Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determines the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. Firstly, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Secondly, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96x reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5x compared to the state-of-the-art CPU-based results and 4.48-6.78x compared to the state-of-the-art GPU-based results reported in the literature.Comment: 25 pages, 10 figure

Quinoa whole grain diet compromises the changes of gut microbiota and colonic colitis induced by dextran Sulfate sodium in C57BL/6 mice

A plethora of evidence highlights that the dysbiosis of gut microbiota is a critical factor for inflammatory bowel disease (IBD). Both in vivo and in vitro studies have demonstrated that quinoa possesses potential prebiotic effects. The present study aims to examine the potential in using quinoa to ameliorate the dysbiosis and colitis induced by dextran sodium sulfate (DSS). A total of 40 C57BL/6 mice were fed either an AIN-93M diet or a quinoa-based diet, separately. Colitis was induced for 10 animals/dietary group with a 5-days exposure to 2.5% DSS. The clinical symptoms were monitored every other day, and the gut microbiota was characterized by 165 rRNA gene sequencing. The results indicated that consumption of quinoa lessened clinical symptoms as indicated by the reduced disease activity index and the degree of histological damage (P \u3c 0.05). As expected, the DSS treatment induced significant dysbiosis of gut microbiota in mice on an AIN-93M diet. However, compared to mice fed the AIN-93M diet, the consumption of quinoa alleviated the DSS-induced dysbiosis remarkably, as indicated by increased species richness and diversity, decreased abnormal expansion of phylum Proteobacteria, and decreased overgrowth of genera Escherichia/Shigella and Peptoclostridium (P \u3c 0.05). The relative abundances of Firmicutes and Bacteroidetes were less altered in mice fed with quinoa comparing to those mice fed the AIN-93M diet. In summary, the consumption of quinoa suppressed the dysbiosis of gut microbiota and alleviated clinical symptoms induced by DSS, indicating the potential to utilize quinoa as a dietary approach to improve intestinal health

Combined helical tomotherapy and Gamma Knife stereotactic radiosurgery for high-grade recurrent orbital meningioma: a case report

Orbital meningioma is a rare type of orbital tumor with high invasiveness and recurrence rates, making it extremely challenging to treat. Due to the special location of the disease, surgery often cannot completely remove the tumor, requiring postoperative radiation therapy. Here, we report a case of an elderly male patient with right-sided proptosis, visual impairment, and diplopia. Imaging diagnosis revealed a space-occupying lesion in the extraconal space of the right orbit. Pathological and immunohistochemical examination of the resected tumor confirmed it as a grade 3 anaplastic meningioma. Two months after surgery, the patient complained of right eye swelling and a magnetic resonance imaging (MRI) scan showed a recurrence of the tumor. The patient received helical tomotherapy (TOMO) in the postoperative tumor bed and high-risk areas within the orbit with a total dose of 48Gy. However, there was no significant improvement in the patient’s right eye swelling, and the size of the recurrent lesion showed no significant change on imaging. Gamma knife multifractionated stereotactic radiosurgery (MF-SRS) was then given to the recurrent lesion with 50% prescription dose 13.5Gy/3f, once every other day. An imaging diagnosis performed 45 days later showed that the tumor had disappeared completely. The patient’s vision remained unchanged, but diplopia was significantly relieved after MF-SRS. We propose a new hybrid treatment model for recurrent orbital meningioma, where conventional radiation therapy ensures local control of high-risk areas around the postoperative cavity, and MF-SRS maximizes the radiation dose to recurrent lesion areas while protecting surrounding tissues and organs

Identification and characterization of CBL and CIPK gene families in canola (Brassica napus L.)

