•Open-source cross-platform software for direct and inverse Mie calculations.•Derive complex refractive indices from optical measurements.•Compute efficiencies for single particles, coated spheres, ...or polydisperse ensembles.•Compute angular scattering intensity functions and matrix elements.
The complex refractive index m = n + ik of a particle is an intrinsic property which cannot be directly measured; it must be inferred from its extrinsic properties such as the scattering and absorption cross-sections. Bohren and Huffman called this approach “describing the dragon from its tracks”, since the inversion of Lorenz-Mie theory equations is intractable without the use of computers. This article describes PyMieScatt, an open-source module for Python that contains functionality for solving the inverse problem for complex m using extensive optical and physical properties as input, and calculating regions where valid solutions may exist within the error bounds of laboratory measurements. Additionally, the module has comprehensive capabilities for studying homogeneous and coated single spheres, as well as ensembles of homogeneous spheres with user-defined size distributions, making it a complete tool for studying the optical behavior of spherical particles.
Qudi is a general, modular, multi-operating system suite written in Python 3 for controlling laboratory experiments. It provides a structured environment by separating functionality into hardware ...abstraction, experiment logic and user interface layers. The core feature set comprises a graphical user interface, live data visualization, distributed execution over networks, rapid prototyping via Jupyter notebooks, configuration management, and data recording. Currently, the included modules are focused on confocal microscopy, quantum optics and quantum information experiments, but an expansion into other fields is possible and encouraged.
Significant progress has recently been made in calculating muon stopping sites using density functional theory. The technique aims to address two of the most common criticisms of the muon-spin ...spectroscopy (μ+SR) technique, namely, where in the sample does the muon stop, and what is its effect on its local environment. We have designed and developed a program called MuFinder that enables users to carry out these calculations through a simple graphical user interface (GUI). The procedure for calculating muon sites by generating initial muon positions, relaxing the structures, and then clustering and analysing the resulting candidate sites, can be done entirely within the GUI. The local magnetic field at the muon site can also be computed, allowing the connection between the muon sites obtained and experiment to be made. MuFinder will make these computations significantly more accessible to non-experts and help to establish muon site calculations as a routine part of μ+SR experiments.
Program Title: MuFinder
CPC Library link to program files:https://doi.org/10.17632/pwwt7p9hv8.1
Developer's repository link:https://gitlab.com/BenHuddart/mufinder
Licensing provisions: GPLv3
Programming language: Python
Nature of problem: To automate the process of calculating muon stopping sites using density functional theory, thereby making these calculations accessible to non-experts.
Solution method: A Python-based graphical user interface (GUI) through which users can calculate muon stopping sites using the structural relaxation method. The program makes use of newly-developed algorithms for generating candidate initial muon positions and for clustering muon positions obtained from the structural relaxations. Analysis of the muon sites, including calculation of the local dipolar magnetic field, is also possible within the GUI.
QuOCS: The quantum optimal control suite Rossignolo, Marco; Reisser, Thomas; Marshall, Alastair ...
Computer physics communications,
October 2023, 2023-10-00, Volume:
291
Journal Article
Peer reviewed
Open access
Quantum optimal control includes a family of pulse-shaping algorithms that aim to unlock the full potential of a variety of quantum technologies. The Quantum Optimal Control Suite (QuOCS) unites ...experimental focus and model-based approaches in a unified framework. Easy usage and installation presented here and the availability of various combinable optimization strategies is designed to improve the performance of many quantum technology platforms, such as color defects in diamond, superconducting qubits, atom- or ion-based quantum computers. It can also be applied to the study of more general phenomena in physics. In this paper, we describe the software and the toolbox of gradient-free and gradient-based algorithms. We then show how the user can connect it to their experiment. In addition, we provide illustrative examples where our optimization suite solves typical quantum optimal control problems, in both open- and closed-loop settings. Integration into existing experimental control software is already provided for the experiment control software Qudi (Binder et al., 2017 41), and further extensions are investigated and highly encouraged.
QuOCS is available from GitHub, under Apache License 2.0, and can be found on the PyPI repository.
Program Title: QuOCS - Quantum Optimal Control Suite
CPC Library link to program files:https://doi.org/10.17632/wjjch757fk.1
Developer's repository link:https://github.com/Quantum-OCS/QuOCS
Licensing provisions: Apache-2.0
Programming language: Python
External routines: NumPy 1, SciPy 1, JAX 2
Nature of problem: Quantum systems are typically controlled by time-dependent electromagnetic fields to perform a certain set of quantum operations. Those operations may in turn provide building blocks for various quantum information processing tasks such as quantum computation, communication, simulation, sensing, and metrology. Numerous control strategies exist to design and improve such operations 3. While some strategies are constructed to target a rather specific problem with high efficiency, others are more general to solve a wide range of applications 4,5. To access the different algorithms, one has to download different optimization suites with different input and output parameters, making them hard to compare and combine. To benefit from the variety of algorithms, we have devised a customizable and intuitive optimization suite that simultaneously provides access to some of the most popular quantum optimal control algorithms.
