PyCCE: A Python Package for Cluster Correlation Expansion Simulations of Spin Qubit Dynamics

2021 ◽  
pp. 2100254
Author(s):  
Mykyta Onizhuk ◽  
Giulia Galli
Keyword(s):  
Spin Qubit ◽  
Cluster Correlation ◽  
Python Package

Engineering Electronic Structure to Protect Phase Memory in Molecular Qubits by Minimising Orbital Angular Momentum

2018 ◽  
Author(s):  
Ana Maria Ariciu ◽  
David H. Woen ◽  
Daniel N. Huh ◽  
Lydia Nodaraki ◽  
Andreas Kostopoulos ◽  
...  
Keyword(s):  
Electron Spin ◽  
Quantum Coherence ◽  
Quantum Information Processing ◽  
Pulse Sequences ◽  
Grover Algorithm ◽  
Ligand Shell ◽  
Information Strategies ◽  
Spin Qubit ◽  
Molecular Spin ◽  
The Impact

Using electron spins within molecules for quantum information processing (QIP) was first proposed by Leuenberger and Loss (1), who showed how the Grover algorithm could be mapped onto a Mn12 cage (2). Since then several groups have examined two-level (S = ½) molecular spin systems as possible qubits (3-12). There has also been a report of the implementation of the Grover algorithm in a four-level molecular qudit (13). A major challenge is to protect the spin qubit from noise that causes loss of phase information; strategies to minimize the impact of noise on qubits can be categorized as corrective, reductive, or protective. Corrective approaches allow noise and correct for its impact on the qubit using advanced microwave pulse sequences (3). Reductive approaches reduce the noise by minimising the number of nearby nuclear spins (7-11), and increasing the rigidity of molecules to minimise the effect of vibrations (which can cause a fluctuating magnetic field via spin-orbit coupling) (9,11); this is essentially engineering the ligand shell surrounding the electron spin. A protective approach would seek to make the qubit less sensitive to noise: an example of the protective approach is the use of clock transitions to render spin states immune to magnetic fields at first order (12). Here we present a further protective method that would complement reductive and corrective approaches to enhancing quantum coherence in molecular qubits. The target is a molecular spin qubit with an effective 2S ground state: we achieve this with a family of divalent rare-earth molecules that have negligible magnetic anisotropy such that the isotropic nature of the electron spin renders the qubit markedly less sensitive to magnetic noise, allowing coherent spin manipulations even at room temperature. If combined with the other strategies, we believe this could lead to molecular qubits with substantial advantages over competing qubit proposals.<br>

Development and psychometric properties of the female adolescents’ sexual reproductive self-care scale

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Zainab Alimoradi ◽  
Nourossadat Kariman ◽  
Fazlollah Ahmadi ◽  
Masoumeh Simbar ◽  
Hamid AlaviMajd
Keyword(s):  
Psychometric Properties ◽  
Content Validity ◽  
Research Team ◽  
Self Care ◽  
Female Adolescents ◽  
Methodological Study ◽  
Initial Questionnaire ◽  
Depth Interviews ◽  
Qualitative Phase ◽  
Cluster Correlation

Abstract Introduction The aim of this study was to design and evaluate the psychometric properties of an instrument for understanding female adolescents’ reproductive and sexual self-care behaviors. Methods A methodological study was conducted. In the qualitative phase, individual in-depth interviews were performed to develop the initial questionnaire. In the quantitative part, the psychometric properties of the questionnaire were evaluated. Findings The initial questionnaire with 128 items was reviewed by the research team and taking into account the cut-off point 1.5 for the item impact and 0.62 for the content validity ratio (CVR), the number of questions fell to 82 items. S-CVR and S-content validity index (CVI) rations were 0.83 and 0.91, respectively. Exploratory factor analysis led to 74 items in seven dimensions. The alpha Cronbach’s coefficient for the whole questionnaire was 0.895 and the intra-cluster correlation coefficient was 0.91. Conclusion The questionnaire developed in this study is reliable and valid for assessing female adolescents’ sexual and reproductive self-care.

scholarly journals GriSPy: A Python package for fixed-radius nearest neighbors search

2020 ◽  
pp. 100443
Author(s):  
M. Chalela ◽  
E. Sillero ◽  
L. Pereyra ◽  
M.A. Garcia ◽  
J.B. Cabral ◽  
...  
Keyword(s):  
Nearest Neighbors ◽  
Fixed Radius ◽  
Python Package

scholarly journals Cluster correlation expansion for studying decoherence of clock transitions in spin baths

2020 ◽  
Vol 102 (24) ◽  
Author(s):  
Geng-Li Zhang ◽  
Wen-Long Ma ◽  
Ren-Bao Liu
Keyword(s):  
Cluster Correlation

scholarly journals BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Colin Farrell ◽  
Michael Thompson ◽  
Anela Tosevska ◽  
Adewale Oyetunde ◽  
Matteo Pellegrini
Keyword(s):  
Data Aggregation ◽  
Bisulfite Sequencing ◽  
Low Complexity ◽  
Sequencing Analysis ◽  
Command Line ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data ◽  
Analysis Platform ◽  
Python Package ◽  
Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Robust and fast post-processing of single-shot spin qubit detection events with a neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom Struck ◽  
Javed Lindner ◽  
Arne Hollmann ◽  
Floyd Schauer ◽  
Andreas Schmidbauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Rabi Oscillation ◽  
Detection Methods ◽  
Measured Signal ◽  
Single Shot ◽  
Post Processing ◽  
Readout Signal ◽  
Spin Qubit

AbstractEstablishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.

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