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arXiv:1110.0573 · Computer Physics Communications

QuTiP: An open-source Python framework for the dynamics of open quantum systems

J. R. Johansson, Paul D. Nation, Franco Nori

Published Feb 28, 2012• 2,130 citations
arXivHTMLPDF
DOI
10.1016/j.cpc.2012.02.021

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We present QuTiP (http://www.qutip.org),an object-oriented, opensource framework for solving the dynamics of open quantum systems.Written in Python, and using a combination of Cython, NumPy, SciPy, and matplotlib, QuTiP provides an environment for computational quantum mechanics that is both easy and efficient to use.Arbitrary quantum systems, including timedependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of unitary or dissipative evolution solvers.Here we give an overview of the basic structure for the framework, and the techniques used in its implementation.We also present a few selected examples from current research on quantum mechanics that illustrate the strengths of the framework, as well as the types of calculations that can be performed.This framework is particularly well suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal as an educational tool.
Nitrogen-vacancy (NV) centers in diamond are a solid-state platform for quantum sensing 1 and information processing due to their spin-dependent optical transitions at room temperature 2. Accurate characterization of NV spin dynamics, including hyperfine interactions with nuclear spins, is essential both for optimizing sensing performance and for designing high-fidelity quantum controls. In this work, we use the open-source package/ library Quantum Toolbox in Python (QuTiP) 3, 4 to simulate two readout protocols (continuous-wave ODMR, pulsed ODMR) with and without the hyperfine coupling. By comparing simulated resonance positions and population dynamics against experimental and simulation profiles from the literature, we demonstrate that our QuTiP model reproduces the characteristic profiles present in other works. We analyze the computational cost of including hyperfine terms to the model via execution time averaging and show that built-in parallelization accelerates the no-hyperfine model but provides small returns when the coupling is included. Finally, we illustrate how simulation parameters align with predictions of DC magnetometry 5. We also provide some ideas for extending this framework to GPU-accelerated and/or machine learning assisted parameter estimation and calibration 6, 7. Our soon-to-be open-source code 8 provides a flexible foundation for both quantum sensing characterization and the development of NV-based quantum information protocols.