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

Aerosolization behavior of antimicrobial resistance in animal farms: a field study from feces to fine particulate matter

Antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB) in animal feces can be released into the atmosphere via aerosolization, posing a high health risk to farm workers. So far, little attention has been paid to the characterization of the aerosolization process. In this study, fecal and fine particulate matter (PM2.5) samples were collected from 20 animal farms involving swine, cattle, layers, and broilers, and the ARGs, ARB, and human pathogenic bacteria (HPB) were loaded in these two media. The results showed that approximately 70% of ARGs, 60% of ARBs, and 43% of HPBs were found to be preferential aerosolization. The bioaerosolization index (BI) of target 30 ARGs varied from 0.04 to 460.07, and the highest value was detected from tetW. The highest BI values of erythromycin- and tetracycline-resistant bacteria were for Kocuria (13119) and Staphylococcus (24746), respectively, and the distribution of BI in the two types of dominant ARB was similar. Regarding the bioaerosolization behavior of HPB, Clostridium saccharolyticum WM1 was the most easily aerosolized pathogen in swine and broiler farms, and Brucella abortus strain CNM 20040339 had the highest value in cattle and layer farms. Notably, the highest BI values for ARGs, ARB, and HPB were universally detected on chicken farms. Most ARGs, ARB, and HPB positively correlated with animal age, stocking density, and breeding area. Temperature and relative humidity have significant effects on the aerosolization behavior of targets, and the effects of these two parameters on the same target are usually opposite. The results of this study provide a basis for a better understanding of the contribution of animal feces to airborne ARGs and HPBs in farms, as well as for controlling the transport of the fecal microbiome to the environment through the aerosolization pathway

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

EEG-based serious games design for E-learning

This final year project report records a summary of the design and implementation of the EEG-based game Remember me! for e-learning applications. This well-designed e-learning game is capable of training a kind of cognitive ability, memory, which helps people to recall important information more quickly and accurately. The player is required to play the role of a bartender and remember his customers' names and drinks in order to earn higher tips and job promotions. Two different brain states of “anxiety” and “boredom” could be detected by EEG, of which the values are calculated using fractal dimension model, during the proceeding of playing the game. These values and the threshold then determine the transition between different levels of difficulty of the game, with each level containing less or more customers to remember.Bachelor of Engineerin

Threat categories of Vatica mangachapoi should be reassessed

IntroductionAsian tropical rainforests have the highest rates of degradation in the world. Consequently, a large decline in Chinese Vatica mangachapoi (a keystone species) had led to its listing in the category of “vulnerable” species by IUCN. However, its current status after decades of conservation efforts remains unknown.MethodsHere, we evaluate the current status of Chinese V. mangachapoi.Results and DiscussionWe found that its population is now dispersed in 14 protected areas, the largest being a coastal forest that contains 96.84% of all the Chinese V. mangachapoi. Compared to their historic records, the age of this forest was estimated at ≤ 70 years. The mono-culturing of V. mangachapoi in this forest, since 1960, has replaced all the older trees, resulting in its extremely high (91%) relative abundance, and an extensively low (only 44) tree species richness. Further, these V. mangachapoi trees now suffer from vine strangulations and severe Amauroderma perplexum infections: 18.5% of V. mangachapoi have died and 75% are at a high risk, thereby creating a threat of its extinction. Although, the other 13 protected areas have a higher tree species richness (152–451), a lower (6.1–25%) relative abundance of V. mangachapoi, and they neither suffer from vine strangulation or disease infections, they contribute to only 3.16% of total Chinese population of this species. Therefore, an immediate revision of threat status of this species in IUCN, from vulnerable to endangered, is warranted. Further, a change in planting patterns, from monocultures to mix-plantations of native species, is needed to promote biodiversity and restrict other biotic challenges so that this species is not extinct