BACKGROUND: Canola (Brassica napus L.) is one of the most important oil-producing crops in China and worldwide. The yield and quality of canola is frequently threatened by environmental stresses including drought, cold and high salinity. Calcium is a ubiquitous intracellular secondary messenger in plants. Calcineurin B-like proteins (CBLs) are Ca(2+) sensors and regulate a group of Ser/Thr protein kinases called CBL-interacting protein kinases (CIPKs). Although the CBL-CIPK network has been demonstrated to play crucial roles in plant development and responses to various environmental stresses in Arabidopsis, little is known about their function in canola. RESULTS: In the present study, we identified seven CBL and 23 CIPK genes from canola by database mining and cloning of cDNA sequences of six CBLs and 17 CIPKs. Phylogenetic analysis of CBL and CIPK gene families across a variety of species suggested genome duplication and diversification. The subcellular localization of three BnaCBLs and two BnaCIPKs were determined using green fluorescence protein (GFP) as the reporter. We also demonstrated interactions between six BnaCBLs and 17 BnaCIPKs using yeast two-hybrid assay, and a subset of interactions were further confirmed by bimolecular fluorescence complementation (BiFC). Furthermore, the expression levels of six selected BnaCBL and 12 BnaCIPK genes in response to salt, drought, cold, heat, ABA, methyl viologen (MV) and low potassium were examined by quantitative RT-PCR and these CBL or CIPK genes were found to respond to multiple stimuli, suggesting that the canola CBL-CIPK network may be a point of convergence for several different signaling pathways. We also performed a comparison of interaction patterns and expression profiles of CBL and CIPK in Arabidospsis, canola and rice, to examine the differences between orthologs, highlighting the importance of studying CBL-CIPK in canola as a prerequisite for improvement of this crop. CONCLUSIONS: Our findings indicate that CBL and CIPK family members may form a dynamic complex to respond to different abiotic or hormone signaling. Our comparative analyses of the CBL-CIPK network between canola, Arabidopsis and rice highlight functional differences and the necessity to study CBL-CIPK gene functions in canola. Our data constitute a valuable resource for CBL and CPK genomics

Reactive oxygen species may be involved in the distinctive biological effects of different doses of 12C6+ ion beams on Arabidopsis

IntroductionHeavy ion beam is a novel approach for crop mutagenesis with the advantage of high energy transfer line density and low repair effect after injury, however, little investigation on the biological effect on plant was performed. 50 Gy irradiation significantly stimulated the growth of Arabidopsis seedlings, as indicated by an increase in root and biomass, while 200 Gy irradiation significantly inhibited the growth of seedlings, causing a visible decrease in plant growth.MethodsThe Arabidopsis seeds were irradiated by 12C6+. Monte Carlo simulations were used to calculate the damage to seeds and particle trajectories by ion implantation. The seed epidermis received SEM detection and changes in its organic composition were detected using FTIR. Evidence of ROS and antioxidant systems were analyzed. RNA-seq and qPCR were used to detect changes in seedling transcript levels.Results and discussionMonte Carlo simulations revealed that high-dose irradiation causes various damage. Evidence of ROS and antioxidant systems implies that the emergence of phenotypes in plant cells may be associated with oxidative stress. Transcriptomic analysis of the seedlings demonstrated that 170 DEGs were present in the 50 Gy and 200 Gy groups and GO enrichment indicated that they were mainly associated with stress resistance and cell wall homeostasis. Further GO enrichment of DEGs unique to 50 Gy and 200 Gy revealed 58 50Gy-exclusive DEGs were enriched in response to oxidative stress and jasmonic acid entries, while 435 200 Gy-exclusive DEGs were enriched in relation to oxidative stress, organic cyclic compounds, and salicylic acid. This investigation advances our insight into the biological effects of heavy ion irradiation and the underlying mechanisms

生殖年齢にある男性がん患者に対する妊孕性温存と支援提供に関する看護師の認識

内容の要約広島大学(Hiroshima University)博士(看護学)Doctor of Philosophy in Nursingdoctora

Implementation and Validation of Quadratic Constitutive Relation in One-Equation k-kL Turbulence Model

Computational Fluid Dynamics (CFD) has been widely used in modern engineering analysis of products and systems involving fluid flow. In majority of applications, the flow is turbulent; however accurate prediction of turbulent flow remains a challenging problem to date. Considering both the accuracy and cost of simulations, the most widely used approach for simulation of turbulent flows is to solve the Reynolds Averaged Navier-Stokes equations (RANS) in conjunction with a turbulence model which models the Reynolds stresses using the Boussinesq approximation that relates the Reynolds stress tensor to strain tensor via an eddy viscosity. In past many decades, a linear relationship between Reynolds Stress tensor and strain tensor has been employed in almost all turbulence models. Linear eddy viscosity models have shown good results for wide variety of flows; however in many cases they have been found to be inadequate. Therefore, the nonlinear Quadratic Constitutive Relation (QCR) has been suggested to improve the accuracy of simulations. In this thesis, QCR is implemented for a recently developed one-equation k-kL turbulence model by Shuai and Agarwal, and is tested by comparing its accuracy with linear eddy viscosity models and one-equation Algebraic Reynolds Stress Model (ARSM) developed by Wen and Agarwal. The computational results for k-kL-QCR for several benchmark cases from NASA TMR are compared to other widely used turbulence models with QCR, such as Spalart-Allmaras model (SA), Wray-Agarwal model (WA) and SST k-ω model. It is shown that one-equation k-kL-QCR model shows good accuracy against experimental data with less computational cost for both incompressible and compressible transonic and supersonic flow cases

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications