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Graphical abstract Highlights Pyruvate kinase M2 is a predictor of clinical severity of coronary artery disease Pyruvate kinase M2 predicts the incidence of major adverse cardiovascular events Pyruvate kinase M2 showed additional prognostic value in coronary artery disease IntroductionPyruvate kinase M2 (PKM2) was involved in the pathophysiology of atherosclerosis and coronary artery disease (CAD). We tested whether plasma PKM2 concentrations were correlated with clinical severity and major adverse cardiovascular events (MACEs) in CAD patients. Materials and methodsA total of 2443 CAD patients and 238 controls were enrolled. The follow-up time was two years. Plasma PKM2 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone, Wuhan, China) using SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, San Jose, USA). The predictors of acute coronary syndrome (ACS) were assessed by logistic regression analysis. The association between PKM2 concentration in different quartiles and MACEs was evaluated by Kaplan-Meier (KM) curves with log-rank test and Cox proportional hazard models. The predictive value of PKM2 and a cluster of conventional risk factors was determined by Receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were utilized to evaluate the enhancement in risk prediction when PKM2 was added to a predictive model containing a cluster of conventional risk factors. ResultsIn CAD patients, PKM2 concentration was the independent predictor of ACS (P < 0.001). Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses revealed that patients with a higher PKM2 concentration had higher incidence of MACEs compared to those with a lower PKM2 concentration (P < 0.001). The addition of PKM2 to a cluster of conventional risk factors significantly increased its prognostic value of MACEs. ConclusionBaseline plasma PKM2 conc

Principal component analysis (PCA) is a widely used tool in machine learning algorithms, but it can be computationally expensive. In 2014, Lloyd, Mohseni & Rebentrost proposed a quantum PCA (qPCA) algorithm [Nat. Phys. 10, 631 (2014)] that has not yet been experimentally demonstrated due to challenges in preparing multiple quantum state copies and implementing quantum phase estimations. In this study, we presented a hardware-efficient approach for qPCA, utilizing an iterative approach that effectively resets the relevant qubits in a nuclear magnetic resonance (NMR) quantum processor. Additionally, we introduced a quantum scattering circuit that efficiently determines the eigenvalues and eigenvectors (principal components). As an important application of PCA, we focused on classifying thoracic CT images from COVID-19 patients and achieved high accuracy in image classification using the qPCA circuit implemented on the NMR system. Our experiment highlights the potential of near-term quantum devices to accelerate qPCA, opening up new avenues for practical applications of quantum machine learning algorithms. (Figure presented.) © Higher Education Press 2024.

People are witnessing quantum computing revolutions nowadays. Progress in the number of qubits, coherence times and gate fidelities is happening. Although quantum error correction era has not arrived, the research and development of quantum computing have inspired insights and breakthroughs in quantum technologies, both in theories and in experiments. In this review, we introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer. There are different experimental platforms for implementing quantum computing. In this review, based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing, as well as the common challenges and techniques.

Chern insulators have been generalized to many classical wave systems and thereby lead to many potential applications such as robust waveguides, quantum computation and high-performance lasers. However, the band structure of a material can be either topologically trivial or non-trivial, depending on how the crystal structure is designed. Here, we propose a second Chern crystal in a four-dimensional parameter space by introducing two extra synthetic translation dimensions. Since the topology of the bulk bands in the synthetic translation space is intrinsically non-trivial, our proposed four-dimensional crystal is guaranteed to be topologically non-trivial regardless of the crystal's detailed configuration. We derive the topologically protected modes on the lower dimensional boundaries of such a crystal via dimension reduction. Remarkably, we observe the one-dimensional gapless dislocation modes and confirm their robustness in experiments. Our findings provide novel perspectives on topologically non-trivial crystals and may inspire designs of classical wave devices. Featuring with the gapless dislocation and corner modes, second Chern crystals with inherently nontrivial topology were demonstrated in the four-dimensional space with the introduction of synthetic translation dimension.

Cheng-Wen Li, Fu-Shan Xue, Xin-Tao Li Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Fu-Shan Xue, Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong-An Road, Xi-Cheng District, Beijing, 100050, People’s Republic of China, Tel +86-13911177655, Fax +86-10-63138362, Email xuefushan@aliyun.com; fushanxue@outlook.com

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion., QC 20220525

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion., QC 20220525

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator’s profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to 47%. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective., QC 20220328

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion., QC 20220525

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion., QC 20220525

Multi-sensor technology has been widely applied in the condition monitoring of rail transit. In practice, the data of some channels in the high channel counts are often abnormal or lost due to the abnormality and damage of the sensors, thus resulting in a large amount of data waste. A method for the data recovery of lost channels by using adjacent channel data is proposed to solve this problem. Based on the LSTM network algorithm, a data recovery model is established based on the “sequence-to-sequence” regression analysis of adjacent channel data. Taking the measured vibration data of a subway as an example, the network is trained with multi-channel measured data to recover the lost channel data of time-series characteristics. The results show that this multi-channel data recovery model is feasible, and the accuracy is up to 98%. This method can also further reduce the number of channels that need to be collected.

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion., QC 20220525

Tian Tian, Xin-Tao Li, Fu-Shan Xue Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Fu-Shan Xue, Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong-An Road, Xi-Cheng District, Beijing, 100050, People’s Republic of China, Tel +86-13911177655, Fax +86-10-63138362, Email xuefushan@aliyun.com; fushanxue@outlook.com

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

Quantum error correction plays an important role in fault-tolerant quantum information processing. It is usually difficult to experimentally realize quantum error correction, as it requires multiple qubits and quantum gates with high fidelity. Here we propose a simple quantum error-correcting code for the detected amplitude damping channel. The code requires only two qubits. We implement the encoding, the channel, and the recovery on an optical platform, the IBM Q System, and a nuclear magnetic resonance system. For all of these systems, the error correction advantage appears when the damping rate exceeds some threshold. We compare the features of these quantum information processing systems used and demonstrate the advantage of quantum error correction on current quantum computing platforms. © 2020 Science China Press

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper., QC 20210920

Cyber-physical systems (CPS) are paving new ground with increasing levels of automation and usage in applications with complex environments, posing greater challenges in terms of safety and reliability. The increasing complexity of CPS environments, tasks, and systems leads to more uncertainties. Unless properly managed, these uncertainties may lead to false detection of real fault condition of a system, which in turn may affect decision making and potentially cause fatal consequences. In order to implement safety-critical missions, such as autonomous driving, it is essential to develop a reliable monitoring and assessment service dealing with the complexity and uncertainty issues. In this article, we propose a fault detection function based on Bayesian inference, which combines empirical knowledge with information of the specific system. By considering uncertainties as possible causes for false detection, various uncertainties during the detection process are analyzed, and the ways to quantify and propagate them are explored. As a result, probabilistic inference is achieved for distinguishing system faults from uncertainties, which contributes to more reliable detection results regarding system faults under dynamically changing environments. A case study on an microelectro mechanical system (MEMS) accelerometer is conducted and the result shows that the fault detection function effectively distinguishes system faults and uncertainties arising from the environment., QC 20201103

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N × N matrixM, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t) as a function of time t according to the constraint dx(t )/dt =Mx(t ) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 × 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

Quantum error correction plays an important role in fault-tolerant quantum information processing. It is usually difficult to experimentally realize quantum error correction, as it requires multiple qubits and quantum gates with high fidelity. Here we propose a simple quantum error-correcting code for the detected amplitude damping channel. The code requires only two qubits. We implement the encoding, the channel, and the recovery on an optical platform, the IBM Q System, and a nuclear magnetic resonance system. For all of these systems, the error correction advantage appears when the damping rate exceeds some threshold. We compare the features of these quantum information processing systems used and demonstrate the advantage of quantum error correction on current quantum computing platforms. © 2020 Science China Press

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N × N matrixM, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t) as a function of time t according to the constraint dx(t )/dt =Mx(t ) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 × 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

Quantum error correction plays an important role in fault-tolerant quantum information processing. It is usually difficult to experimentally realize quantum error correction, as it requires multiple qubits and quantum gates with high fidelity. Here we propose a simple quantum error-correcting code for the detected amplitude damping channel. The code requires only two qubits. We implement the encoding, the channel, and the recovery on an optical platform, the IBM Q System, and a nuclear magnetic resonance system. For all of these systems, the error correction advantage appears when the damping rate exceeds some threshold. We compare the features of these quantum information processing systems used and demonstrate the advantage of quantum error correction on current quantum computing platforms. © 2020 Science China Press

Gravimetrijski satelitski podaci u širokoj su primjeni u geofizici u proučavanju dubokih struktura na regionalnoj i globalnoj razini. Ti podaci uključuju i gravitacijske anomalije slobodnog zraka, koje obično treba korigirati na Bouguer-ovu gravitacijsku anomaliju, radi praktične primjene. Metode Bouguer-ove redukcije mogu se podijeliti u dvije skupine ovisno o primijenjenom koordinatnom sustavu: 1. metoda sfernih koordinata (SBG) i 2. metoda kartezijevih koordinata; potonja se dalje dijeli na metode CEBG i CBG, koje uključuju, odnosno ne uključuju korekciju zbog zakrivljenosti Zemlje. U ovom su radu podaci o gravitacijskim anomalijama slobodnog zraka s Istočno-tibetanske visoravni i susjednih područja korišteni kao ulazni podaci za usporedbu triju metoda Bouguer-ove korekcije gravitacije: CBG, CEBG i SBG. Usporedba ove tri Bouguer-ove metode za gravitacijsku korekciju pokazala je da u istraživanom području učinak zakrivljenosti Zemlje na gravitaciju raste s povećanjem nadmorske visine. Cilj je bio shvatiti točnost inverzije za podatke dobivene različitim pristupima Bouguer-ovim anomalijama gravitacije. Raspodjele dubine Mohorovičićevog diskontinuiteta dobiveni su inverzijom Bouguer-ovih gravitacijskih anomalija dobivenih primjenom CBG, CEBG i SBG metoda, a aktivni seizmički profili korišteni su kao reference za usporedbu i ocjenu. Rezultati pokazuju da se dubine Mohorovičićevog diskontinuiteta dobivene SBG inverzijom bolje podudaraju s izmjerenim dubinama na seizmičkom profilu. Stoga se SBG metoda preporučuje kao najrealističniji pristup u postupku globalnih ili regionalnih istraživanja koja koriste gravimetrijske podatke., Satellite gravity data are widely used in the field of geophysics to study deep structures at the regional and global scales. These data comprise free-air gravity anomaly data, which usually need to be corrected to a Bouguer gravity anomaly for practical application. Bouguer reduction approaches can be divided into two methods based on the coordinate system: the spherical coordinates method (SBG) and the Cartesian coordinates method; the latter is further divided into the CEBG and CBG methods, which do and do not include the Earth’s curvature correction. In this paper, free-air gravity anomaly data from the eastern Tibetan Plateau and its adjacent areas were used as the basic data to compare the CBG, CEBG, and SBG Bouguer gravity correction methods. The comparison of these three Bouguer gravity correction methods shows that the effect of the Earth’s curvature on the gravitational effect increases with increasing elevation in the study area. We want to understand the inversion accuracy for the data obtained by different Bouguer gravity reduction approaches. The depth distributions of the Moho were obtained by the interface inversion of the Bouguer gravity anomalies obtained by the CBG, CEBG, and SBG, and active seismic profiles were used as references for comparison and evaluation. The results show that the depths of the Moho obtained by the SBG inversion are more consistent with the measured seismic profile depths. Therefore, the SBG method is recommended as the most realistic approach in the process of global or regional research employing gravity data.

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N × N matrixM, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t) as a function of time t according to the constraint dx(t )/dt =Mx(t ) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 × 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

Cyber-Physical Systems (CPS) are a result of highly cross-disciplinary processes and are evolving to perform increasingly challenging tasks in dynamically changing environments. This leads to an increasing CPS complexity and therefore the management of uncertainty to ensure the trustworthiness of these systems is needed. Our paper focuses on uncertainty management (UM) both in general and more specifically in the context of CPS situation awareness (SA). The motivation behind this is the important role of SA and its many inherent uncertainties. To this end, firstly, a literature review is conducted to acquire the state of the art of UM. Later, we present findings and observations from the literature review, with two main challenges identified-inconsistent understanding and terminology among a multitude of uncertainty perspectives, and a lack of collaboration among different communities. On this basis, lastly, two case studies are conducted to exemplify the challenges and provide brief ideas on how to deal with them. The whole investigation in the paper suggests an urgent strengthening of common understanding through enhanced collaboration and regulations., QC 20210315

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N × N matrixM, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t) as a function of time t according to the constraint dx(t )/dt =Mx(t ) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 × 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N × N matrixM, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t) as a function of time t according to the constraint dx(t )/dt =Mx(t ) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 × 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

Gravimetrijski satelitski podaci u širokoj su primjeni u geofizici u proučavanju dubokih struktura na regionalnoj i globalnoj razini. Ti podaci uključuju i gravitacijske anomalije slobodnog zraka, koje obično treba korigirati na Bouguer-ovu gravitacijsku anomaliju, radi praktične primjene. Metode Bouguer-ove redukcije mogu se podijeliti u dvije skupine ovisno o primijenjenom koordinatnom sustavu: 1. metoda sfernih koordinata (SBG) i 2. metoda kartezijevih koordinata; potonja se dalje dijeli na metode CEBG i CBG, koje uključuju, odnosno ne uključuju korekciju zbog zakrivljenosti Zemlje. U ovom su radu podaci o gravitacijskim anomalijama slobodnog zraka s Istočno-tibetanske visoravni i susjednih područja korišteni kao ulazni podaci za usporedbu triju metoda Bouguer-ove korekcije gravitacije: CBG, CEBG i SBG. Usporedba ove tri Bouguer-ove metode za gravitacijsku korekciju pokazala je da u istraživanom području učinak zakrivljenosti Zemlje na gravitaciju raste s povećanjem nadmorske visine. Cilj je bio shvatiti točnost inverzije za podatke dobivene različitim pristupima Bouguer-ovim anomalijama gravitacije. Raspodjele dubine Mohorovičićevog diskontinuiteta dobiveni su inverzijom Bouguer-ovih gravitacijskih anomalija dobivenih primjenom CBG, CEBG i SBG metoda, a aktivni seizmički profili korišteni su kao reference za usporedbu i ocjenu. Rezultati pokazuju da se dubine Mohorovičićevog diskontinuiteta dobivene SBG inverzijom bolje podudaraju s izmjerenim dubinama na seizmičkom profilu. Stoga se SBG metoda preporučuje kao najrealističniji pristup u postupku globalnih ili regionalnih istraživanja koja koriste gravimetrijske podatke., Satellite gravity data are widely used in the field of geophysics to study deep structures at the regional and global scales. These data comprise free-air gravity anomaly data, which usually need to be corrected to a Bouguer gravity anomaly for practical application. Bouguer reduction approaches can be divided into two methods based on the coordinate system: the spherical coordinates method (SBG) and the Cartesian coordinates method; the latter is further divided into the CEBG and CBG methods, which do and do not include the Earth’s curvature correction. In this paper, free-air gravity anomaly data from the eastern Tibetan Plateau and its adjacent areas were used as the basic data to compare the CBG, CEBG, and SBG Bouguer gravity correction methods. The comparison of these three Bouguer gravity correction methods shows that the effect of the Earth’s curvature on the gravitational effect increases with increasing elevation in the study area. We want to understand the inversion accuracy for the data obtained by different Bouguer gravity reduction approaches. The depth distributions of the Moho were obtained by the interface inversion of the Bouguer gravity anomalies obtained by the CBG, CEBG, and SBG, and active seismic profiles were used as references for comparison and evaluation. The results show that the depths of the Moho obtained by the SBG inversion are more consistent with the measured seismic profile depths. Therefore, the SBG method is recommended as the most realistic approach in the process of global or regional research employing gravity data.

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach., QC 20191112

QCR 20190520

QCR 20190520

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach., QC 20191112

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach., QC 20191112

Quantum pseudorandomness, also known as unitary designs, comprises a powerful resource for emergent quantum technologies. Although in theory pseudorandom unitary operators can be constructed efficiently, realizing these objects in realistic physical systems is a challenging task. Here, we demonstrate experimental generation and detection of quantum pseudorandomness on a 12-qubit nuclear magnetic resonance system. We first apply random sequences to the interacting nuclear spins, leading to random quantum evolutions that can quickly form unitary designs. Then, in order to probe the growth of quantum pseudorandomness during the time evolutions, we propose the idea of using the system's multiple-quantum coherence distribution as an indicator. Based on this indicator, we measure the spreading of quantum coherences and find that substantial quantum pseudorandomness has been achieved at the 12-qubit scale. This may open up a path to experimentally explore quantum randomness on forthcoming large-scale quantum processors. © 2019 American Physical Society.

Quantum state tomography is a daunting challenge of experimental quantum computing, even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine-learning method to recover the ground states of k-local Hamiltonians from just the local information, where a fully connected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4-qubit nuclear magnetic resonance system our method yields global states via the 2-local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems.

Quantum state tomography is a daunting challenge of experimental quantum computing, even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine-learning method to recover the ground states of k-local Hamiltonians from just the local information, where a fully connected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4-qubit nuclear magnetic resonance system our method yields global states via the 2-local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems.

Quantum pseudorandomness, also known as unitary designs, comprises a powerful resource for emergent quantum technologies. Although in theory pseudorandom unitary operators can be constructed efficiently, realizing these objects in realistic physical systems is a challenging task. Here, we demonstrate experimental generation and detection of quantum pseudorandomness on a 12-qubit nuclear magnetic resonance system. We first apply random sequences to the interacting nuclear spins, leading to random quantum evolutions that can quickly form unitary designs. Then, in order to probe the growth of quantum pseudorandomness during the time evolutions, we propose the idea of using the system's multiple-quantum coherence distribution as an indicator. Based on this indicator, we measure the spreading of quantum coherences and find that substantial quantum pseudorandomness has been achieved at the 12-qubit scale. This may open up a path to experimentally explore quantum randomness on forthcoming large-scale quantum processors. © 2019 American Physical Society.

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach., QC 20191112

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach., QC 20191112

We present an electrically tunable terahertz (THz) spatial phase shifter (SPS) based on liquid crystal (LC). An integrated structure was proposed, consisting of two LC layers (with horizontal and vertical grating electrode respectively) and a 45 linear polarization plate in the middle. The performance of this device provides actively controllable spatial phase modulation capabilities, which has many potential applications. Moreover, one application as adaptive THz lens was also implemented. The initial measurement results show good focusing capacity and adjustable focal length.

We present an electrically tunable terahertz (THz) spatial phase shifter (SPS) based on liquid crystal (LC). An integrated structure was proposed, consisting of two LC layers (with horizontal and vertical grating electrode respectively) and a 45 linear polarization plate in the middle. The performance of this device provides actively controllable spatial phase modulation capabilities, which has many potential applications. Moreover, one application as adaptive THz lens was also implemented. The initial measurement results show good focusing capacity and adjustable focal length.

Hao Xu, Shangchen Xu, Rui Zhang, Tao Xin, Qi Pang Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan 250021, Shandong, China Purpose: SIL1 is a ubiquitous protein localized to the endoplasmic reticulum and functions as a cochaperone of BiP. Previous studies have shown that function loss of SIL1 is often associated with neurological diseases, such as Marinesco-Sjögren Syndrome. However, no studies have investigated the function of SIL1 in tumors. In this study we aim to reveal functions of SIL1 and the underlying mechanisms in glioma. Materials and methods: First, by searching on Gene Expression Profiling Interactive Analysis, we examined SIL1 expression and prognostic value in glioblastoma multiforme (GBM) and brain lower grade glioma (LGG). Immunohistochemical analysis (IHC) was also performed to determine the endogenic SIL1 level. Cell counting kit-8 (CCK8) and clone formation assays were used to detect cell proliferation of U251 cells. Cell migration was detected by transwell assay and cell cycle and apoptosis were detected by flow cytometry. Western blot was performed to determine protein expression. Results: We found that the expression of SIL1 was increased by approximately 1.5-fold in GBM and 1.3-fold in LGG compared with normal controls (P<0.05) and negatively correlated with patients’ survival. IHC revealed that SIL1 expression was significantly higher in glioma tissues than that in paracancerous tissues (P<0.05). Glioma patients with high SIL1 expression accounted for 65.79% (25/38) of total samples and SIL1 expression significantly increased in grade IV glioma compared to grades I–III (P=0.026). Suppression of SIL1 expression led to significant inhibition of U251 cell proliferation. Transwell assay showed that cell migration of U251 was significantly inhibited by siSIL transfection, with an inhibitory rate reaching 69%. Flow cytometry detection showed that siSIL1 cou

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate.

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements. © 2017 American Physical Society.

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements. © 2017 American Physical Society.

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate.

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate.