Mitigating algorithmic errors in quantum optimization through energy extrapolation

Quantum optimization algorithms offer a promising route to finding the ground states of target Hamiltonians on near-term quantum devices. Nonetheless, it remains necessary to limit the evolution time and circuit depth as much as possible, since otherwise decoherence will degrade the computation. Even when this is done, there always exists a non-negligible error in estimates of the ground state energy. Here we present a scalable extrapolation approach to mitigating this algorithmic error, which significantly improves estimates obtained using three well-studied quantum optimization algorithms: quantum annealing (QA), the variational quantum eigensolver, and the quantum imaginary time evolution at fixed evolution time or circuit depth. The approach is based on extrapolating the annealing time to infinity or the variance of estimates to zero. The method is reasonably robust against noise. For Hamiltonians which only involve few-body interactions, the additional computational overhead is an increase in the number of measurements by a constant factor. Analytic derivations are provided for the quadratic convergence of estimates of energy as a function of time in QA, and the linear convergence of estimates as a function of variance in all three algorithms. We have verified the validity of these approaches through both numerical simulation and experiments on IBM quantum machines. This work suggests a promising new way to enhance near-term quantum computing through classical post-processing.journal articl

JourneyDB: A Benchmark for Generative Image Understanding

While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehend generated images remains uncertain. Synthetic images, in comparison to real data, encompass a higher level of diversity in terms of both content and style, thereby presenting significant challenges for the models to fully grasp. In light of this challenge, we introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images within the context of multi-modal visual understanding. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images, each paired with the corresponding text prompts that were employed in their creation. Furthermore, we additionally introduce an external subset with results of another 22 text-to-image generative models, which makes JourneyDB a comprehensive benchmark for evaluating the comprehension of generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension in relation to both content and style interpretation. These benchmarks encompass prompt inversion, style retrieval, image captioning, and visual question answering. Lastly, we evaluate the performance of state-of-the-art multi-modal models when applied to the JourneyDB dataset, providing a comprehensive analysis of their strengths and limitations in comprehending generated content. We anticipate that the proposed dataset and benchmarks will facilitate further research in the field of generative content understanding. The dataset is publicly available at https://journeydb.github.io.Comment: Accepted to the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Wafer-scale growth of large arrays of perovskite microplate crystals for functional electronics and optoelectronics

Methylammonium lead iodide perovskite has attracted intensive interest for its diverse optoelectronic applications. However, most studies to date have been limited to bulk thin films that are difficult to implement for integrated device arrays because of their incompatibility with typical lithography processes. We report the first patterned growth of regular arrays of perovskite microplate crystals for functional electronics and optoelectronics. We show that large arrays of lead iodide microplates can be grown from an aqueous solution through a seeded growth process and can be further intercalated with methylammonium iodide to produce perovskite crystals. Structural and optical characterizations demonstrate that the resulting materials display excellent crystalline quality and optical properties. We further show that perovskite crystals can be selectively grown on prepatterned electrode arrays to create independently addressable photodetector arrays and functional field effect transistors. The ability to grow perovskite microplates and to precisely place them at specific locations offers a new material platform for the fundamental investigation of the electronic and optical properties of perovskite materials and opens a pathway for integrated electronic and optoelectronic systems.Comment: 8 pages, 4 figure

On sampled-data control for master-slave synchronization of chaotic Lur'e systems

This brief presents a new method for master-slave synchronization of chaotic Lur'e systems with sampled-data control. The new method is based on a novel construction of piecewise differentiable Lyapunov functionals in the framework of the input delay approach. The new Lyapunov functional is continuous at sampling times but not necessarily positive definite inside the sampling intervals. Compared with the existing works, the proposed method makes full use of the information on the piecewise constant input and the actual sampling pattern. Two illustrative examples are given which substantiate the usefulness of the proposed method

Upgulation of lncRNA GASL1 inhibits atherosclerosis by regulating miR-106a/LKB1 axis

Abstract Background Atherosclerosis (AS) is a common frequently-occurring disease in the clinic and a serious threat to human health. This research aimed to explore the value between GASL1 and AS. Methods The expression and values of GASL1 in AS patients were revealed by qRT-PCR and ROC curve. The HUVEC cells were induced by ox-LDL to construct in-vitro models. Cell viability was detected by MTT assay, and apoptosis was detected by flow cytometry. The inflammatory situation was reflected by the ELISA assay. Double luciferase reporter gene assay verified the regulatory relationship between GASL1 and miR-106a, miR-106a and LKB1. Results The levels of GASL1 was lower in AS group than those in control group. The value of GASL1 in predicting AS patients was also tested by the ROC curve. After HUVEC cells were induced by ox-LDL, the levels of GASL1 and LKB1 decreased significantly, while the level of miR-106a increased significantly. Upregulation of LKB1 reversed the effect of upregulation of GASL1 on viability, apoptosis, and inflammation of HUVEC cells induced by ox-LDL. Conclusion Overexpression of GASL1 might suppress ox-LDL-induced HUVEC cell viability, apoptosis, and inflammation by regulating miR-106a/LKB1 axis

A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM

Recent Advances in Cyanotoxin Synthesis and Applications: A Comprehensive Review

Over the past few decades, nearly 300 known cyanotoxins and more than 2000 cyanobacterial secondary metabolites have been reported from the environment. Traditional studies have focused on the toxic cyanotoxins produced by harmful cyanobacteria, which pose a risk to both human beings and wildlife, causing acute and chronic poisoning, resulting in diarrhea, nerve paralysis, and proliferation of cancer cells. Actually, the biotechnological potential of cyanotoxins is underestimated, as increasing studies have demonstrated their roles as valuable products, including allelopathic agents, insecticides and biomedicines. To promote a comprehensive understanding of cyanotoxins, a critical review is in demand. This review aims to discuss the classifications; biosynthetic pathways, especially heterogenous production; and potential applications of cyanotoxins. In detail, we first discuss the representative cyanotoxins and their toxic effects, followed by an exploration of three representative biosynthetic pathways (non-ribosomal peptide synthetases, polyketide synthetases, and their combinations). In particular, advances toward the heterologous biosynthesis of cyanotoxins in vitro and in vivo are summarized and compared. Finally, we indicate the potential applications and solutions to bottlenecks for cyanotoxins. We believe that this review will promote a comprehensive understanding, synthetic biology studies, and potential applications of cyanotoxins in the future