QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains
By Phillip Weinberg, Marin Bukov. 2016
380 citations recorded by the source corpus.
We present a new open-source Python package for exact diagonalization and quantum dynamics of spin(-photon) chains, called QuSpin, supporting the use of various symmetries in 1-dimension and (imaginary) time evolution for chains up to 32 sites in length. The package is well-suited to study, among others, quantum quenches at finite and infinite times, the Eigenstate Thermalisation hypothesis, many-body localisation and other dynamical phase transitions, periodically-driven (Floquet) systems, adiabatic and counter-diabatic ramps, and spin-photon interactions. Moreover, QuSpin's user-friendly interface can easily be used in combination with other Python packages which makes it amenable to a high-level customisation. We explain how to use QuSpin using four detailed examples: (i) Standard exact diagonalisation of XXZ chain (ii) adiabatic ramping of parameters in the many-body localised XXZ model, (iii) heating in the periodically-driven transverse-field Ising model in a parallel field, and (iv) quantised light-atom interactions: recovering the periodically-driven atom in the semi-classical limit of a static Hamiltonian.
Subfields: Atomic and Molecular Physics, and Optics, Condensed Matter Physics
View Paper: https://www.semanticscholar.org/paper/71f0412d3911ba81cf88b29f2f9e065cd994aac1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
- R. Shir
- A. Sánchez-Garrido
- E. Rabinovici
- J. Sonner
- Pankaj Mehta
- Dries Sels
- A. Polkovnikov
- Phillip Weinberg
In Condensed Matter Physics
Citations in the corpus
- Krylov complexity from integrability to chaos (2022, methodology): “Some of the Hamiltonians and operators used in the numerical computations were constructed using the QuSpin package [43].”
- Reinforcement Learning in Different Phases of Quantum Control (2017, methodology): “We use QUSPIN for simulating the dynamics of the qubit systems [90,91].”