Optimizing Quantum Programs against Decoherence: Delaying Qubits into Quantum Superposition

Quantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum programs, the compiler and runtime system should consider underlying quantum hardware features such as decoherence. To address the challenges posed by decoherence, in this paper, we propose and prototype QLifeReducer to minimize the qubit lifetime in the input OpenQASM program by delaying qubits into quantum superposition. QLifeReducer includes three core modules, i.e.,the parser, parallelism analyzer and transformer. It introduces the layered bundle format to express the quantum program, where a set of parallelizable quantum operations is packaged into a bundle. We evaluate quantum programs before and after transformed by QLifeReducer on both real IBM Q 5 Tenerife and the self-developed simulator. The experimental results show that QLifeReducer reduces the error rate of a quantum program when executed on IBMQ 5 Tenerife by 11%; and can reduce the longest qubit lifetime as well as average qubit lifetime by more than 20% on most quantum workloads.Comment: To appear in TASE2019 - the 13th International Symposium on Theoretical Aspects of Software Engineering (submitted on Jan 25, 2019, and this is camera-ready version

Large-scale single-photon imaging

Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Numerical Solutions of Inverse Nodal Problems for a Boundary Value Problem

In this paper, we study inverse nodal problems for a boundary value problem. A uniqueness result for the potential function and a reconstruction method are obtained. By using the nodal points as input data, we compute the approximation solution of the potential function for the boundary value problem by the first kind Chebyshev wavelet method. Two numerical examples show that the first kind Chebyshev wavelet method for solving the inverse nodal problems for the boundary value problem is valid

Numerical Solutions of Inverse Nodal Problems for a Boundary Value Problem

In this paper, we study inverse nodal problems for a boundary value problem. A uniqueness result for the potential function and a reconstruction method are obtained. By using the nodal points as input data, we compute the approximation solution of the potential function for the boundary value problem by the first kind Chebyshev wavelet method. Two numerical examples show that the first kind Chebyshev wavelet method for solving the inverse nodal problems for the boundary value problem is valid

High-resolution single-photon imaging with physics-informed deep learning

Abstract High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique’s state-of-the-art super-resolution SPAD imaging performance

Flare and change in disease activity among patients with stable rheumatoid arthritis following coronavirus disease 2019 vaccination: A prospective Chinese cohort study

Abstract. Background:. Vaccination has been shown effective in controlling the global coronavirus disease 2019 (COVID-19) pandemic and reducing severe cases. This study was to assess the flare and change in disease activity after COVID-19 vaccination in patients with stable rheumatoid arthritis (RA). Methods:. A prospective cohort of RA patients in remission or with low disease activity was divided into a vaccination group and a non-vaccination group based on their COVID-19 vaccination status. Each of them was examined every 3 to 6 months. In the vaccination group, disease activity was compared before and after vaccination. The rates of flare defined as disease activity scores based on 28-joint count (DAS28) >3.2 with ΔDAS28 ≥0.6 were compared between vaccination and non-vaccination groups. Results:. A total of 202 eligible RA patients were enrolled. Of these, 98 patients received no vaccine shot (non-vaccination group), and 104 patients received two doses of vaccine (vaccination group). The median time interval from pre-vaccination visit to the first immunization and from the second dose of vaccine to post-vaccination visit was 67 days and 83 days, respectively. The disease activity scores at pre-vaccination and post-vaccination visits in the vaccination group patients were similar. At enrollment, gender, RA disease course, seropositivity, and disease activity were comparable across the two groups. Flare was observed in five (4.8%) of the vaccination group patients and nine (9.2%) of the non-vaccination group patients at post-vaccination assessment (P = 0.221). In terms of safety, 29 (27.9%) patients experienced adverse events (AEs) after vaccination. No serious AEs occurred. Conclusions:. COVID-19 vaccinations had no significant effect on disease activity or risk of flare in RA patients in remission or with low disease activity. Patients with stable RA should be encouraged to receive the COVID-19 vaccination

Metabolite and protein shifts in mature erythrocyte under hypoxia

Summary: As the only cell type responsible for oxygen delivery, erythrocytes play a crucial role in supplying oxygen to hypoxic tissues, ensuring their normal functions. Hypoxia commonly occurs under physiological or pathological conditions, and understanding how erythrocytes adapt to hypoxia is fundamental for exploring the mechanisms of hypoxic diseases. Additionally, investigating acute and chronic mountain sickness caused by plateaus, which are naturally hypoxic environments, will aid in the study of hypoxic diseases. In recent years, increasingly developed proteomics and metabolomics technologies have become powerful tools for studying mature enucleated erythrocytes, which has significantly contributed to clarifying how hypoxia affects erythrocytes. The aim of this article is to summarize the composition of the cytoskeleton and cytoplasmic proteins of hypoxia-altered erythrocytes and explore the impact of hypoxia on their essential functions. Furthermore, we discuss the role of microRNAs in the adaptation of erythrocytes to hypoxia, providing new perspectives on hypoxia-related diseases