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18 results for “Quantum processor”
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Data for "Qubit vitrification and entanglement criticality on a quantum processor"
<p>Data includes the filtered measurement results from the IBM Q devices, as well as the code to generate the figures. See the README file for more information regarding the data.</p>
Data for "Realizing topologically ordered states on a quantum processor"
<p>Data and code used in "Realizing topologically ordered states on a quantum processor," Satzinger et al. (2021), preprint at https://arxiv.org/abs/2104.01180</p>
Detecting and Tracking Drift in Quantum Information Processors
<p>This is supplemental data and code for:<strong> </strong>T. Proctor et al, <a href="https://www.nature.com/articles/s41467-020-19074-4"><em>Detecting and tracking drift in quantum information processors</em></a>, Nat. Comm. 11, 5396 (2020).</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does <em>not</em> work on the latest full release of pyGSTi (0.9.9). It is anticipated that it will work with the next full release of pyGSTi (0.9.10).</p> <p>Below is a basic guide to navigating this SI:</p> <p><strong>Time-resolved Ramsey tomography on experimental data.</strong></p> <p><em>Directory: ramsey/experiment</em></p> <p>This folder contains the data and analysis code for the time-resolved Ramsey experiment, the results of which are presented in Figure 1 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis.</p> <p><strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong></p> <p><em>Directory: rb/simulation</em></p> <p>This folder contains the data and analysis code for the simulation of time-resolved RB, the results of which are presented in Figure 2 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis on the simulated data, and which can be used to run new simulations with the same noise model.</p> <p><strong>Time-resolved gate set tomography (GST) on simulated data.</strong></p> <p><em>Directory: gst/simulation</em></p> <p>This contains the data and analysis code for the simulation of time-resolved GST, the results of which are presented in Figure 2 of the paper. The raw simulated data is contained in the "data" folder. All the code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>create_simulated_data.py : this generates the simulated data. This was run using MPI on 20 cores.</li> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul> <p><strong>Time-resolved gate set tomography (GST) on experimental data.</strong></p> <p><em>Directory: gst/experiments</em></p> <p>This folder contains the data and analysis code for the two time-resolved GST experiments, the results of which are presented in Figure 3 of the paper. The raw data is contained in the two folders "data/1" and "data/2", corresponding to the first and second experiment, respectively. All analysis code is contained in the "analysis" folder. This contains the following code files:</p> <ul> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores.</li> <li>trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul>
Blueprint for a Molecular-Spin Quantum Processor
<p>Data supporting the original figures 5, 6, 7, 8, 9,10 and 11 of the related publication Phys. Rev. Applied 19, 064060 (2023).</p> <p>Preprint on https://arxiv.org/abs/2305.01688</p>
A universal qudit quantum processor with trapped ions
<p>Source data underlying the graphical representations used in the figures.</p>
Supporting data for 'A shuttling-based two-qubit logic gate for linking distant silicon quantum processors'
<p>Data supporting for paper 'A shuttling-based two-qubit logic gate for linking distant silicon quantum processors'.</p> <p>All the data are stored in the HDF5 format that can be conveniently loaded by the xarray Python package.</p>
Small-world complex network generation on a digital quantum processor
<p>Quantum cellular automata (QCA) evolve qubits in a quantum circuit depending only on the states of their neighborhoods and model how rich physical complexity can emerge from a simple set of underlying dynamical rules. The inability of classical computers to simulate large quantum systems hinders the elucidation of quantum cellular automata, but quantum computers offer an ideal simulation platform. Here, we experimentally realize QCA on a digital quantum processor, simulating a one-dimensional Goldilocks rule on chains of up to 23 superconducting qubits. We calculate calibrated and error-mitigated population dynamics and complex network measures, which indicate the formation of small-world mutual information networks. These networks decohere at fixed circuit depth independent of system size, the largest of which corresponding to 1,056 two-qubit gates. Such computations may enable the employment of QCA in applications like the simulation of strongly-correlated matter or beyond-classical computational demonstrations.</p>
High-rate generation and state tomography of non-gaussian quantum states for ultra-fast clock frequency quantum processors
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Small-world complex network generation on a digital quantum processor
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Quantum supremacy using a programmable superconducting processor
<p>The tantalizing promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here, we report using a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 ∼ 10^16. Measurements from repeated experiments sample the corresponding probability distribution, which we verify using classical simulations. While our processor takes about 200 seconds to sample one instance of a quantum circuit 1 million times, a state-of-the-art supercomputer would require approximately 10,000 years to perform the equivalent task. This dramatic speedup relative to all known classical algorithms provides an experimental realization of quantum supremacy on a computational task and heralds the advent of a much-anticipated computing paradigm.</p>
Source Data for figures in "Emergence of steady quantum transport in a superconducting processor"
<p>Experimental and numerical data for figures in "Emergence of steady quantum transport in a superconducting processor".</p>
Supporting data for "A programmable two-qubit quantum processor in silicon"
<p>Raw data, analysis scripts, and simulation files used to produce the figures for "A programmable two-qubit quantum processor in silicon", ArXiv:708.04214 (preprint) and <a href="http://dx.doi.org/">http://dx.doi.org/</a>10.1038/nature25766 (publication).</p> <p>This dataset consists of four zipped folders that contain the following: (1) analysis_rawdata_final.zip - raw data and Matlab files used to plot and analyse the data. (2) DM_solver-master.zip - Software used to calculate the time evolution of a density matrix given a time dependent Hamiltonian. A manual is included. (3) Simulations_double_dot-paper.zip - simulation scripts and results for the time evolution of the two qubit system during the quantum algorithms. (4) The raw single-shot data for the state tomography experiments. </p> <p> </p>
Native qudit entanglement in a trapped ion quantum processor
<p>Source data underlying the graphical representations used in the figures.</p>
Quantum supremacy using a programmable superconducting processor
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Quantum information phases in space-time: measurement-induced entanglement and teleportation on a noisy quantum processor
<p>Data for the manuscript at https://arxiv.org/abs/2303.04792</p>
Supporting data for 'Universal control of a six qubit quantum processor in silicon'
<p>Supporting data that hosts all the data for the raw plots</p>
Experimental data for "Stark Many-Body Localization on a Superconducting Quantum Processor" at 5.15 GHz
<p>Experimental results with the Stark potential centered around 5.15 GHz. </p>
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