone Montangero, "Introduction to Tensor Network Methods", Springer (2018) 3) For TTOs: Nora Reinić et al., "Finite-temperature Rydberg arrays: quantum phases and entanglement characterization", Physical Review Research 6 (3), 033322 (2024). 4) For sampling: Marco Ballarin et al., "Optimal sampling of tensor networks targeting wave function's fast decaying tails", arXiv:2401.10330 (2024) You find a bibtex amongst the files. We provide additional information on how to run qtealeaves via the Quantum TEA homepage. (Authors are in alphabetical order.)
Tensor network methods, Quantum information, Tree tensor networks, Quantum physics, Matrix product states, Quantum simulation
Tensor network methods, Quantum information, Tree tensor networks, Quantum physics, Matrix product states, Quantum simulation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
