QuTiP 2: A Python framework for the dynamics of open quantum systems

By J. Johansson, Paul D. Nation, Franco Nori. Computer Physics Communications, 2012

2,218 citations recorded by the source corpus.

Abstract We present version 2 of QuTiP, the Quantum Toolbox in Python. Compared to the preceding version [J.R. Johansson, P.D. Nation, F. Nori, Comput. Phys. Commun. 183 (2012) 1760.], we have introduced numerous new features, enhanced performance, and made changes in the Application Programming Interface (API) for improved functionality and consistency within the package, as well as increased compatibility with existing conventions used in other scientific software packages for Python. The most significant new features include efficient solvers for arbitrary time-dependent Hamiltonians and collapse operators, support for the Floquet formalism, and new solvers for Bloch–Redfield and Floquet–Markov master equations. Here we introduce these new features, demonstrate their use, and give a summary of the important backward-incompatible API changes introduced in this version. Program Summary Program title: QuTiP: The Quantum Toolbox in Python Catalog identifier: AEMB_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMB_v2_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 33625 No. of bytes in distributed program, including test data, etc.: 410064 Distribution format: tar.gz Programming language: Python. Computer: i386, x86-64. Operating system: Linux, Mac OSX. RAM: 2+ Gigabytes Classification: 7. External routines: NumPy, SciPy, Matplotlib, Cython Catalog identifier of previous version: AEMB_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 1760 Does the new version supercede the previous version?: Yes Nature of problem: Dynamics of open quantum systems Solution method: Numerical solutions to Lindblad, Floquet–Markov, and Bloch–Redfield master equations, as well as the Monte Carlo wave function method. Reasons for new version: Compared to the preceding version we have introduced numerous new features, enhanced performance, and made changes in the Application Programming Interface (API) for improved functionality and consistency within the package, as well as increased compatibility with existing conventions used in other scientific software packages for Python. The most significant new features include efficient solvers for arbitrary time-dependent Hamiltonians and collapse operators, support for the Floquet formalism, and new solvers for Bloch–Redfield and Floquet–Markov master equations. Restrictions: Problems must meet the criteria for using the master equation in Lindblad, Floquet–Markov, or Bloch–Redfield form. Running time: A few seconds up to several tens of hours, depending on size of the underlying Hilbert space.

Subfields: Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/8a950eba288f908df4cce6c864338f2f0a6b2004

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains (2016, methodology): “idespreadtheuseofsuchnumericaltechniquesamongthe condensedmattercommunity: theAlgorithmsandLibrariesforPhysicsSimulations[10,11,12, 13](ALPS),theC++ libraryITensor[14],theQuantumToolboxinPython(QuTiP)[15,16], as well as the Mathematica Quantum Many-Body Physics PackageDiracQ[17] are among the most c”
Open quantum systems with local and collective incoherent processes: Efficient numerical simulations using permutational invariance (2018, methodology): “PIQS is a robustly tested library developed in Python, designed to be memory efficient, fast, and easy to use.”
Hybrid-Liouvillian formalism connecting exceptional points of non-Hermitian Hamiltonians and Liouvillians via postselection of quantum trajectories (2020, background): “[56] J.”
Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe (2022, methodology): “Note that these methods can also be combined with software for computing quantum dynamics [312].”
Training Schrödinger’s cat: quantum optimal control (2015, methodology): “nce) has been the most extensively used general-purpose software and also includes an optimal control toolbox to facilitate robust experiment design [415]. MATPULSE [137, 255], DYNAMO [264] and QuTiP [184] are versatile Matlab and Python based simulation and optimization programs. Other software pac”
Repeated quantum error detection in a surface code (2019, neutral): “We solve the master equation numerically [51].”
ARC: An open-source library for calculating properties of alkali Rydberg atoms (2016, methodology): “tools like ElecSus [59], The Software Atom [60] and QuTIP [61].”
Theory of variational quantum simulation (2018, methodology): “ween the exact result and the result obtained from variational algorithm. We set the time step δt= 0.01 and simulated from t= 0 to t= 10. We used a software package QuTIP for the numerical simulation [61,62]. 7 Discussion In this work, we focus on the theory of variational quantum simulation. We rst”