Solution method: We combine, in a unified framework, some of the frequently used optimal control algorithms which are the dressed Chopped Random Basis method (dCRAB) 6, and Gradient Ascent Pulse Engineering (GRAPE) 7, with an extension to make use of Automatic Differentiation (AD) 8. The software is able to connect to both models of quantum dynamics in simulations and real-time quantum experiments to perform open- and closed-loop optimization, respectively. With minimal knowledge of optimal control theory, the user can manage to run optimizations of quantum processes using a variety of additional features such as stopping criteria and drift compensation. Logging and data management of the optimization progress and results are also handled by the suite. Its modular structure allows for extensions that accommodate customized algorithms and can be implemented by the user straightforwardly.
Additional comments including unusual features: The connection to the experiments is performed by an extension that enables a direct integration to a laboratory control software Qudi 9.
1T.E. Oliphant, Comput. Sci. Eng. 9 (2007) 10, http://www.scipy.org/.2J. Bradbury, et al., JAX: composable transformations of Python+NumPy programs, http://github.com/google/jax, 2018.3S. Glaser, U. Boscain, T. Calarco, et al., Eur. Phys. J. D 69 (2015) 279.4C.P. Koch, U. Boscain, T. Calarco, et al., EPJ Quantum Technol. 9 (2022) 19.5Schaefer, Ido, Ronnie Kosloff, Phys. Rev. A 101 (2) (2020).6N. Rach, M.M. Müller, T. Calarco, S. Montangero, Phys. Rev. A 92 (2015) 6.7N. Khaneja, et al., J. Magn. Reson. 92 (6) (2015) 296–305.8N. Leung, et al., Phys. Rev. A 95 (2017) 4.9J.M. Binder, et al., SoftwareX 6 (2017) 85–90.
Timely graduation of students is essential for determining the quality of college. Universities must know the percentage of students' ability to complete their studies on time. So, to deal with this ...problem, data mining classification is carried out to predict student graduation on time to find patterns for student on-time graduation predictions. This research can yield new information to help colleges anticipate student graduations that are not on time. The method used is a classification data mining method with 4 algorithms: naïve Bayes, random forest, support vector machine (SVM), and artificial neural network (ANN). The attributes used are gender, parental income, length of guidance, working student status or not, semester 1 to semester 8 grades, and GPA. This study used Python 3 programming language on jupyter notebooks in Anaconda to process datasets. The distribution of datasets is divided by 70% for training data and 30% for testing data. The results of this study were obtained with the best algorithm accuracy in the support vector machine (SVM) algorithm is 0.94. Based on the results of this study, the accuracy is good for predicting student graduation on time.
For the analysis of low-statistics counting experiments, a traditional nonlinear least squares minimization routine may not always provide correct parameter and uncertainty estimates due to the ...assumptions inherent in the algorithm(s). In response to this, a user-friendly Python package (SATLAS) was written to provide an easy interface between the data and a variety of minimization algorithms which are suited for analyzinglow, as well as high, statistics data. The advantage of this package is that it allows the user to define their own model function and then compare different minimization routines to determine the optimal parameter values and their respective (correlated) errors. Experimental validation of the different approaches in the package is done through analysis of hyperfine structure data of 203Fr gathered by the CRIS experiment at ISOLDE, CERN.
Source code: https://github.com/woutergins/satlas/
Documentation: https://woutergins.github.io/satlas/
Program Title: SATLAS
Program Files doi:http://dx.doi.org/10.17632/3hr8f5nkhb.1
Licensing provisions: MIT
Programming language: Python
External routines/libraries: NumPy, SciPy, LMFIT, Pandas, NumDiffTools
Nature of problem: Fitting data from a counting experiment to extract parameter information.
Solution method: Supply a modular library with fitting routines using pre-implemented goodness-of-fit statistics for counting data under different circumstances.
