This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry. Here, the array of features measured and modeled is periodic in one-dimension (i.e., a line grating) with a nominal line width of 100 nm placed at 300 nm intervals, or pitch = 300 nm; the short-hand label for the features is "L100P300." The entirety of the modeled data is included, over two thousand simulations that are indexed using a top, middle, and bottom linewidth as floating parameters. Two subsets of these data, featuring differing sampling strategies, are also provided. This data set also contains angle-resolved optical measurements with uncertainties for nine arrays which differ in their dimensions due to lithographic variations using a focus/exposure matrix, as identified in a previous publication (https://doi.org/10.1117/12.777131). We have previously reported line widths determined from these measurements based upon non-linear regression to compare theory to experiment. Machine learning approaches are to be fostered for solving such inverse problems. Data are formatted for direct use in "Model-Based Optical Metrology in R: MoR" software which is also available from data.nist.gov. (https://doi.org/10.18434/T4/1426859). Note: Certain commercial materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials are necessarily the best available for the purpose.

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry. Here, the array of features measured and modeled is periodic in one-dimension (i.e., a line grating) with a nominal line width of 100 nm placed at 300 nm intervals, or pitch = 300 nm; the short-hand label for the features is "L100P300." The entirety of the modeled data is included, over two thousand simulations that are indexed using a top, middle, and bottom linewidth as floating parameters. Two subsets of these data, featuring differing sampling strategies, are also provided. This data set also contains angle-resolved optical measurements with uncertainties for nine arrays which differ in their dimensions due to lithographic variations using a focus/exposure matrix, as identified in a previous publication (https://doi.org/10.1117/12.777131). We have previously reported line widths determined from these measurements based upon non-linear regression to compare theory to experiment. Machine learning approaches are to be fostered for solving such inverse problems. Data are formatted for direct use in "Model-Based Optical Metrology in R: MoR" software which is also available from data.nist.gov. (https://doi.org/10.18434/T4/1426859). Note: Certain commercial materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials are necessarily the best available for the purpose.

The Maxwell-Bloch equations for a two-level system are solved in a particular case. The example follows that of P. Siddons, "Light propagation through atomic vapours," Journal of Physics B: Atomic, Molecular, and Optical Physics 47, 093001 (2014). In the reference, the optical intensity, population of the upper state and coherence are given for light with a carrier frequency which is on resonance. Here, the same example is worked, but the result is given at intermediate times as well as at the entrance and exit faces as in the example. The solution is found using Mathematica's NDSolve for the time dimension and the Method of Lines for propagation in space.

The Maxwell-Bloch equations for a two-level system are solved in a particular case. The example follows that of P. Siddons, "Light propagation through atomic vapours," Journal of Physics B: Atomic, Molecular, and Optical Physics 47, 093001 (2014). In the reference, the optical intensity, population of the upper state and coherence are given for light with a carrier frequency which is on resonance. Here, the same example is worked, but the result is given at intermediate times as well as at the entrance and exit faces as in the example. The solution is found using Mathematica's NDSolve for the time dimension and the Method of Lines for propagation in space.

This is the data underlying "Sub-Doppler spectroscopy of quantum systems through nanophotonic spectral translation of electro-optic light" https://arxiv.org/abs/2309.16069 which is to be published in Nature Photonics.

This dataset represents the results of calculations of atomic absorption spectra for the case of two-color EIT. We compare computation methods, specifically Gaussian sampling, to find that one sampling method converges to smooth transmittance curves in less time than the other. We also include some example scripts which generate and plot the figure data.

This dataset represents absorption/transmission spectra of resonant probe light power through a Rydberg atom vapor, subject to a simultaneous dressing field and a 'low frequency' field. Data is taken as an oscilloscope average of 5 photodiode voltage traces, with frequency offsets given by a simultaneous reference cell (not included). Some data are given as 2-D arrays, with axes of laser detuning across a waterfall of field strength. Some data represents theory eigen-energies of the system, for comparison. This paper will be submitted to Physical Review Letters.

This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3. Data for figure 3 is given in a slightly unorthodox form, where the [electric field over position plot] and the [total transmission over detuning plot] are given along either of the large array's corresponding axes, matching the arrangement in Fig 3. All data is in CSV comma separated value form, with return characters between rows.

These data files contain the data for the measured transition frequencies shown in Table I and the traces in Figure 3 of the publication "Frequency-comb spectroscopy on pure quantum states of a single molecular ion," accessible at https://arxiv.org/abs/1911.12808. In this publication we use generally applicable quantum-logic techniques to prepare a trapped molecular ion in a single quantum state, drive terahertz rotational transitions with an optical frequency comb, and read out the molecular state non-destructively, leaving the molecule ready for further manipulation. One file contains data For Table 1. In the measurement of rotational transition frequencies, the intensities of the comb beams are varied to characterize the effect of AC Stark shift, while the intensity ratio between the sigma and pi polarized beams are kept at close to 2. The average intensity of the sigma-polarized comb beam is quantified by measuring the resultant Stark shift, fSS_sigma, on the 729 nm transition of the Ca+ ion, with the Ca+ ion where the CaH+ ion would be during rotational spectroscopy experiments. The other file contains data for Figure 3, (a) Spectra for the J = 4 to 2 transition: 40CaH+ is prepared in J = 2, followed by a pulse train from the comb Raman beams probing the J = 2 to J = 4 transition. After the probe pulse train, projective measurements of both initial and final states are performed and the state occupation probability is determined. The probe time is ~1.6 ms. The frequency shows the offset of the Raman difference frequency from the resonant value. (b) Rabi flopping on the J = 4 to J = 2 transition: Starting in J = 4, with the comb Raman pulse detuning set to resonance, the state of the 40CaH+ ion is driven coherently to J = 2 by a pulse train of variable duration from the comb Raman beams. The center wavelength of the frequency comb was ~800 nm for these spectra and Rabi flopping traces. The error bars stand for ±1 standard deviation.

The dataset underlying the figures in the manuscript is "Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays."Abstract of the paper: Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.Data description: Each figure folder contains a complete set of files necessary to reproduce figures, including Jupyter Notebooks with the figure source code, Adobe Illustrator, and pre-processed data files (hdf5 and pkl). The complete set of all raw data files used in this study is available at Zenodo. [doi: 10.5281/zenodo.14173838].Acknowledgments: This research was sponsored in part by the Army Research Office (ARO) under Awards No. W911NF-23-1-0110 and W911NF-23-1-0258. We acknowledge support from the European Union through the IGNITE project with grant agreement No. 101069515 and from the Dutch Research Council (NWO) via the National Growth Fund program Quantum Delta NL (Grant No. NGF.1582.22.001). The views, conclusions, and recommendations contained in this paper are those of the authors and are not necessarily endorsed nor should they be interpreted as representing the official policies, either expressed or implied, of the Army Research Office (ARO) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.