Showing papers on "NQS published in 2021"

Derivatization of amphetamine to allow its electrochemical detection in illicit drug seizures

Abstract: Amphetamine (AMP) is posing critical issues in our society being one of the most encountered drugs-of-abuse in the current illicit market. The continuous drug production in Europe urges the development of new tools for the rapid on-site determination of illicit drugs such as AMP. However, the direct electrochemical detection of AMP is a challenge because the molecule is non-electroactive at the potential window of conventional graphite SPEs. For this reason, a derivatization step is needed to convert the primary amine into an electroactive oxidizable group. Herein, the rapid electrochemical detection of AMP in seized samples based on the derivatization by 1,2-naphthoquinone-4-sulfonate (NQS) is presented by using square wave voltammetry (SWV) at graphite screen-printed electrodes (SPEs). First, a detailed optimization of the key parameters and the analytical performance is provided. The method showed a sensitivity of 7.9 μA mM−1 within a linear range from 50 to 500 μM, a limit of detection of 22.2 μM, and excellent reproducibility (RSD = 4.3 %, n = 5 at 500 μM). Subsequently, the effect of NQS on common cutting agents for the selective detection of AMP is addressed. The comparison of the method with drugs-of-abuse containing secondary and tertiary amines confirms the selectivity of the method. Finally, the concept is applied to quantify AMP in 20 seized samples provided by forensic laboratories, exhibiting an accuracy of 97.3 ± 10.5 %. Overall, the fast analysis of samples with the electrochemical profiling of derivatized AMP exhibits a straightforward on-site screening aiming to facilitate the tasks of law enforcement agents in the field.

Naphthoquinones and Their Derivatives: Emerging Trends in Combating Microbial Pathogens

Abstract: In the current era, an ever-emerging threat of multidrug-resistant (MDR) pathogens pose serious health challenges to mankind. Researchers are uninterruptedly putting their efforts to design and develop alternative, innovative strategies to tackle the antibiotic resistance displayed by varied pathogens. Among several naturally derived and chemically synthesized compounds, quinones have achieved a distinct position to defeat microbial pathogens. This review unleashes the structural diversity and promising biological activities of naphthoquinones (NQs) and their derivatives documented in the past two decades. Further, realizing their functional potentialities, researchers were encouraged to approach NQs as lead molecules. We have retrieved information that is dedicated on biological applications (antibacterial, antifungal, antiparasitic) of NQs. The multiple roles of NQs offer them a promising armory to combat microbial pathogens including MDR and the ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) group. In bacteria, NQs may exhibit their function in the following ways (1) plasmid curing, (2) inhibiting efflux pumps (EPs), (3) generating reactive oxygen species (ROS), (4) the inhibition of topoisomerase activity. Sparse but meticulous literature suggests the mechanistic roles of NQs. We have highlighted the possible mechanisms of NQs and how the targeted drug synthesis can be achieved via molecular docking analysis. This bioinformatics-oriented approach will explicitly lead to the development of effective and most potent drugs against targeted pathogens. The mechanistic approaches of emerging molecules like NQs might prove a milestone to defeat the battle against microbial pathogens.

Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations

Abstract: Neural-network quantum state (NQS) has attracted significant interests as a powerful wave-function ansatz to model quantum phenomena. In particular, a variant of NQS based on the restricted Boltzmann machine (RBM) has been adapted to model the ground state of spin lattices and the electronic structures of small molecules in quantum devices. Despite these progresses, significant challenges remain with the RBM-NQS-based quantum simulations. In this work, we present a state-preparation protocol to generate a specific set of complex-valued RBM-NQS, which we name the unitary-coupled RBM-NQS, in quantum circuits. Our proposal expands the applicability of NQS as prior works deal exclusively with real-valued RBM-NQS for quantum algorithms. With this scheme, we achieve (1) modeling complex-valued wave functions, (2) using as few as one ancilla qubit to simulate M hidden spins in an RBM architecture, and (3) avoiding post-selections to improve scalability.

A novel electrochemical sensor based on EDTA-NQS/GC for simultaneous determination of heavy metals

Abstract: In this paper, we present a simple, sensitive and low-cost electrochemical sensor based on an organic layer of EDTA-NQS (1,2-napthaquinone-4 sulphonic acid sodium salt) formed on the surface of a glassy carbon electrode (GC) for the first time. The electrochemical characteristics of the newly fabricated EDTA-NQS/GC sensor were investigated by cyclic voltammetry (CV) and linear sweep voltammetry (LSV) techniques. Experimental results confirmed that the new sensor showed superior analytical performance for the simultaneous detection of heavy metals trace in well separated anodic peaks even in the existence of some interfering species. The peak potentials for Cd2+, Pb2+, and Cu2+ are - 0.842, - 0.795 and - 0.285 V (vs. Ag/AgCl), respectively. In addition, it is sensitive to detect some transitional elements like Fe3+ and Hg2+ at - 0.027 and + 0.206 V. The detection limit for Cu2+ and Pb2+ was estimated to be 10.2 and 15.4 nM, respectively. Eventually, the developed sensor was successfully applied to detect contaminants in various cosmetic samples. A mechanism for sensing model has been proposed and discussed.

JVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration.

Abstract: The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.

Compact neural-network quantum state representations of Jastrow and stabilizer states

Abstract: Neural-network quantum states (NQS) have become a powerful tool in many-body physics. Of the numerous possible architectures in which neural-networks can encode amplitudes of quantum states the simplicity of the Restricted Boltzmann Machine (RBM) has proven especially useful for both numerical and analytical studies. In particular devising exact NQS representations for important classes of states, like Jastrow and stabilizer states, has provided useful clues into the strengths and limitations of the RBM based NQS. However, current constructions for a system of $N$ spins generate NQS with $M \sim O(N^2)$ hidden units that are very sparsely connected. This makes them rather atypical NQS compared to those commonly generated by numerical optimisation. Here we focus on compact NQS, denoting NQS with a hidden unit density $\alpha = M/N \leq 1$ but with system-extensive hidden-visible unit connectivity. By unifying Jastrow and stabilizer states we introduce a new exact representation that requires at most $M=N-1$ hidden units, illustrating how highly expressive $\alpha \leq 1$ can be. Owing to their structural similarity to numerical NQS solutions our result provides useful insights and could pave the way for more families of quantum states to be represented exactly by compact NQS.

Implementing the National Quality Standard in schools: leadership that motivates improvement initiatives through psychological ownership

Abstract: From 2016, all Western Australian schools were mandated to implement the National Quality Standard (NQS) in Kindergarten through to Year 2. Over the first year of implementation, this mandate had varying degrees of success in adoption. This study examined four schools which were identified as having implemented the NQS. A qualitative methodology was employed to examine those factors that supported implementation. A key finding was the integral role played by distributed leadership in adopting new initiatives. Using Activity Theory to conceptualise the data, it was found that psychological ownership was a key factor in enabling distributed leadership. Ownership was enabled when community differences were acknowledged, and time was given for the NQS tool to be reassessed and reconfigured as one’s own. Once staff had psychological ownership, they were more likely to support implementation of the NQS. This study has implications for leaders and those implementing the NQS or other initiatives in schools.

Neural-Network Quantum States for Spin-1 Systems: Spin-Basis and Parameterization Effects on Compactness of Representations.

Abstract: Neural network quantum states (NQS) have been widely applied to spin-1/2 systems, where they have proven to be highly effective. The application to systems with larger on-site dimension, such as spin-1 or bosonic systems, has been explored less and predominantly using spin-1/2 Restricted Boltzmann Machines (RBMs) with a one-hot/unary encoding. Here, we propose a more direct generalization of RBMs for spin-1 that retains the key properties of the standard spin-1/2 RBM, specifically trivial product states representations, labeling freedom for the visible variables and gauge equivalence to the tensor network formulation. To test this new approach, we present variational Monte Carlo (VMC) calculations for the spin-1 anti-ferromagnetic Heisenberg (AFH) model and benchmark it against the one-hot/unary encoded RBM demonstrating that it achieves the same accuracy with substantially fewer variational parameters. Furthermore, we investigate how the hidden unit complexity of NQS depend on the local single-spin basis used. Exploiting the tensor network version of our RBM we construct an analytic NQS representation of the Affleck-Kennedy-Lieb-Tasaki (AKLT) state in the xyz spin-1 basis using only M=2N hidden units, compared to M∼O(N2) required in the Sz basis. Additional VMC calculations provide strong evidence that the AKLT state in fact possesses an exact compact NQS representation in the xyz basis with only M=N hidden units. These insights help to further unravel how to most effectively adapt the NQS framework for more complex quantum systems.

Compact Neural-network Quantum State representations of Jastrow and Stabilizer states

Abstract: Neural-network quantum states (NQS) have become a powerful tool in many-body physics. Of the numerous possible architectures in which neural-networks can encode amplitudes of quantum states the simplicity of the Restricted Boltzmann Machine (RBM) has proven especially useful for both numerical and analytical studies. In particular devising exact NQS representations for important classes of states, like Jastrow and stabilizer states, has provided useful clues into the strengths and limitations of the RBM based NQS. However, current constructions for a system of $N$ spins generate NQS with $M \sim O(N^2)$ hidden units that are very sparsely connected. This makes them rather atypical NQS compared to those commonly generated by numerical optimisation. Here we focus on compact NQS, denoting NQS with a hidden unit density $\alpha = M/N \leq 1$ but with system-extensive hidden-visible unit connectivity. By unifying Jastrow and stabilizer states we introduce a new exact representation that requires at most $M=N-1$ hidden units, illustrating how highly expressive $\alpha \leq 1$ can be. Owing to their structural similarity to numerical NQS solutions our result provides useful insights and could pave the way for more families of quantum states to be represented exactly by compact NQS.

Neural-network Quantum States for Spin-1 systems: spin-basis and parameterization effects on compactness of representations

Abstract: Neural network quantum states (NQS) have been widely applied to spin-1/2 systems where they have proven to be highly effective. The application to systems with larger on-site dimension, such as spin-1 or bosonic systems, has been explored less and predominantly using spin-1/2 Restricted Boltzmann Machines (RBMs) with a one-hot/unary encoding. Here we propose a more direct generalisation of RBMs for spin-1 that retains the key properties of the standard spin-1/2 RBM, specifically trivial product states representations, labelling freedom for the visible variables and gauge equivalence to the tensor network formulation. To test this new approach we present variational Monte Carlo (VMC) calculations for the spin-1 antiferromagnetic Heisenberg (AFH) model and benchmark it against the one-hot/unary encoded RBM demonstrating that it achieves the same accuracy with substantially fewer variational parameters. Further to this we investigate how the hidden unit complexity of NQS depend on the local single-spin basis used. Exploiting the tensor network version of our RBM we construct an analytic NQS representation of the Affleck-Kennedy-Lieb-Tasaki (AKLT) state in the $xyz$ spin-1 basis using only $M = 2N$ hidden units, compared to $M \sim O(N^2)$ required in the $S^z$ basis. Additional VMC calculations provide strong evidence that the AKLT state in fact possesses an exact compact NQS representation in the $xyz$ basis with only $M=N$ hidden units. These insights help to further unravel how to most effectively adapt the NQS framework for more complex quantum systems.