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3 results for “quantum chaos”
Dataset for "Binary-coupling sparse Sachdev-Ye-Kitaev model: an improved model of quantum chaos and holography"
<p>Spectral data for binary, unary and Gaussian-coupling, sparse and dense Sachdev-Ye-Kitaev model used for the publication.</p> <p>Directories are named by the number of Majorana fermions, and the coupling type (binary, Gaussian, unary) and the number of non-zero couplings are indicated in the file name.</p> <p>In each file, the computed eigenenergies are given in little-endian double-precision floating numbers, eight bytes for each eigenstate, in ascending order for each parity sector (even followed by odd) for each generated sample.</p> <p>Except for N = 32 and 34 where a single sample is given, each file contains 2^{24-(N/2)} samples, 131 072 for N = 14, 65 536 for N = 16, ..., and 512 for N = 30.</p>
Data and code for "Quantum chaos on edge" (matfiles, a matlab script, and figures)
<p>The data files and matlab script to generate Figure 5,7,8 of the manuscript "Quantum chaos on edge" are uploaded.</p> <ul> <li>In 'script' folder, there is a single matlab file to generate Fig 5,7,8. </li> <li>In 'raw_data' folder, there are mat-files to be processed by the script file.</li> <li>In 'figures' folder, the generated figures are included.</li> </ul>
Data underpinning "Classical Chaos in Quantum Computers"
<div> <div> <div> <div> <p>We provide the data used to produce the figures shown in our publication "Classical Chaos in Quantum Computer" and a Jupyter Notebook to reproduce all figures.</p> <h3>Abstract: </h3> <div> <div> <div> <div> <p>The development of quantum computing hardware is facing the challenge that current-day quantum processors, comprising 50-100 qubits, already operate outside the range of quantum simulation on silicon computers. In this paper, we demonstrate that the simulation of classical limits can be a potent diagnostic tool potentially mitigating this problem. As a testbed for our approach, we consider the transmon qubit processor, a computing platform in which the coupling of large numbers of nonlinear quantum oscillators may trigger destabilizing chaotic resonances. We find that classical and quantum simulations lead to similar stability metrics (classical Lyapunov exponents vs. quantum wave function participation ratios) in systems with O(10) transmons. However, the big advantage of classical simulation is that it can be pushed to large systems comprising up to thousands of qubits. We exhibit the utility of this classical toolbox by simulating all current IBM transmon chips, including the recently announced 433-qubit processor of the Osprey generation, as well as future devices with 1,121 qubits (Condor generation). For realistic system parameters, we find a systematic increase of Lyapunov exponents in system size, suggesting that larger layouts require added efforts in information protection.</p> </div> </div> </div> </div> </div> </div> </div> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.