We present an open-source software for simulation of observables in magnetic resonance experiments, including nuclear magnetic/quadrupole resonance NMR/NQR and electron spin resonance (ESR). Inspired ...by magnetic resonance protocols that emerged in the context of quantum information science (QIS), this software can assist experimental research in the design of new strategies for the investigation of fundamental quantum properties of materials. The package introduced here can simulate both standard NMR spectroscopic observables and the time-evolution of an interacting single-spin system subject to complex pulse sequences, i.e. quantum gates. The main purpose of this software is to facilitate the development of much needed novel NMR-based probes of emergent quantum order, which can be elusive to standard experimental probes. The software is based on a quantum mechanical description of nuclear spin dynamics in NMR/NQR experiments and has been widely tested on available theoretical and experimental results. Moreover, the structure of the software allows for basic experiments to be easily generalized to more sophisticated ones because it includes all the libraries required for the numerical simulation of generic spin systems. In order to make the program easily accessible to a large user base, we developed a user-friendly graphical interface, Jupyter notebooks, and fully-detailed documentation. Lastly, we portray several examples of the execution of the code that illustrate the prospects of a novel NMR paradigm, inspired by QIS, for efficient investigation of emergent phases in strongly correlated materials.
The present work reports the theoretical background and the capabilities of SEISMIC, a Python code specifically developed to calculate the propagation of the sound waves inside crystalline materials. ...SEISMIC is a tool integrated in the Quantas package and provides a series of useful information for engineers and geophysicists, such as the phase and group velocities, the power flow angle, the enhancement factors, and the polarization vectors, using as input the elastic moduli and the density of the material. Numerical treatments of the derivatives were avoided, using analytical methods to obtain numerically stable results. The code relies only on Python numerical and graphical libraries to ensure a full cross-platform usability.
•Automated software to calculate the propagation of seismic waves in crystalline solids.•Analytical evaluation of the second derivatives, which improve the description.•Entirely coded in Python, with no need of external software for plotting.•Integrated in Quantas, a software suite for the analysis of solid phases.•The code is completely cross-platform.
We present an open-source software for simulation of observables in magnetic resonance experiments, including nuclear magnetic/quadrupole resonance NMR/NQR and electron spin resonance (ESR). Inspired ...by magnetic resonance protocols that emerged in the context of quantum information science (QIS), this software can assist experimental research in the design of new strategies for the investigation of fundamental quantum properties of materials. The package introduced here can simulate both standard NMR spectroscopic observables and the time-evolution of an interacting single-spin system subject to complex pulse sequences, i.e. quantum gates. The main purpose of this software is to facilitate the development of much needed novel NMR-based probes of emergent quantum order, which can be elusive to standard experimental probes. The software is based on a quantum mechanical description of nuclear spin dynamics in NMR/NQR experiments and has been widely tested on available theoretical and experimental results. Moreover, the structure of the software allows for basic experiments to be easily generalized to more sophisticated ones because it includes all the libraries required for the numerical simulation of generic spin systems. In order to make the program easily accessible to a large user base, we developed a user-friendly graphical interface, Jupyter notebooks, and fully-detailed documentation. Lastly, we portray several examples of the execution of the code that illustrate the prosepcts of a novel NMR paradigm, inspired by QIS, for efficient investigation of emergent phases in strongly correlated materials.
Program Title: PULSEE (Program for the simULation of nuclear Spin Ensemble Evolution)
CPC Library link to program files:https://doi.org/10.17632/vvv8tcb2nt.1
Developer's repository link:https://github.com/vemiBGH/PULSEE
Licensing provisions: GPLv3
Programming language: Python 3
Nature of problem: Application of nuclear magnetic/quadrupole resonance techniques to study properties of materials often requires extensive spectral simulations. On the other hand, application of magnetic resonance techniques to quantum information science (QIS) involves different sets of observables. Available simulation software addresses only one of these applications: either detailed spectral simulations 1 or QIS relevant observables 2. For this reason, NMR has not seen as much development in the condensed matter community compared to other spectroscopic techniques that combine these two approaches. Therefore, there is a need for an up-to-date and easily accessible software that can simulate an extensive set of NMR/NQR experimental observables, reproducing the behavior/response of nuclear systems with a varying degree of complexity encountered in strongly correlated quantum materials.
Solution method: The open-source Python code provides an extensive set of libraries for the simulation of spin time evolution in the presence of specific interactions and reproduction of spectra; as well as other observables measured in magnetic resonance experiments; and simulations of quantum circuits and gates. The ready-to-use software features a user-friendly graphical interface, and Jupyter notebooks.
1F. A. Perras, C. M. Widdifield, and D. L. Bryce, “QUEST - Quadrupolar Exact Software: A fast graphical program for the exact simulation of NMR and NQR spectra for quadrupolar nuclei,” Solid State Nuclear Magnetic Resonance, vol. 45-46, pp. 36-44, (2012).2D. Possa, A. C. Gaudio, and J. C. C. Freitas, “Numerical simulation of NQR/NMR: Applications in quantum computing,” Journal of Magnetic Resonance, vol. 209, pp. 250-260, (2011).
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