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Dataset results
11 results for “spin chain”
Indicator values for the many-body localization of the Heisenberg spin chain at various energies, system sizes and disorder magnitude
<p>This data set accompanies the preprint <em>Scalable approach to many-body localization via quantum data </em>(arXiv: <a href="https://arxiv.org/abs/2202.08853">2202.08853</a>) and its code, available at <a href="https://github.com/GreschAl/MBLlearning">GitHub</a>.</p> <p>It consists of the numerically obtained indicator values for the many-body localization for the Heisenberg model (see preprint for details and background) for various values of the energy density <span class="math-tex">\(\epsilon = 0.05, 0.1, \dots, 0.9, 0.95\)</span> and for chain lengths <span class="math-tex">\(L = 10, 12, 14\)</span>. For each tuple <span class="math-tex">\((\epsilon,L)\)</span>, there exist two files, representing the training and the test set, respectively. Each such file contains various values of the disorder parameter <span class="math-tex">\(h = 0.5, 1, \dots, 14.5, 15\)</span> with <span class="math-tex">\(N = 1000\ (100)\)</span> sampled realizations of the disorder vector for each <span class="math-tex">\(h\)</span> for the training (test) set, followed by the three calculated values of the three indicators.</p> <p>The data is automatically processable by the code provided in the GitHub repository.</p>
Long-lived magnetization in an atomic spin chain tuned to a diabolic point
<p>This is the OpenData Folder for the paper under the same name.</p>
Data underpinning "Localisation and Sub-Diffusive Transport in Quantum Spin Chains With Dilute Disorder"
<p>It is widely believed that many-body localisation in one dimension is fragile and can be easily destroyed by thermal inclusions, however there are still many open questions regarding the stability of the localised phase and under what conditions it breaks down. Here I construct models with dilute disorder, which interpolate between translationally invariant and fully random models, in order to study the breakdown of localisation. This opens up the possibility to controllably increase the density of thermal regions and examine the breakdown of localisation as this density is increased. At strong disorder, the numerical results are consistent with commonly-used diagnostics for localisation even when the concentration of thermalising regions is high. At moderate disorder, I present evidence for slow dynamics and sub-diffusive transport across a large region of the phase diagram, suggestive of a `bad metal' phase. This suggests that dilute disorder may be a useful effective model for studying Griffiths effects in many-body localisation, and perhaps also in a wider class of disordered systems.</p>
Code for `Integrability and complexity in quantum spin chains'
<p>We provide the code and data to reproduce the numerical results presented in the paper https://arxiv.org/abs/2305.00037.</p>
Coherent spin dynamics of solitons in the organic spin chain compounds (o-DMTTF)2X (X = Cl, Br)
<p>Dataset of the paper</p> <p>https://doi.org/10.1103/PhysRevB.100.224414</p> <p> </p>
Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
<p>Transferred magnetization data presented in arxiv.org/abs/2306.09333. </p> <p>The files are named according to the parameters mu, which describes the initial magnetization imbalance, and Delta, which describes the relative strengths of XX+YY and ZZ in the Hamiltonian. Each file contains a dictionary with the following keys (see manuscript for further details):</p> <p>n: Number of qubits<br>mu: Initial magnetization imbalance parameter<br>theta: fSim-gate parameter controlling XX+YY coupling<br>phi: fSim-gate parameter controlling ZZ coupling<br>mean: Mean magnetization transfer as a function of circuit depth<br>d_mean: Uncertainty in mean magnetization transfer<br>moments: n-th central moments for as a function of circuit depth, starting with n=0<br>d_moments: Uncertainty in central moments<br>skewness: Skewness of distribution of transferred magnetization as a function of circuit depth<br>d_skewness: Uncertainty in skewness as a function of circuit depth<br>kurtosis: Kurtosis of distribution of transferred magnetization as a function of circuit depth<br>d_kurtosis: Uncertainty in kurtosis as a function of circuit depth<br>Z: Expectation value of Z for each initial state, qubit and circuit depth<br>nTrials: Number of different initial states investigated</p> <p>In addition, the file `numerical_simulation_results.zip` contains the results of classical statevector simulations of our experiment, up to cycle 18.</p>
Data for "Luther-Emery liquid and dominant singlet superconductivity in the hole-doped Haldane spin-1 chain"
<p>This archive contains the data presented in the paper "Luther-Emery liquid and dominant singlet superconductivity in the hole-doped Haldane spin-1 chain", Phys. Rev. B <strong>110</strong>, 064515 (2024), <a href="https://doi.org/10.1103/PhysRevB.110.064515">https://doi.org/10.1103/PhysRevB.110.064515</a>, with preprint arXiv:2311.13440, <a href="https://doi.org/10.48550/arXiv.2311.13440">https://doi.org/10.48550/arXiv.2311.13440</a></p>
Modular nanomagnet design for spin qubits confined in a linear chain
<p>supporting data</p>
EPR dataset : Superlattice Induced by Charge Order in the Organic Spin Chain (TMTTF)2X (X = SbF6, AsF6, and PF6) Revealed by High-Field Electron Paramagnetic Resonance
<p>Dataset for the reference</p> <p>10.1021/acs.jpclett.8b02070</p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.</p>
Data for Stefanescu et. al. "Robust implicit quantum control of interacting spin chains"
<p>Codes and data for the paper "Robust implicit quantum control of interacting spin chains."</p>
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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.