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5 results for “shot noise”

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zenodo44/100

Data for "Challenges of variational quantum optimization with measurement shot noise"

<p>In this repository, we save all the data used in the article <em>"Challenges of variational quantum optimization with measurement shot noise"</em> <a href="https://doi.org/10.1103/PhysRevA.109.032408" target="_blank" rel="noopener">Phys. Rev. A 109, 032408</a>.</p> <p>For reproducing the same figure as in the article, we provide an interactive notebook 'paper-figure.ipynb' and its HTML version.&nbsp;</p> <p><strong>Data</strong></p> <p>Unzip with</p> <pre><code>tar -xvf data.tar.gz</code></pre> <p>You will find ten&nbsp;data&nbsp;files:</p> <ul> <li>&nbsp;`dataferro.csv` Ferromagnetic model both for VQE and QAOA with random parameters initialization;</li> <li>`datanoise.csv`Ferromagnetic model for VQE with&nbsp;random parameters initialization and circuit noise;</li> <li>`data_spinglass.csv` Disordered model both for VQE and QAOA with random parameters initialization;</li> <li>`data_linopt.csv` Ferromagnetic model for QAOA with annealing-like parameters initialization;</li> <li>`data_lininit_rand.csv` Disordered model for QAOA with annealing-like parameters initialization;</li> <li>`data_spinglass_iter1.csv` Disordered model for QAOA, no optimization and random parameters initialization;</li> <li>`data_opt16shots.csv` Ferromagnetic model for QAOA, no optimization and annealing-like parameters initialization;</li> <li>`datagrad.csv` Ferromagnetic model for VQE with gradient-based optimization;</li> <li>`datagrad_eps05.csv` Ferromagnetic model for QAOA with gradient-based optimization;</li> <li>`grad_epsilon.npz` Ferromagnetic model for QAOA with gradient-based optimization, varying \(\varepsilon\)&nbsp;in the finite differences formula.</li> </ul> <p>Most of the data are CSV sheets, where you can find the aggregate results of the simulations described&nbsp;in our&nbsp;article.&nbsp;</p> <p><strong>Abstract</strong></p> <p>Quantum enhanced optimization of classical cost functions is a central theme of quantum computing due to its high potential value in science and technology.<br>The variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are popular variational approaches that are considered the most viable solutions in the noisy-intermediate scale quantum (NISQ) era.<br>Here, we study the scaling of the quantum resources, defined as the required number of circuit repetitions, to reach a fixed success probability as the problem size increases, focusing on the role played by measurement shot noise, which is unavoidable in realistic implementations.<br>Simple and reproducible problem instances are addressed, namely, the ferromagnetic and disordered Ising chains.&nbsp;<br>Our results show that:&nbsp;</p> <ol> <li>VQE with the standard heuristic ansatz scales comparably to direct brute-force search when energy-based optimizers are employed. The performance improves at most quadratically using a gradient-based optimizer;</li> <li>When the parameters are optimized from random guesses, also the scaling of QAOA implies problematically long absolute runtimes for large problem sizes;</li> <li>QAOA becomes practical when supplemented with a physically-inspired initialization of the parameters.</li> </ol> <p>Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop, but focus on smart parameters initialization.</p>

opencc-by-4.0Aug 2023View details →
zenodo40/100

Raw data for: Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy

<p>The repository shares the data used to produce the figures in the paper "Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy", submitted 2024 in Optics Express.&nbsp;</p> <p>The upload includes for each Figure the raw and processed self-referenced transient normalized transmission data, (T_on-T_off) / T_off, from which all results and statistics are deduced.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks

<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>

opencc-by-4.0Jun 2024View details →
zenodo28/100

2M++ shot noise

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →
zenodo12/100

Imaging of Non-local Hot Electron Energy Dissipation via Shot Noise: Data deposit

<p>This content of this data deposit&nbsp;is the following:</p> <p>Archives&nbsp;&lsquo;Data (Fig.1-3, and Fig.S1-S11)&rsquo;: Raw data and executable files related to figures 1 to 3, and figures S1 to S11 of the publication</p>

restrictedMar 2018View details →

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