A quantum access network

Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample...

Boolean networks are commonly used in systems biology to dynamically model gene regulatory interactions. Here, we present a protocol for implementing Boolean network dynamics as quantum circuits. We describe steps for accessing cloud-based quantum processing units offered by IBM and IonQ and downloading and parsing logic for gene regulatory networks. We then detail procedures for performing simulations of quantum circuits on local devices and visualizing measurement results. For complete details on the use and execution of this protocol, please refer to Weidner et al...

This study aims to investigate the potential therapeutic application of Ixeridium dentatum (ID) in treating atopic dermatitis (AD) through network pharmacology, molecular docking, and molecular dynamic simulation. We employed GC-MS techniques and identified 40 bioactive compounds present in the ID and determined their targets by accessing public databases. The convergence of compounds and dermatitis related targets led to the identification of 32 common genes. Among them, IL1B, PTGS2, IL6, IL2, and RELA, were found to be significant targets which were analyzed using Cytoscape network topology...

Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size N, have long been a concern. Here we propose the Schmidt tensor network state (Schmidt TNS) that efficiently represents the Schmidt decomposition of finite- and even infinite-size quantum states with nontrivial bipartition boundary. The key idea is to represent the Schmidt coefficients (i.e., entanglement spectrum) and transformations in the decomposition to tensor networks (TNs) with linearly scaled complexity versus N...

Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent blocks (QRBs) are constructed in the hardware-efficient way, and the QRNN is built by stacking the QRBs in a staggered way that can greatly reduce the algorithm's requirement with regard to the coherent time of quantum devices...

Having access to accurate electron densities in chemical systems, especially for dynamical systems involving chemical reactions, ion transport, and other charge transfer processes, is crucial for numerous applications in materials chemistry. Traditional methods for computationally predicting electron density data for such systems include quantum mechanical (QM) techniques, such as density functional theory. However, poor scaling of these QM methods restricts their use to relatively small system sizes and short dynamic time scales...

Two-dimensional (2D) transition metal titanium carbide (Ti3 C2 Tx ) as a promising candidate material for batteries and supercapacitors has shown excellent electrochemical performance, but it is difficult to meet practical applications because of its poor morphology structure, low mechanical properties, and expensive process. Here, w e propose an applied and efficient method based on microfluidic wet-fusing spinning chemistry (MWSC) to construct hierarchical structure of MXene-based fibre fabrics (MFFs), allowing the availability of MFF electrodes with ultra-strong toughness, high conductivity and easily machinable properties...

Continuous variable quantum key distribution that can be implemented using only low-cost and off-the-shelf components reveals great potential in practical large-scale realization. Access networks, as a modern network necessity, connect many end-users to the network backbone. In this work, we first demonstrate upstream transmission quantum access networks using continuous variable quantum key distribution. A two-end-user quantum access network is then experimentally realized. Through phase compensation, data synchronization, and other technical upgrades, we achieve a secret key rate of the total network of 390 kbits/s...

Thus far, a perfectly secure encryption key storage system doesn't exist. As long as key storage is connected to a network system, there is always a chance that it can be cracked. Even if storage is not continually connected to a network system; it is repeatedly necessary for an individual to access storage to upload and download the data; hence there is always a loophole with every conventional encryption key storage system. By utilizing the penetrative nature of cosmic-ray muons, the COSMOCAT (Cosmic coding and transfer) technique may tackle this problem by eliminating the requirement for any network connection to data storage...

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN...

Metal hydrides are an uprising compound class bringing up various functional materials. Due to the low X-ray scattering power of hydrogen, neutron diffraction is often crucial to fully disclose the structural characteristics thereof. We herein present the second strontium nitridoborate hydride known so far, Sr13[BN2]6H8, formed in a solid-state reaction of the binary nitrides and strontium hydride at 950 °C. The crystal structure was elucidated based on single-crystal X-ray and neutron powder diffraction in the hexagonal space group P63/m (no...

Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds...

Programmable linear optical interferometers are important for classical and quantum information technologies, as well as for building hardware-accelerated artificial neural networks. Recent results showed the possibility of constructing optical interferometers that could implement arbitrary transformations of input fields even in the case of high manufacturing errors. The building of detailed models of such devices drastically increases the efficiency of their practical use. The integral design of interferometers complicates its reconstruction since the internal elements are hard to address...

Wolfiporia extensa (WE) is a medicinal mushroom and an excellent source of naturally occurring anti-inflammatory substances. However, the particular bioactive compound(s) and mechanism(s) of action against inflammation have yet to be determined. Here, we studied anti-inflammatory bioactive compounds and their molecular mechanisms through network pharmacology. Methanol (ME) extract of WE (MEWE) was used for GC-MS analysis to identify the bioactives, which were screened by following Lipinski's rules. Public databases were used to extract selected bioactives and inflammation-related targets, and Venn diagrams exposed the common targets...

ConspectusMetal chalcogenide quantum dots (QDs) are prized for their unique and functional properties, associated with both intrinsic (quantum confinement) and extrinsic (high surface area) effects, as dictated by their size, shape, and surface characteristics. Thus, they have considerable promise for diverse applications, including energy conversion (thermoelectrics and photovoltaics), photocatalysis, and sensing. QD gels are macroscopic porous structures consisting of interconnected QDs and pore networks in which the pores may be filled with solvent (i...

PURPOSE: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data...

This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions...

Soft graphitizable carbon-based multifunctional nanomaterials have found versatile applications ranging from energy storage to quantum computing. In contrast, their hard-carbon analogues have been poorly investigated from both fundamental and application-oriented perspectives. The predominant challenges have been (a) the lack of approaches to fabricate porous hard-carbons and (b) their thermally nongraphitizable nature, leading to inaccessibility for several potential applications. In this direction, we present design principles for fabrication of porous hard-carbon-based nanostructured carbon florets (NCFs) with a highly accessible surface area (∼936 m2 /g), rivalling their soft-carbon counterparts...

Destabilization of a ligand-stabilized semiconductor nanocrystal solution with an oxidizing agent can lead to a macroscopic highly porous self-supporting nanocrystal network entitled hydrogel, with good accessibility to the surface. The previously reported charge carrier delocalization beyond a single nanocrystal building block in such gels can extend the charge carrier mobility and make a photocatalytic reaction more probable. The synthesis of ligand-stabilized nanocrystals with specific physicochemical properties is possible, thanks to the advances in colloid chemistry made in the last decades...

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra...