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.

This Python software package provides functionality for inference of the initial state of a Hidden Markov Model (HMM), when we have access to permutations of the underlying states. We provide both analytical calculations to compute the probability of correct inference, and functionality for Monte Carlo computations. Further details are provided in arxiv:xxxx.xxxxxx.

This Python software package provides functionality for inference of the initial state of a Hidden Markov Model (HMM), when we have access to permutations of the underlying states. We provide both analytical calculations to compute the probability of correct inference, and functionality for Monte Carlo computations. Further details are provided in arxiv:xxxx.xxxxxx.

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.

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.

Supplementary data for the article "Quantum state tracking and control of a single molecular ion in a thermal environment" by Yu Liu, Julian Schmidt, Zhimin Liu, David R. Leibrandt, Dietrich Leibfried, Chin-wen Chou, submitted to Science in 2024. The manuscript describes a quantum state-specific investigation of the molecular state evolution of a single CaH+ ion in a thermal environment. The molecular state can be tracked in real time with single quantum-state resolution and the thermal radiation-induced transitions can be reversed with coherent molecular state manipulation according to the outcomes of state measurements. Results on the transition rates are used to infer the properties of the thermal environment. The data may be used to reproduce the plots shown in the figures.

Supplementary data for the article "Quantum state tracking and control of a single molecular ion in a thermal environment" by Yu Liu, Julian Schmidt, Zhimin Liu, David R. Leibrandt, Dietrich Leibfried, Chin-wen Chou, submitted to Science in 2024. The manuscript describes a quantum state-specific investigation of the molecular state evolution of a single CaH+ ion in a thermal environment. The molecular state can be tracked in real time with single quantum-state resolution and the thermal radiation-induced transitions can be reversed with coherent molecular state manipulation according to the outcomes of state measurements. Results on the transition rates are used to infer the properties of the thermal environment. The data may be used to reproduce the plots shown in the figures.

Qubit noise is one of the major barriers limiting broad adoption of quantum computing in solving complex real-word problems, such as optimisation. The qubit noise and the local minima in the underlying optimisation objective function often make the probability of obtaining the true optimal solution (global optimal rather than local minima) decrease exponentially with increasing underlying problem complexity. A quantum computing error mitigation invention has been filed by CSIRO with IP Australia in Feb 2024. Based on the invention, a Python SEMO (spin-error mitigation for optimisation) software module has been developed. The SEMO software module is available for collaboration for selected parties with a SEMO digital token issued from the CSIRO applied quantum computing project team. The token is authenticated online against the registration server at the beginning of each usage session. The SEMO software module can be evaluated without a digital token for small problems up to 100 spin variables and 200 couplings. Information about CSIRO applied quantum computing and SEMO is available at http://research.csiro.au/aqc. Enquiries about accessing the SEMO software module, please email appliedquantumcomputing@csiro.au.

양자통신과 양자센서, 양자컴퓨팅 전 분야를 포괄하는 양자산업 생태계 정보를 통합.집대성 하고, 국내외 시장, 정책, 투자, 표준화 , 특허 등 양자정보기술 동향과 분야별 R&D 동향, 각 산업 분야별 활용모델을 소개하여 미래 양자정보기술의 산업화를 위한 방향 제시 - 목차- 1. 양자기술동향 2. 양자기술 R&D동향 3. 양자기술 산업화 모델 4. 양자기술 전문인력 교육 정보 및 현황 5. 글로벌 양자분야 주요 산학연 협의체 현황