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32 results for “quantum efficiency”
Dataset for "Fast and efficient demultiplexing of single photons from a GaAs quantum dot with resonantly enhanced electro-optic modulators"
<p><strong>Dataset for "Fast and efficient demultiplexing of single photons from a quantum dot with resonantly enhanced electro-optic modulators"</strong></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p>
High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx
<p>This page contains selected data from the peer-reviewed paper "High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx" published in Sensors by Ozhan Koybasi.</p> <p>Description of attached files:</p> <p>Figure 5. Simulated p-polarization reflectance as a function of wavelength for PQEDs mounted in trap configuration with an angle of 15° between the diodes. In this configuration the light beam undergoes 7 reflections: one at 0° degree and two reflections at 15°, 30°, and 45°. The reflectance is reported for six different thicknesses of the SiNx.</p> <p>Figure 6. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness for PQEDs mounted in trap configuration with an angle of 15° between the diodes.</p> <p>Figure 7. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness, when a buffer layer of 6 nm SiO2 is depos-ited before SiNx, for PQEDs mounted in trap configuration with an angle of 15° between the di-odes.</p> <p>Figure 8. Effective lifetime τeff vs. excess carrier density (Δn) for samples prepared with passivation processes described in Table 1.</p> <p>Figure 9. Photoluminescence (PL) lifetime images of samples prepared with the passivation processes described in Table 1.</p> <p>Figure 10. Capacitance—voltage (C—V) measurement results of MIS capacitors prepared with the passivation processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1, at a frequency of 1 kHz.</p> <p>Figure 11. Injection dependent effective minority carrier lifetime τeff (Δn) of test samples passivated with processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1 with simu-lation fits to extract SRV and τbulk.</p> <p>Figure 12. Simulated IQD as a function of reverse bias voltage for p-type inversion-layer photodi-ode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a wavelength of 488 nm.</p> <p>Figure 13. Simulated IQD as a function of wavelength for p-type inversion-layer photodiode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a reverse bias voltage of 5 V.</p> <p>Figure 16. Spatial uniformity of optical power responsivity of the PQEDs with SiO2/SiNx stack photodiodes P18-55-45 (a) and P18-54-44 (b).</p> <p> </p>
Dataset for "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency"
<p>The dataset contains the data used in the article "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency".</p> <p>A low-cost monitoring system composed of 6 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115C0-003), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from November 2022 to June 2023. It contains the median of the PM2.5, relative humidity, temperature, and atmospheric pressure measurements of the low-cost sensors, after being aggregated to either one minute or one hour.</p> <p>The official measurements of the beta attenuation device are also provided.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Data for: Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation
<p>Data for publication: J. Roulet, S. Choi, J. Vanicek, Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation, J. Chem. Phys. <strong>150</strong>, 204113 (2019)</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Efficient learning of quantum noise
<p>Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently uncorrelated and weak noise could be used to solve computational problems that are intractable with current digital computers. There has been substantial progress towards engineering such systems. However, continued progress depends on the ability to characterize quantum noise reliably and efficiently with high precision. Here we introduce a protocol that comprehensively and efficiently characterizes the error rates of quantum noise and we experimentally implement it on a 14-qubit superconducting quantum architecture. The method returns an estimate of the effective noise with relative precision and can detect arbitrary correlated errors. We show how to construct a quantum noise correlation matrix allowing the easy visualization of all pairwise correlated errors, enabling the discovery of long-range two-qubit correlations in the 14 qubit device that had not previously been detected. These properties of the protocol make it exceptionally well suited for high-precision noise metrology in quantum information processors. Our results are the first implementation of a provably rigorous, full diagnostic protocol capable of being run on state of the art devices and beyond. These results pave the way for noise metrology in next-generation quantum devices, calibration in the presence of crosstalk, bespoke quantum error-correcting codes, and customized fault-tolerance protocols that can greatly reduce the overhead in a quantum computation.</p>
Quantum efficiency and vertical position of quantum emitters in hBN determined by Purcell effect in hybrid metal-dielectric planar photonic structures
<p>Data from the article "Quantum efficiency and vertical position of quantum emitters in hBN determined by Purcell effect in hybrid metal-dielectric planar photonic structures", <a href="https://pubs.acs.org/doi/10.1021/acsphotonics.4c01416">ACS Photonics, (2024)</a> - [arXiv:2407.20160]</p>
Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"
<p>Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory. See the README for more details.</p>
Provably efficient machine learning for quantum many-body problems
<p>Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems".</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs
<p>Abstract: Quantum key distribution (QKD) represents a cutting-edge technology that ensures unbreakable security. Coexisting quantum and classical signals on a multi-band (O+E+S+C+L-band) system offer a viable solution for secure, high-rate networks amidst growing classical traffic and address quantum signal sensitivity. In this study, we assume a dynamic classical traffic load and varying configurations of classical channels (CChs). Considering the varying behavior of Secure Key Rate (SKR) under different classical conditions, solving the integral noise equations are crucial for optimizing QKD implementation and enhancing resource efficiency. The complexity and time-consuming nature of this process challenge infrastructure providers in determining the optimal quantum channel (QCh) frequency in real time. To tackle these challenges, we propose a machine learning (ML) algorithm. By leveraging ML, QKD can be implemented efficiently, optimizing resource utilization while significantly reducing computation and processing time in dynamic classical traffic. We implement three ML algorithms at various fiber intervals, all of which estimate the optimal frequency for QCh with 99\% accuracy and perform computations on average in 0.09 seconds, which is significantly faster compared to integral computational methods that have a mean time of 637 seconds.<br><br>Information: In this file, the Excel sheet contains data for each fiber interval, including inputs such as fiber length in each interval, the overall classical loading factor percentage, the C-band loading factor percentage, the L-band loading factor percentage, the highest active classical frequency (which serves as input to the machine learning model), and the QCh frequency that resulted in the highest SKR.</p>
Self-assembled molecules for hole extraction in efficient inverted PbS quantum dot solar cells
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Data and code for "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification"
<p>It contains the code and the data to reproduce the figures in the paper "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification" published in arXiv: https://arxiv.org/abs/2309.09976</p>
Efficient learning of quantum noise
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Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo
<p>Data and Python Scripts required to produce the figures in:</p> <p>Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo</p> <p>http://arxiv.org/abs/1601.00865</p> <p>Reanaylisis requires pyhande (part of the HANDE package) available from:</p> <p>https://github.com/hande-qmc/hande.git</p> <p>To reproduce the figures by reanalysing the data from scratch modify sys.path.append() in ./bin/Efficiency.py and in ./figure4/figure4.py. To point to hande_top_level_dir/tools.</p> <p> </p>
High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
<p>data used to generate the numbers and figures in the paper</p>
Numerical data supporting the publication "Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization"
<p>Numerical data and plotting scripts for regenerating figures in the article "Accurate and gate-efficient quantum ansätze for electronic states without adaptive optimisation".</p> <p>See README for more details.</p>
Data of "Efficient and Device-Independent Active Quantum State Certification"
<p>Dataset and analysis code for the manuscript "Efficient and Device-Independent Active Quantum State Certification"</p>
Correcting Artifacts in Single Molecule Localization Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras
<p>Jupyter notebooks and supplementary data for the paper "Correcting Artifacts in Single Molecule Localization<br> Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras".</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
<p> In this paper, we demonstrate a classical simulation framework for quantum computation by contracting tensor networks of sizes previously deemed out of reach. The main contribution of this work is a parallelization scheme called <em>index slicing</em> that breaks down an infeasibly large tensor network contraction task into smaller subtasks that can be executed fully in parallel, without interdependencies or intermediate communications. As a benchmarking example, we show that our algorithm can reduce the simulation of the Sycamore random circuit sampling task to less than 20 days, achieving an acceleration of over five orders of magnitude compared to the original proposal. We then showcase the capabilities of the simulation framework via investigations of near-term quantum algorithms and quantum error correction. Given the ubiquity of tensor networks in quantum information science, we believe that our simulation framework will be a valuable tool in the era of quantum information technology.</p>
Raw data for publication titled " GaN buffer growth temperature and efficiency of InGaN/GaN quantum wells: The critical role of nitrogen vacancies at the GaN surface"
<p>Raw data (Time-resolved photoluminescence and Secondary Ion Mass Spectrometry) used for the publication: <a href="https://doi.org/10.1063/5.0040326">https://doi.org/10.1063/5.0040326</a></p> <p>Layer sequence of each sample could be found in the excel sheet named SampleLibrary</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.