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187 results for “Phase Transition”
Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 2 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Data for "Phase transition in Random Circuit Sampling"
<p><strong>Purpose</strong></p> <p>This dataset defines the Random Quantum Circuits (RQCs) used in our paper "Phase transition in Random Circuit Sampling" and lists the bitstrings observed in the experimental executions of the circuits on the Sycamore processor. See [1] for more details about the experiment. This data upload is modeled after that of [5].</p> <p><strong>Background</strong></p> <p><strong>Circuit parameters</strong></p> <p>RQCs posted here are uniquely identified<br>using the following parameters:</p> <ul> <li> `n`: number of qubits (69, 70),</li> <li> `m`: number of cycles (04, 06, 08, ..., 28, 30),</li> <li> `s`: seed for the pseudo-random number generator (000, 001, ..., 595),</li> <li> `patches`: the number of patches,</li> <li> `p`: sequence of coupler activation patterns (`ABCD`, `ABCDCDAB`),</li> <li> `num_sq`: the number of distinct single-qubit gates (3, 8),</li> <li> the date on which the data was collected, included in yymmdd format in the filename, and</li> <li> `phase_match`: included in the file name if phase matching was performed.</li> </ul> <p>See Figure S25 in [2] and Figure 3 of [1] for the coupler activation patterns and Figure 4 of [1] for illustrations of the patches. Also see code snippets below for visualizing the patches and activation patterns from the provided circuits.<br>When `num_sq` is 8, the single-qubit gates are chosen randomly from \(Z^p X^{1/2} Z^{-p}\), with \(p \in \{-1, -3/4, -1/2, -1/4, 0, 1/4, 1/2, 3/4 \}\), whereas when `num_sq` is 3, they are chosen randomly from among \(\sqrt X\), \(\sqrt Y\), and \(\sqrt W\). Phase matching is described in Appendix C.1 of [1]. Note that circuits which share the same seed `s` share the same initial gate sequence.</p> <p><strong>Content description</strong></p> <p>For each RQC there are four files in the dataset:<br> * original RQC specification in QSIM format, named `circuit_\*.qsim`,<br> * derived RQC specificaton as python code using cirq, named `circuit_\*.py`,<br> * derived RQC specification in QASM format, named `circuit_\*.qasm`,<br> * bitstrings observed in experiments, named `measurements_\*.txt`.</p> <p>The asterisk \* in the names above stands for a string specifying the parameters identifying a RQC. For example,<br>`circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.qasm` contains the definition of the 70-qubit, 24-cycle RQC with PRNG seed 0, 3 patches, a simplifiable sequence of coupler activation patterns (i.e. ABCD), and 8 distinct single-qubit gates, taken on October 14, 2022, with phase matching, in the QASM format.</p> <p>Files are grouped by parameters `n`, `m`, and `patches` and into compressed tarballs. For example, tarball `n69_m04_patches2.tar.gz` contains all RQCs and measurement files for circuits with 69 qubits, 4 cycles, and 2 patches.</p> <p><strong>Circuit file formats</strong></p> <p><strong>QSIM format</strong></p> <p>First line specifies the number of qubits n. Each subsequent line specifies a single gate and consists of moment number, gate name and one or two qubits as a number in 0..n-1 optionally followed by gate parameters.</p> <p>The circuits use the following gates:<br> * `x_1_2`: parameter-free, single-qubit pi/2 rotation around the X axis of the Bloch sphere, see equation (45) in section VII of [2],<br> * `y_1_2`: parameter-free, single-qubit pi/2 rotation around the Y axis of the Bloch sphere, see equation (46) in section VII of [2],<br> * `hz_1_2`: parameter-free, single-qubit pi/2 rotation around the X+Y axis of the Bloch sphere, see equation (47) in section VII of [2],<br> * `rz`: single-qubit rotation around the Z axis of the Bloch sphere through the angle specified in radians by the gate's sole parameter,<br> * `fsim`: two-qubit gate corresponding to the composition of the iSWAP and CPHASE gates and taking two parameters in radians: theta (the negative iSWAP angle) and phi (the CPHASE angle), see equation (48) in section VII of [2].</p> <p>Note that the two-qubit gates executed in our experiments on Sycamore belong to the five-parameter family of two-qubit gates that preserve the number of 0 and 1 states of the qubits. Each such gate can be decomposed into one fsim gate and four rz gates. Therefore, one cycle consisting of one application of<br>single-qubit gates and one application of two-qubit gates is represented in the file using four moments. The first moment contains `x_1_2`, `y_1_2` and `hz_1_2` gates. The other three moments use `rz` and `fsim` gates to describe the two-qubit gates used in the experiments. See section VII in [2] for more details about the Sycamore gates and their decomposition.</p> <p>Qubits are specified as numbers in 0..n-1 and hence do not directly indicate qubit location on the device.</p> <p><strong>Python/cirq format</strong></p> <p>Each python file defines two variables: QUBIT_ORDER and CIRCUIT. The former is a python list object containing `cirq.GridQubit` objects initialized with the<br>row and column of each qubit on the device. The latter is a `cirq.Circuit` object initialized with all gate operations contained in the circuit. The files have been tested using cirq version 1.2.0.dev20230613162638.</p> <p>The following code snippet illustrates how one can use cirq and our circuit definitions to compute output state amplitudes:</p> <pre><code>$ python -i circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.py >>> cirq.final_wavefunction(CIRCUIT, qubit_order=QUBIT_ORDER) array([ 0.00263724+0.00337646j, 0.0009332 +0.00111853j, -0.0007809 +0.00386362j, ..., 0.00574739-0.00027827j, -0.00254766+0.00299345j, 0.00396056+0.00312335j], dtype=complex64)</code></pre> <p> </p> <p>See [3] for more details about cirq.</p> <p><strong>QASM format</strong></p> <p>Each QASM file has been generated using cirq and specifies the RQC decomposed into CNOT and single-qubit gates. The files have been generated using cirq.</p> <p>See [4] for more details about the format.</p> <p><strong>Measurements file format</strong></p> <p>Each line contains the bitstring obtained in a single execution of the RQC on Sycamore. The first, left-most position corresponds to the qubit 0 in QSIM format and the first qubit in the `QUBIT_ORDER` list in the python/cirq files.</p> <p><strong>To visualize the patches</strong></p> <p>After loading `CIRCUIT` from the appropriate `.py` file, the following code snippet can be used to visualize the patches:</p> <pre><code>import cirq import matplotlib.pyplot as plt pairs = set() for moment in CIRCUIT: for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add(tuple(sorted(q))) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax)</code></pre> <p> </p> <p><strong>Visualize the activation patterns</strong></p> <p>The activation pattern sequence can also be visualized in a similar manner. After loading `CIRCUIT` from the appropriate `.py` pyle, the following code snippet can be used to visualize the activation patterns:</p> <pre><code>import cirq import matplotlib.pyplot as plt def has_two_qubit_gates(moment): has = False for op in moment.operations: if len(op.qubits) > 1: has = True break return has def plot_pairs(moment): pairs = set() for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add( tuple(sorted(q)) ) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax) return ax moments = [_ for _ in CIRCUIT if has_two_qubit_gates(_)] moment_to_visualize = moments[0] # iterate through this manually plot_pairs(moment_to_visualize)</code></pre> <p> </p> <p><strong>Content listing</strong></p> <p>The dataset includes the following tarball files:</p> <pre><code>n69_m04_patches2.tar.gz (80 files) n69_m04_patches3.tar.gz (80 files) n69_m06_patches2.tar.gz (80 files) n69_m06_patches3.tar.gz (80 files) n69_m08_patches2.tar.gz (80 files) n69_m08_patches3.tar.gz (80 files) n69_m10_patches2.tar.gz (80 files) n69_m10_patches3.tar.gz (80 files) n69_m12_patches2.tar.gz (80 files) n69_m12_patches3.tar.gz (80 files) n69_m14_patches2.tar.gz (80 files) n69_m14_patches3.tar.gz (80 files) n69_m16_patches2.tar.gz (80 files) n69_m16_patches3.tar.gz (80 files) n69_m18_patches2.tar.gz (80 files) n69_m18_patches3.tar.gz (80 files) n69_m20_patches2.tar.gz (80 files) n69_m20_patches3.tar.gz (80 files) n69_m22_patches2.tar.gz (80 files) n69_m22_patches3.tar.gz (80 files) n69_m24_patches1.tar.gz (4 files) n69_m24_patches2.tar.gz (80 files) n69_m24_patches3.tar.gz (80 files) n69_m26_patches2.tar.gz (80 files) n69_m26_patches3.tar.gz (80 files) n69_m28_patches2.tar.gz (80 files) n69_m28_patches3.tar.gz (80 files) n69_m30_patches2.tar.gz (80 files) n69_m30_patches3.tar.gz (80 files) n70_m16_patches2.tar.gz (960 files) n70_m16_patches3.tar.gz (1040 files) n70_m16_patches9.tar.gz (160 files) n70_m18_patches2.tar.gz (960 files) n70_m18_patches3.tar.gz (1040 files) n70_m18_patches9.tar.gz (160 files) n70_m20_patches2.tar.gz (960 files) n70_m20_patches3.tar.gz (1040 files) n70_m20_patches9.tar.gz (160 files) n70_m22_patches2.tar.gz (960 files) n70_m22_patches3.tar.gz (1040 files) n70_m22_patches9.tar.gz (160 files) n70_m24_patches1.tar.gz (56 files) n70_m24_patches2.tar.gz (960 files) n70_m24_patches3.tar.gz (1040 files) n70_m24_patches9.tar.gz (160 files) n70_m26_patches1.tar.gz (4 files) n70_m26_patches2.tar.gz (880 files) n70_m26_patches3.tar.gz (960 files) n70_m26_patches9.tar.gz (160 files)<br>67-qubit data (described below)</code></pre> <p> </p> <p>Additionally, the file <code>Figures 2 and 3 data.zip</code> contains processed data (9 files in Pickle format) that are shown in Figures 2 and 3 of the paper.</p> <p>The 67-qubit data files, <code>n67_full.zip</code>, <code>n67_patches2.zip</code>, and <code>n67_patches3.zip</code> are formatted differently. The circuits are savd in .json format and can be loaded with `cirq.read_json()`. The data files are in .npy format and can be opened with numpy.load(). The number of cycles (`m` elsewhere) is indicated after `d` in the filename. The contents of these zip files can be previewed with the "preview" button.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>[1] Google AI Quantum and collaborators, "Phase transition in Random Circuit Sampling". <a href="https://arxiv.org/abs/2304.11119">arXiv:2304.11119</a></p> <p>[2] Google AI Quantum and collaborators, Supplementary information for “Quantum supremacy using a programmable superconducting processor”. <a href="https://arxiv.org/abs/1910.11333">arXiv:1910.11333</a></p> <p>[3] Cirq: A Python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits, <a href="https://github.com/quantumlib/Cirq">https://github.com/quantumlib/Cirq</a>.</p> <p>[4] Cross, Andrew W.; Bishop, Lev S.; Smolin, John A.; Gambetta, Jay M. "Open Quantum Assembly Language", <a href="https://arxiv.org/abs/1707.03429">arXiv:1707.03429</a></p> <p>[5] Martinis, John M. et al. (2022), "Quantum supremacy using a programmable superconducting processor", Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.k6t1rj8">https://doi.org/10.5061/dryad.k6t1rj8</a></p>
Unveiling a Common Phase Transition Pathway of High-Density Amorphous Ices through Time-Resolved X-ray Scattering
<p>XFEL Pump-Probe experiments on VHDA.</p> <p><strong><span>DOI: 10.1063/5.0216904</span></strong></p> <p>The data that support the findings of this study are available here.</p> <p> </p>
Data for 'Interlayer Interaction induced Plateau Phase Transition in Quantum Anomalous Hall Insulators'
<p>The MS data file contains the data for the figures in the manuscript. The SI data file contains the data for the figures in the supplementary materials. The txt files are raw data, and the opj files are the processed Origin data.</p>
Data for 'Interlayer Interaction induced Plateau Phase Transition in Quantum Anomalous Hall Insulators'
<p>The MS data file contains the data for the figures in the manuscript. The SI data file contains the data for the figures in the supplementary materials. The txt files are raw data, and the opj files are the processed Origin data.</p>
Simulation code and results for "Critical behavior and the Kibble-Zurek mechanism in a musical phase transition"
<p>Matlab simulation code and results for "Critical behavior and the Kibble-Zurek mechanism in a musical phase transition."</p> <p>See readme.txt for descriptions of all files.</p>
Nanofocused X-ray beam drives a hydriding phase transition in Pd nanoparticles under electrochemical control
<p>For analysis, see</p> <p>https://github.com/maxiv-science/20200372-analysis</p>
