Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.Comment: 12pages, 6 figure

Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this letter, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first develop a new way of performing the sine transformation, and based on it the algorithm is optimized by reducing the depth of the circuit from n2 to n. Then, we analyze the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error mitigation and error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.Comment: 20 pages, 9 figure

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard

Genome-wide copy number variation detection in a large cohort of diverse horse breeds by whole-genome sequencing

Understanding how genetic variants alter phenotypes is an essential aspect of genetic research. Copy number variations (CNVs), a type of prevalent genetic variation in the genome, have been the subject of extensive study for decades. Numerous CNVs have been identified and linked to specific phenotypes and diseases in horses. However, few studies utilizing whole-genome sequencing to detect CNVs in large horse populations have been conducted. Here, we performed whole-genome sequencing on a large cohort of 97 horses from 16 horse populations using Illumina Hiseq panels to detect common and breed-specific CNV regions (CNVRs) genome-wide. This is the largest number of breeds and individuals utilized in a whole genome sequencing-based horse CNV study, employing racing, sport, local, primitive, draft, and pony breeds from around the world. We identified 5,053 to 44,681 breed CNVRs in each of the 16 horse breeds, with median lengths ranging from 1.9 kb to 8 kb. Furthermore, using Vst statistics we analyzed the population differentiation of autosomal CNVRs in three diverse horse populations (Thoroughbred, Yakutian, and Przewalski’s horse). Functional annotations were performed on CNVR-overlapping genes and revealed that population-differentiated candidate genes (CTSL, RAB11FIP3, and CTIF) may be involved in selection and adaptation. Our pilot study has provided the horse genetic research community with a large and valuable CNVR dataset and has identified many potential horse breeding targets that require further validation and in-depth investigation

AT2R (Angiotensin II Type 2 Receptor)-Mediated Regulation of NCC (Na-Cl Cotransporter) and Renal K Excretion Depends on the K Channel, Kir4.1

AT2R (AngII [angiotensin II] type 2 receptor) is expressed in the distal nephrons. The aim of the present study is to examine whether AT2R regulates NCC (Na-Cl cotransporter) and Kir4.1 of the distal convoluted tubule. AngII inhibited the basolateral 40 pS K channel (a Kir4.1/5.1 heterotetramer) in the distal convoluted tubule treated with losartan but not with PD123319. AT2R agonist also inhibits the K channel, indicating that AT2R was involved in tonic regulation of Kir4.1. The infusion of PD123319 stimulated the expression of tNCC (total NCC) and pNCC (phosphorylated NCC; Thr(53)) by a time-dependent way with the peak at 4 days. PD123319 treatment (4 days) stimulated the basolateral 40 pS K channel activity, augmented the basolateral K conductance, and increased the negativity of distal convoluted tubule membrane. The stimulation of Kir4.1 was essential for PD123319-induced increase in NCC because inhibiting AT2R increased the expression of tNCC and pNCC only in wild-type but not in the kidney-specific Kir4.1 knockout mice. Renal clearance study showed that thiazide-induced natriuretic effect was larger in PD123319-treated mice for 4 days than untreated mice. However, this effect was absent in kidney-specific Kir4.1 knockout mice which were also Na wasting under basal conditions. Finally, application of AT2R antagonist decreased the renal ability of K excretion and caused hyperkalemia in wild-type but not in kidney-specific Kir4.1 knockout mice. We conclude that AT2R-dependent regulation of NCC requires Kir4.1 in the distal convoluted tubule and that AT2R plays a role in stimulating K excretion by inhibiting Kir4.1 and NCC

Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science

Nano-engineering of electron correlation in oxide superlattices

This Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 license after a 12 month embargo period. The published version can found here: https//dx.doi.org/10.1088/2399-1984/aa8f39Oxide heterostructures and superlattices have attracted a great deal of attention in recent years owing to the rich exotic properties encountered at their interfaces. We focus on the potential of tunable correlated oxides by investigating the spectral function of the prototypical correlated metal SrVO<sub>3</sub>, using soft x-ray absorption spectroscopy (XAS) and resonant inelastic soft x-ray scattering (RIXS) to access both unoccupied and occupied electronic states, respectively. We demonstrate a remarkable level of tunability in the spectral function of SrVO<sub>3</sub> by varying its thickness within the SrVO<sub>3</sub>/SrTiO<sub>3</sub> superlattice, showing that the effects of electron correlation can be tuned from dominating the energy spectrum in a strongly correlated Mott-Hubbard insulator, towards a correlated metal. We show that the effects of dimensionality on the correlated properties of SrVO<sub>3</sub> are augmented by interlayer coupling, yielding a highly flexible correlated oxide that may be readily married with other oxide systems.2018-09-2