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.

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device. Each sample data is stored as a 100 x 100-pixel map from plunger gate voltages to (i) current through the device at infinitesimal bias, (ii) output of the charge sensor evaluated as the Coulomb potential at the sensor location - the experimentally relevant parameters that can be measured, (iii) information about the number of charges on each dot (with a default value 0 for short circuit and a barrier), and (iv) a label determining the state of the device, distinguishing between a single dot, a double dot, a short circuit, and a barrier state. The expanded dataset, QFlow 2.0, consists of 1599 idealized simulated measurements stored as roughly 250 x 250-pixel maps from plunger gate voltages to (i) output of the charge sensor, (ii) net charge on each dot, and (iii) a label determining the state of the device, distinguishing between a left, central, and right single QD, a double QD, and a barrier or short circuit (no QD) state. In addition, the QFlow 2.0 dataset includes two sets of noisy simulated measurements, one with the noise level varied around 1.5 times the optimized noise level and the other one with the noise level ranging from 0 to 7 times the optimized noise level. See the "Project description" and "Data structure" documents for additional information about these datasets.Acknowledgments: This research is sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. The development and maintenance of the growth facilities used for fabricating samples were supported by the Department of Energy, through Grant No. DE-FG02-03ER46028. We acknowledge the use of clean room facilities supported by The National Science Foundation (NSF) through the UW-Madison MRSEC (DMR-1720415) and electron beam lithography equipment acquired with the support of the NSF MRI program (DMR-1625348). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the 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 NIST.

This software package performs joint quantum state and measurement tomography. The software is provided as Python source code. A description of the algorithms used is in "Joint Quantum State and Measurement Tomography with Incomplete Measurements" https://arxiv.org/abs/1803.08245Included are three example scripts that simulate data for one or two trapped ion systems with either symmetric or asymmetric measurements:- analysis_scripts/paper_simulations.py: produces all data and histograms shown in related publication with seed = 0. Also provides an example of symmetric measurements.- analysis_scripts/asym_simulations.py: produces simulated data from asymmetric measurements by similar methods as in previous script.- analysis_scripts/load_tutorial/load_simulations.py: gives an example of loading data with asymmetric measurements.

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.

Data presented is part of the journal manuscript "Optically Distributing Remote Two-node Microwave Entanglement using Doubly Parametric Quantum Transducers." Data includes graphical plots generated from numerical models and computations for various network topologies which illustrate their thresholds for achieving quantum information transfer.

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.

Data presented is part of abstract and poster titled "Entangling Superconducting Qubits over Optical Fiber ? Towards Optimization and Implementation" presented at "IEEE International Conference on Quantum Computing and Engineering - QCE22". Data includes a demonstration of squeezed light generation as the twin beam difference current relative to shot noise and a numerical simulation of the performance of 4 quantum network topologies.