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Summary: Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2×$$ 2\times $$ to 4×$$ 4\times $$ more energy efficient than their classical counterparts; why feedback‐based quantum optimization is energy‐inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥$$ \ge $$20×$$ \times $$. Finally, we use the NchooseK high‐level programming model to run optimization problems on both gate‐based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate‐based counterparts. [ABSTRACT FROM AUTHOR]

In the ultra-wideband indoor positioning sceneraio, the non-line of sight (NLOS) propagation may be caused by obstacles, which may lead to the deviation of ranging value and affect the positioning precision. Therefore, we propose a NLOS identification and error regression positioning algorithm based on light gradient boosting machine (LightGBM). Firstly, ReliefF algorithm combined with Spearman correlation coefficient is used to analyze the feature correlation, and eight channel features such as total channel impulse response power and standard deviation of noise are selected as NLOS identification features. Then, we adopt genetic algorithm to optimize the hyperparameters of LightGBM for NLOS identification. On this basis, the proposed error regression model based on convolutional neural network (CNN) combined with LightGBM is used to correct the ranging results, so as to achieve high-precision positioning. Through the verification on the public dataset, the NLOS identification accuracy reached 91.8%, and the positioning precision is improved by 45 cm after correcting the ranging results. [ABSTRACT FROM AUTHOR]

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover's algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator's results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the 99 % of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement. These results have been calculated for the IBM quantum computers, but both the tool and the analysis can be easily extended to any other quantum computer. [ABSTRACT FROM AUTHOR]

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation. [ABSTRACT FROM AUTHOR]

In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system's resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments. [ABSTRACT FROM AUTHOR]

Abstract The effective use of noisy intermediate‐scale quantum devices requires error mitigation to improve the accuracy of sampled measurement distributions. The more accurately the effects of noise on these distributions can be modeled, the more closely error mitigation will be able to approach theoretical bounds. The characterization of noisy quantum channels and the inference of their effects on general observables are challenging problems, but in many cases a change in representation can greatly simplify the analysis. Here, spin Wigner functions for multiqudit systems are investigated. This study generalizes previous kernel constructions, capturing the effects of several probabilistic unitary noise models in few parameters.

Background Assisted reproductive technology (ART) has positively impacted the field of human reproduction. Over the years, ART procedures have evolved to include several complex techniques, associated with various risks of errors and failure opportunities, especially in laboratories. IVF-associated errors, although rare, have significant implications. Patients may be psychologically affected, given the emotional attachment associated with IVF treatments. Most of these errors are associated with humans and/or systemic failure. Methods In this article, we used the Google Scholar database to search for related peer-reviewed original and review articles. Search keywords were "IVF laboratory", "the embryologist", "laboratory error", "IVF laboratory errors", "IVF error prevalence", "risks of IVF error", "consequences/ repercussion, IVF errors", "risk assessment", and "risk mitigation". All studies were extensively evaluated. Findings There is a need for an effective approach toward improving existing risk management systems and, possibly, developing better risk management models that can eliminate these errors. Because laboratory resources (equipment and human expertise) are finite and are dependent on systemic policies, error mitigation must involve a multidimensional approach. This review includes several of these error-mitigating strategies as well as innovative technologies that may provide practical approaches to improve error surveillance, reporting, management, and potentially, eradication. Furthermore, errors in IVF laboratories threaten the integrity of the IVF processes and pose significant implications, which are often negative. Conclusion This review highlights those errors and the stages they occur during the IVF process. Recommendations Understanding the risks of errors in IVF laboratories can help embryologists develop better approaches to identify, evaluate the causes, and prevent errors in the laboratory. Essentially, the practice of effective risks assessment and management can help reinforce patients' trust in the clinics and prevent repercussions such as litigations and many more. More laboratories can also begin reporting even minor errors to curb the scarce data in this subject. [ABSTRACT FROM AUTHOR]

With the development of noisy intermediate-scale quantum machines, quantum processors show their supremacy in specific applications. To better understand the quantum behavior and verify larger quantum bit (qubit) algorithms, simulation on classical computers becomes crucial. However, as the simulated number of qubits increases, the full-state simulation suffers exponential memory increment for state vector storing. In order to compress the state vector, some existing works reduce the memory by data encoding compressors. Nevertheless, the memory requirement remains massive. Meanwhile, others utilize compact decision diagrams (DD) to represent the state vector, which only demands linear memory size. However, the existing DD-based simulation algorithm possesses many redundant calculations that require further exploration. Besides, the traditional normalization-based nodes merging method of DD amplifies the side influences of approximate error. Therefore, to tackle the above challenges, in this paper, we first fully explore the redundancies in the recursive-based DD simulation (RecurSim) algorithm. Inspired by the regularities of the quantum circuit model, a scale-based simulation (ScaleSim) algorithm is proposed, which removes plenty of unnecessary computations. Furthermore, to eliminate the influences of approximate error, we propose a new pre-check DD building method, namely PCB, which maintains the accuracy of DD representation and produces more memory saving. Comprehensive experiments show that our method achieves up to 24124.2 × acceleration and 3.2 × 10 7 × memory reduction than traditional DD-based methods on quantum algorithms while maintaining the representation accuracy. [ABSTRACT FROM AUTHOR]

Quantum secret sharing is a way to share secret messages among several people using the quantum channel with unconditional security. Hillery et al. (Phys Rev A 59(3):1829, 1999) proposed the first quantum secret sharing protocol for three parties by using the 3-qubit GHZ states and a possible generalization of the protocol for four parties by using the 4-qubit GHZ states. Also in the same paper, they proposed a three-party secret-sharing scheme to share quantum states. Later Xiao et al. (Phys Rev A 69(5):052307, 2004) generalized the quantum secret sharing protocol for n parties to share a classical secret message. In this paper, we implement these protocols in IBM simulators as well as real backends and check security against some quantum attacks. Also, we create a noise model and simulate the protocols using the noise model. Finally, we use the zero noise extrapolation method to mitigate errors due to noise. [ABSTRACT FROM AUTHOR]

Nowadays, the maritime sector strongly relies on global navigation satellite systems (GNSS) for the provision of position, navigation, and timing (PNT) information. The standard functionality of several ships' bridge instruments depends on such information, which becomes critical to have a safe and reliable navigation. Nevertheless, the possibility of GNSS outages combined with unintentional and intentional interference to GNSS signals, which have been increasing over the last years, can severely threaten the nominal activity of the crew onboard of vessels. For these reasons, alternative position, navigation, and timing (APNT) systems become fundamental in order to provide operational continuity. R(anging)-Mode is a terrestrial alternative system to GNSS for the maritime domain. In one of its possible implementation, it exploits synchronized medium frequency (MF) signals transmitted by maritime radio beacons. In the MF band, the radio waves are attenuated and distorted by the change of electrical properties of the ground along the propagation path. Moreover, these signals are affected by terrain elevation changes and large metallic infrastructures which induce an overall loss in the accuracy of the system. In this paper, we propose a mitigation technique based on the estimation of these additional error sources by using in-field range measurements. The theoretical details are explained in depth and the technique is validated with real data gathered on a measurement campaign. A clear and significant performance improvement is achieved on the range accuracy when the proposed approach is applied. [ABSTRACT FROM AUTHOR]

Objective: I examine John Senders' work and discuss his influence on the study of error causation,error mitigation, and sociotechnical system safety. Background: John Senders' passing calls for an evaluation of the impact of his work. Method: I review literature and accident investigation findings to discuss themes in Senders' work and potential associations between that work and error causation and system safety. Results: Senders consistently emphasized empirical rigor and theoretical exploration in his research, with the desire to apply that work to enhance human performance. He has contributed to changing the way error has been viewed, and to developing and implementing programs and techniques to mitigate error. While a causal relationship between Senders' work and safety cannot be established, an association can be drawn between his research and efforts to mitigate error. Conclusion: Because of Senders' work, we have a better understanding of error causation and enhanced ways of mitigating system errors. However, new sources of error, involving advanced systems and operators' knowledge and understanding of their functionalities can, if not addressed, degrade system safety. Application: Modifications to advanced automation and operator training are suggested, and research to improve operator expertise in interacting with automated systems proposed. [ABSTRACT FROM AUTHOR]

Abstract Background Assisted reproductive technology (ART) has positively impacted the field of human reproduction. Over the years, ART procedures have evolved to include several complex techniques, associated with various risks of errors and failure opportunities, especially in laboratories. IVF-associated errors, although rare, have significant implications. Patients may be psychologically affected, given the emotional attachment associated with IVF treatments. Most of these errors are associated with humans and/or systemic failure. Methods In this article, we used the Google Scholar database to search for related peer-reviewed original and review articles. Search keywords were “IVF laboratory”, “the embryologist”, “laboratory error”, “IVF laboratory errors”, “IVF error prevalence”, “risks of IVF error”, “consequences/ repercussion, IVF errors”, “risk assessment”, and “risk mitigation”. All studies were extensively evaluated. Findings There is a need for an effective approach toward improving existing risk management systems and, possibly, developing better risk management models that can eliminate these errors. Because laboratory resources (equipment and human expertise) are finite and are dependent on systemic policies, error mitigation must involve a multidimensional approach. This review includes several of these error-mitigating strategies as well as innovative technologies that may provide practical approaches to improve error surveillance, reporting, management, and potentially, eradication. Furthermore, errors in IVF laboratories threaten the integrity of the IVF processes and pose significant implications, which are often negative. Conclusion This review highlights those errors and the stages they occur during the IVF process. Recommendations Understanding the risks of errors in IVF laboratories can help embryologists develop better approaches to identify, evaluate the causes, and prevent errors in the laboratory. Essentially, the practice of effective risks assessment and management can help reinforce patients’ trust in the clinics and prevent repercussions such as litigations and many more. More laboratories can also begin reporting even minor errors to curb the scarce data in this subject.

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments.

Quantum networks consist of quantum nodes that are linked by entanglement, and quantum information can be transferred from one node to another. Operations can be applied to qubits of local nodes coordinated by classical communication to manipulate quantum states, and readout/measurement will be employed to obtain results. Here, we use quantum circuits to simulate quantum state transfer between two nodes connected in a linear geometry through other nodes. We explore the interplay between gate and readout errors on the performance of state transfer. We find that the nominal success probability is not necessarily a monotonic function of the two error rates and employ numerical simulations and analytic tools to understand their interplay. [ABSTRACT FROM AUTHOR]

Summary: The material point method (MPM) is a robust numerical simulation approach for continuum mechanics problems involving large material deformations coupled to changing surface topographies. These types of problems are present in many different engineering contexts, from understanding the failure processes of earthen slopes to predicting the strengths and failure mechanisms of body armor to modeling the impact forces of waves in fluid tanks. By using a set of persistent material point tracers to follow the motion and deformation of the continuum material while solving the equations of motion on a static simulation grid, the MPM avoids several shortcomings of more traditional numerical approaches including blurring of material surfaces — as in Eulerian finite element or finite volume methods (FEMs or FVMs) — and mesh tangling — as in Lagrangian FEMs. Despite its robustness, MPM is known to develop significant numerical errors: namely, (i) the particle ringing instability and (ii) solution dependent discretization and integration errors. In this work, we present an analysis of local‐in‐time, spatial integration errors in the MPM and several techniques designed to mitigate these errors. Error mitigation approaches previously described in the literature are compared to a new method we propose for problems involving very large material deformations. The proposed method is shown to offer substantial improvement over standard MPM for simulations of fluid‐like materials without requiring significant augmentation of existing MPM frameworks. [ABSTRACT FROM AUTHOR]

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the noisy-intermediate scale quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field. [ABSTRACT FROM AUTHOR]

Digital circuits for space applications can suffer from operation failures due to radiation effects. Error detection and mitigation techniques are widely accepted solutions to improve dependability of digital circuits under Single Event Upsets (SEUs) and Single Event Transients (SETs). These solutions imply design modifications that must be validated. This paper presents a formal verification method to prove that the applied fault tolerance techniques do actually prevent fault propagation as well as that the fault-tolerant circuit is functionally equivalent to the original version. The method has been implemented in an in-house software tool, VeriHard. It has been successfully applied to verify a wide variety of fault tolerance techniques, such as Triple Modular Redundancy (TMR), Duplication with Comparison (DwC), Safe Finite State Machines and Hamming encoding. Experimental results with benchmarks and industrial cases illustrates the capabilities of the method and its high performance.

Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning. [ABSTRACT FROM AUTHOR]

Qubit measurement is generally the most error-prone operation that degrades the performance of near-term quantum devices, and the exponential decay of readout fidelity severely impedes the development of large-scale quantum information processing. Given these disadvantages, we present a quantum state readout method, named compression readout, that naturally avoids large multi-qubit measurement errors by compressing the quantum state into a single qubit for measurement. Our method generally outperforms direct measurements in terms of accuracy, and the advantage grows with the system size. Moreover, because only one-qubit measurements are performed, our method requires solely a fine readout calibration on one qubit and is free of correlated measurement error, which drastically diminishes the demand for device calibration. These advantages suggest that our method can immediately boost the readout performance of near-term quantum devices and will greatly benefit the development of large-scale quantum computing. [ABSTRACT FROM AUTHOR]

We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the quantum approximate optimization algorithm, to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999 999 999 (nine nines) upon a subsequent optimization by GRadient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.

A procedure for defining virtual spaces, and the periodic one-electron and two-electron integrals, for plane-wave second quantized Hamiltonians has been developed, and it was validated using full configuration interaction (FCI) calculations, as well as executions of variational quantum eigensolver (VQE) circuits on Quantinuum's ion trap quantum computers accessed through Microsoft's Azure Quantum service. This work is an extension to periodic systems of a new class of algorithms in which the virtual spaces were generated by optimizing orbitals from small pairwise CI Hamiltonians, which we term as correlation optimized virtual orbitals with the abbreviation COVOs. In this extension, the integration of the first Brillouin zone is automatically incorporated into the two-electron integrals. With these procedures, we have been able to derive virtual spaces, containing only a few orbitals, that were able to capture a significant amount of correlation. The focus in this manuscript is on comparing the simulations of small molecules calculated with plane-wave basis sets with large periodic unit cells at the Γ -point, including images, to results for plane-wave basis sets with aperiodic unit cells. The results for this approach were promising, as we were able to obtain good agreement between periodic and aperiodic results for an LiH molecule. Calculations performed on the Quantinuum H1-1 quantum computer produced surprisingly good energies, in which the error mitigation played a small role in the quantum hardware calculations and the (noisy) quantum simulator results. Using a modest number of circuit runs (500 shots), we reproduced the FCI values for the 1 COVO Hamiltonian with an error of 11 milliHartree, which is expected to improve with a larger number of circuit runs. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) and global sensitivity analysis (GSA) are applied to quantum computing hardware to evaluate imperfect, noisy quantum hardware to provide insight on the sources of uncertainty associated with gate operations in the light of estimates of quantum state probability outputs from a circuit. UQ is first applied to a quantum circuit to quantify the uncertainties of gate operators based upon measurements of the circuit's quantum state probability. This uncertainty is combined with GSA to determine (1) the most influential gate angle parameter or parameter combination (based upon first-order and higher-order sensitivity indices) that provides the highest contribution controlling the circuit output, (2) the least or non-influential gate angle parameters on the circuit output. We apply the method to a Gauss quadrature numerical integration quantum circuit that is implemented on IBM quantum hardware. We identify one quantum gate angle that has a sufficiently large first-order effect, but practically no higher- order effects and minimal parameter correlation. This provides a simplified tuning strategy that allows independent parameter adjustments to enhance the accuracy of the calculation. Significant enhancement in numerical integration is shown via hardware validation which results in a reduction of error from 3.74% using nominal gate angle parameters to 0.42% using the compensated gate angle that is guided by the UQ-GSA results. [ABSTRACT FROM AUTHOR]

Currently available quantum computers are prone to errors. Circuit optimization and error mitigation methods are needed to design quantum circuits to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling (DD) is generally used to suppress the decoherence error, and different DD strategies have been proposed. Moreover, the circuit fidelity can be improved by pulse-level optimization, such as creating hardware-native pulse-efficient gates. This article implements all the popular DD sequences and evaluates their performances on IBM quantum chips with different characteristics for various well-known quantum applications. Also, we investigate combining DD with the pulse-level optimization method and apply them to QAOA to solve the max-cut problem. Based on the experimental results, we find that DD can be a benefit for only certain types of quantum algorithms, while the combination of DD and pulse-level optimization methods always has a positive impact. Finally, we provide several guidelines for users to learn how to use these noise mitigation methods to build circuits for quantum applications with high fidelity on IBM quantum computers.

The real-time segmentation of sidewalk environments is critical to achieving autonomous navigation for robotic wheelchairs in urban territories. A robust and real-time video semantic segmentation offers an apt solution for advanced visual perception in such complex domains. The key to this proposition is to have a method with lightweight flow estimations and reliable feature extractions. We address this by selecting an approach based on recent trends in video segmentation. Although these approaches demonstrate efficient and cost-effective segmentation performance in cross-domain implementations, they require additional procedures to put their striking characteristics into practical use. We use our method for developing a visual perception technique to perform in urban sidewalk environments for the robotic wheelchair. We generate a collection of synthetic scenes in a blending target distribution to train and validate our approach. Experimental results show that our method improves prediction accuracy on our benchmark with tolerable loss of speed and without additional overhead. Overall, our technique serves as a reference to transfer and develop perception algorithms for any cross-domain visual perception applications with less downtime. [ABSTRACT FROM AUTHOR]

Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices. [ABSTRACT FROM AUTHOR]

Many-body physics is one very well suited field for testing quantum algorithms and for finding working heuristics on present quantum computers. We have investigated the non-equilibrium dynamics of one- and two-electron systems, which are coupled to an environment that introduces decoherence and dissipation. In our approach, the electronic system is represented in the framework of a two-site Hubbard model while the environment is modelled by a spin bath. To simulate the non-equilibrium population probabilities of the different states on a quantum computer we have encoded the electronic states and environmental degrees of freedom into qubits and ancilla qubits (bath), respectively. The total evolution time was divided into short time intervals, during which the system evolves. After each of these time steps, the system interacts with ancilla qubits representing the bath in thermal equilibrium. We have specifically studied spin baths leading to both, unital and non-unital dynamics of the electronic system and have found that electron correlations clearly enhance the electron transfer rates in the latter case. For short time periods, the simulation on the quantum computer is found to be in very good agreement with the exact results if error mitigation methods are applied. Our method to simulate also non-unitary time-evolution on a quantum computer can be well extended to simulate electronic systems in correlated spin baths as well as in bosonic and fermionic baths. [ABSTRACT FROM AUTHOR]

From Pharmacology to Cryptography and from Geology to Astronomy are some of the scientific fields in which Quantum Computing potentially will take off and fly high. Big Quantum Computing vendors invest a large amount of money in improving the hardware and they claim that soon enough a quantum program will be hundreds of thousands of times faster than a typical one we know nowadays. But still the reliability of such systems is the main obstacle. In this work, the reliability of real quantum devices is tested and techniques of noise and error correction are presented while measurement error mitigation is explored. In addition, a well-known string matching algorithm (Bernstein–Vazirani) was applied to the real quantum computing device in order to measure its accuracy and reliability. Simulated environments were also used in order to evaluate the results. The results obtained, even if these were not 100% accurate, are very promising which proves that even these days a quantum computer working side by side with a typical one is reliable and especially when error mitigation techniques are applied.

Deep neural networks (DNN) can be applied at the post-processing stage for the improvement of the results of quantum computations on noisy intermediate-scale quantum (NISQ) processors. Here, we propose a method based on this idea, which is most suitable for digital quantum simulation characterized by the periodic structure of quantum circuits consisting of Trotter steps. A key ingredient of our approach is that it does not require any data from a classical simulator at the training stage. The network is trained to transform data obtained from quantum hardware with artificially increased Trotter steps number (noise level) toward the data obtained without such an increase. The additional Trotter steps are fictitious, i.e., they contain negligibly small rotations and, in the absence of hardware imperfections, reduce essentially to the identity gates. This preserves, at the training stage, information about relevant quantum circuit features. Two particular examples are considered that are the dynamics of the transverse-field Ising chain and XY spin chain, which were implemented on two real five-qubit IBM Q processors. A significant error reduction is demonstrated as a result of the DNN application that allows us to effectively increase quantum circuit depth in terms of Trotter steps. [ABSTRACT FROM AUTHOR]

Quantum walks are an analog of classical random walks in quantum systems. Quantum walks have smaller hitting times compared to classical random walks on certain types of graphs, leading to a quantum advantage of quantum-walks-based algorithms. An important feature of quantum walks is that they are accompanied by the excitation transfer from one site to another, and a moment of hitting the destination site is characterized by the maximum probability amplitude of observing the excitation on this site. It is therefore prospective to consider such problems as candidates for quantum advantage demonstration, since gate errors can smear out a peak in the transfer probability as a function of time, nevertheless leaving it distinguishable. We investigate the influence of quantum noise on hitting time and fidelity of a typical quantum walk problem—a perfect state transfer (PST) over a qubit chain. We simulate dynamics of a single excitation over the chain of qubits in the presence of typical noises of a quantum processor (homogeneous and inhomogeneous Pauli noise, crosstalk noise, thermal relaxation, and dephasing noise). We find that Pauli noise mostly smears out a peak in the fidelity of excitation transfer, while crosstalks between qubits mostly affect the hitting time. Knowledge about these noise patterns allows us to propose an error mitigation procedure, which we use to refine the results of running the PST on a simulator of a noisy quantum processor. [ABSTRACT FROM AUTHOR]

A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (NISQ) computers and, perhaps, early fault tolerant (EFT) quantum computers. This motivates the development of many near-term quantum algorithms including robust amplitude estimation (RAE), which is a quantum-enhanced algorithm for estimating expectation values. One obstacle to using RAE has been a paucity of ways of getting realistic error models incorporated into this algorithm. So far the impact of device noise on RAE is incorporated into one of its subroutines as an exponential decay model, which is unrealistic for NISQ devices and, maybe, for EFT devices; this hinders the performance of RAE. Rather than trying to explicitly model realistic noise effects, which may be infeasible, we circumvent this obstacle by tailoring device noise using randomized compiling to generate an effective noise model, whose impact on RAE closely resembles that of the exponential decay model. Using noisy simulations, we show that our noise-tailored RAE algorithm is able to regain improvements in both bias and precision that are expected for RAE. Additionally, on IBM’s quantum computer ibmq_belem our algorithm demonstrates advantage over the standard estimation technique in reducing bias. Thus, our work extends the feasibility of RAE on NISQ computers, consequently bringing us one step closer towards achieving quantum advantage using these devices.

Despite the advent of Grover's algorithm for the unstructured search, its successful implementation on near-term quantum devices is still limited. We apply three strategies to reduce the errors associated with implementing quantum search algorithms. Our improved search algorithms have been implemented on the IBM quantum processors. Using them, we demonstrate three- and four-qubit search algorithm with higher average success probabilities compared to previous works. We present the successful execution of the five-qubit search on the IBM quantum processor for the first time. The results have been benchmarked using degraded ratio, which is the ratio between the experimental and the theoretical success probabilities. The fast decay of the degraded ratio supports our divide-and-conquer strategy. Our proposed strategies are also useful for implementation of quantum search algorithms in the post-NISQ era. [ABSTRACT FROM AUTHOR]

This letter proposes a low-cost non-line-of-sight (NLOS) identification and mitigation technique for ultra-wideband (UWB) ranging in both static and dynamic environments. Unlike conventional ranging that employs one mobile tag and a single fixed anchor at a known location, this technique utilizes two anchors and one tag. By utilizing Fresnel zones, this approach is effective in variable environments without requiring large quantities of prior knowledge. The proposed method was evaluated using simulations and experiments, achieving an identification accuracy of 96.41%. After error mitigation, ranging errors were reduced by 88.80%, 69.91%, and 96.12% in line-of-sight (LOS), NLOS, and dynamic NLOS environments, respectively. [ABSTRACT FROM AUTHOR]

The real-time segmentation of sidewalk environments is critical to achieving autonomous navigation for robotic wheelchairs in urban territories. A robust and real-time video semantic segmentation offers an apt solution for advanced visual perception in such complex domains. The key to this proposition is to have a method with lightweight flow estimations and reliable feature extractions. We address this by selecting an approach based on recent trends in video segmentation. Although these approaches demonstrate efficient and cost-effective segmentation performance in cross-domain implementations, they require additional procedures to put their striking characteristics into practical use. We use our method for developing a visual perception technique to perform in urban sidewalk environments for the robotic wheelchair. We generate a collection of synthetic scenes in a blending target distribution to train and validate our approach. Experimental results show that our method improves prediction accuracy on our benchmark with tolerable loss of speed and without additional overhead. Overall, our technique serves as a reference to transfer and develop perception algorithms for any cross-domain visual perception applications with less downtime.

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

Digital quantum simulation uses the capabilities of quantum computers to determine the dynamics of quantum systems, which are beyond the computability of modern classical computers. A notoriously challenging task in this field is the description of non-equilibrium dynamics in quantum many-body systems. Here, we use the IBM quantum computers to simulate the non-equilibrium dynamics of few spin and fermionic systems. Our results reveal that with a combination of error mitigation, noise extrapolation and optimized initial state preparation, one can tackle the most important drawbacks of modern quantum devices. The systems we simulate demonstrate the potential for large-scale quantum simulations of light–matter interactions in the near future. [ABSTRACT FROM AUTHOR]

In correctly predicting that selection efficiency is positively correlated with the effective population size (N e), the nearly neutral theory provides a coherent understanding of between-species variation in numerous genomic parameters, including heritable error (germline mutation) rates. Does the same theory also explain variation in phenotypic error rates and in abundance of error mitigation mechanisms? Translational read-through provides a model to investigate both issues as it is common, mostly nonadaptive, and has good proxy for rate (TAA being the least leaky stop codon) and potential error mitigation via "fail-safe" 3′ additional stop codons (ASCs). Prior theory of translational read-through has suggested that when population sizes are high, weak selection for local mitigation can be effective thus predicting a positive correlation between ASC enrichment and N e. Contra to prediction, we find that ASC enrichment is not correlated with N e. ASC enrichment, although highly phylogenetically patchy, is, however, more common both in unicellular species and in genes expressed in unicellular modes in multicellular species. By contrast, N e does positively correlate with TAA enrichment. These results imply that local phenotypic error rates, not local mitigation rates, are consistent with a drift barrier/nearly neutral model. [ABSTRACT FROM AUTHOR]

Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices.

This article proposes a software error mitigation approach that uses the single instruction multiple data (SIMD) coprocessor to accelerate computation over redundant data. In addition, an external IP connected to the microprocessor’s trace interface is used to detect errors that are difficult to cover with software-implemented techniques. The proposed approach has been implemented in an ARM microprocessor, and an irradiation campaign with neutrons has been carried out at Los Alamos National Laboratory. Experimental results demonstrate the high error coverage (more than 99.9%) of the proposed approach. The neutron cross section of errors that were not corrected nor detected was reduced by more than three orders of magnitude. [ABSTRACT FROM AUTHOR]

Developing of failsafe systems is the problem solving in different sectors. Hardware design is not an exception. This sector imposes additional requirements constructing failover controllers, such as chip area and energy consumption. This article provides types and causes of errors to define their effect on SoC and offers the general idea of errors resilient SoC that consists of error detecting, error fixing and error mitigating mechanism. Also in the paper reconfiguration is considered as the error mitigation mechanism and methods used in practice to implement reconfiguration are presented. Before producing controller or other IP-block with error mitigation, first of all it is necessary to assess possible variants of failure and ways to improve its failsafe. Therefore methods of failure assessments are presented. As the result - Markov chain is chosen as the assessment tool and example of constructing of Markov chain for not reconfigurable and reconfigurable controller of transport layer protocol is presented. Comparative analyses of the results are carried out.