A most-likely noise path control strategy for qubit state preparation under dephasing achieves higher success rates than mean-path methods while the latter maximize average fidelity.
QuTiP 2: A Python framework for the – 33 – dynamics of open quantum systems
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abstract
We present version 2 of QuTiP, the Quantum Toolbox in Python. Compared to the preceding version [Comput. Phys. Comm. 183 (2012) 1760], we have introduced numerous new features, enhanced performance, 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.
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Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.
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Quantum state-preparation control in noisy environment via most-likely paths
A most-likely noise path control strategy for qubit state preparation under dephasing achieves higher success rates than mean-path methods while the latter maximize average fidelity.
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Quantum Tomography and Entanglement in Semi-Leptonic $h\to VV^*$ Decays at Higher Orders
Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.