"Transition to the Haldane phase driven by electron-electron correlations"
<p>Dataset (Fig.1-Fig.5) for "Transition to the Haldane phase driven by electron-electron correlations"</p>
Data and codes for "Dynamical Quantum Phase Transitions Following Double Quenches"
<p>Codes for producing figures and data in the paper "Dynamical Quantum Phase Transitions Following Double Quenches" (<a href="https://arxiv.org/abs/2302.01661">https://arxiv.org/abs/2302.01661</a>). Included are the data for the return rates and python codes for producing this data and the figures. A Mathematica file is included which was used for analysing the conditions for DQPTs in the paper, and for producing the remaining figures.</p>
Dataset for large-scale experiment of block-grain phase transition
<p>Dataset related to the manuscript “Block-grain phase transition in rock avalanches: insights from large-scale experiments”, submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The dataset provides the raw and processed data for the large-scale experiments of block-grain phase transition due to fragmentation, including parameters reflecting the dynamics, fragmentation, deposit.</p> <p><strong>S1_data_velocity</strong> provides the average velocity at the front, middle and tail of the moving mass determined by the laser ranging sensors under different experimental configurations.</p> <p><strong>S2_data_fragmentation</strong> provides the fragment size distribution and relative breakage ratio.</p> <p><strong>S3_data_deposit </strong>provides the parameters of deposit, including the runout distances, friction coefficients.</p> <p><strong>S4_data_seismic </strong>provides the parameters used to characterize the seismic signal excited by the moving mass, including the envelope curve, marginal spectrum and seismic energy.</p>
Phase I Study With Sorafenib in Addition to Vinflunine in Metastatic Transitional Cell Carcinoma of the Urothelial Tract
ClinicalTrials.gov study NCT01844947. IPD Sharing: NO. Countries: 2. Publications: 1.
A Phase I Trial Using Suramin to Treat Superficial Transitional Cell Carcinoma of the Bladder
ClinicalTrials.gov study NCT00001381. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Phase III Trial of Vinflunine Plus Best Supportive Care vs. Best Supportive Care in Patients With Transitional Cell Carcinoma (TCC) of the Urothelial Tract
ClinicalTrials.gov study NCT00315237. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Phase I, Randomized, Placebo-controlled, Double Blind, Repeat Dose Escalation Study to Evaluate the Safety, Tolerability, Pharmacokinetics, Gastrointestinal Transit Time and Pharmacodynamic Biomarke
ClinicalTrials.gov study NCT02798991. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Processed data file for article "Implications of the iron oxide phase transition on the interiors of rocky exoplanets"
Open the record for dataset details and reuse information.
Coupled magnetic and structural phase transitions in antiferromagnetic polar metal Pb2CoOsO6 under pressure
<p>This data repo. includes the optimized structures of Pb2CoOsO6 under pressures up to 9 GPa. The structures were fully optimized in the DFT calculations using VASP code. </p>
Data from: Thermal evolution of the crystal structure and phase transitions of KNbO3
The thermal evolution of the crystal structure and phase transitions of KNbO3 were investigated by high-temperature powder X-ray diffraction and Rietveld refinement of the diffraction data. Two phase transitions from orthorhombic (Amm2) to tetragonal (P4mm) and from tetragonal to cubic (Pm3 ̅m) were confirmed, both on heating and cooling. Both phase transtions are first order based on the observed hysteresis. The mixed displacive and order-disorder nature of the tetragonal to cubic transition is argued based on symmetry and apparent divergence of the atomic positions from pseudo-cubic values. The transition between the orthorhombic and tetragonal phase show no temperature-dependence for atomic positions and only thermal expansion of the unit cell parameters and is thus discussed in relation to a lattice dynamical instability.
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