QC Lab: A Python Package for Quantum–Classical Dynamics

QC Lab is an open-source Python package for quantum-classical (QC) dynamics simulations aimed to promote the development of QC algorithms, and their application to a wide variety of relevant model problems. It follows a modular design that facilitates cross-compatibility between algorithms and models. By decomposing algorithms and models into a series of tasks and ingredients that can be substituted and reused, it minimizes development efforts and code redundancy. In this Paper, we introduce the first stable version of QC Lab, and describe its design philosophy.

Authors

Institutions

Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2026-03-23
DOI
https://doi.org/10.1021/acs.jctc.5c01818
Primary Topic
Cold Atom Physics and Bose-Einstein Condensates
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

QC Lab: A Python Package for Quantum–Classical Dynamics

Alex Krotz, Ken Miyazaki, Roel Tempelaar, Antonio J. Garzón-Ramírez et al.
Journal of Chemical Theory and Computation
Cold Atom Physics and Bose-Einstein Condensates
article

QC Lab: A Python Package for Quantum–Classical Dynamics

Alex Krotz, Ken Miyazaki, Roel Tempelaar, Antonio J. Garzón-Ramírez, Ethan Byrd
article en

Abstract

QC Lab is an open-source Python package for quantum-classical (QC) dynamics simulations aimed to promote the development of QC algorithms, and their application to a wide variety of relevant model problems. It follows a modular design that facilitates cross-compatibility between algorithms and models. By decomposing algorithms and models into a series of tasks and ingredients that can be substituted and reused, it minimizes development efforts and code redundancy. In this Paper, we introduce the first stable version of QC Lab, and describe its design philosophy.

Journal of Chemical Theory and Computation
Northwestern University (US)
Openalex Percentile: Top 10%
Cold Atom Physics and Bose-Einstein Condensates
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.