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Abstract Quantum metrology promises unprecedented precision of parameter estimation, but it is often vulnerable to noise. While significant efforts have been devoted to improving the metrology performance in Markovian environments, practical control schemes specifically designed for non-Markovian noises are much less investigated. Here, we propose two control-enhanced quantum metrology schemes that are suitable for tackling general non-Markovian noises described by noise channels or noise spectra. We conduct experiments to verify the efficacy of these schemes on a nuclear magnetic resonance system. The experimental results involving multiqubit probes show that the parameter estimation precision can be greatly improved, significantly surpassing the standard quantum limit, with our schemes. At present, non-Markovian noises are widely encountered on diverse quantum devices, the proposed schemes are relevant for realistic metrology applications on these platforms.

With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations.

Abstract Noise is ubiquitous in real quantum systems, leading to non-Hermitian quantum dynamics, and may affect the fundamental states of matter. Here we report in an experiment a quantum simulation of the two-dimensional non-Hermitian quantum anomalous Hall (QAH) model using the nuclear magnetic resonance processor. Unlike the usual experiments using auxiliary qubits, we develop a stochastic average approach based on the stochastic Schrödinger equation to realize the non-Hermitian dissipative quantum dynamics, which has advantages in saving the quantum simulation sources and simplifying the implementation of quantum gates. We demonstrate the stability of dynamical topology against weak noise and observe two types of dynamical topological transitions driven by strong noise. Moreover, a region where the emergent topology is always robust regardless of the noise strength is observed. Our work shows a feasible quantum simulation approach for dissipative quantum dynamics with stochastic Schrödinger equation and opens a route to investigate non-Hermitian dynamical topological physics.

Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distribution (OOD) data and prompt human intervention, a critical competency. While several frameworks for OOD detection have been introduced and achieved remarkable results, most state-of-the-art (SOTA) models rely on supervised learning with annotated data for their training. However, acquiring labeled data can be a demanding, time-consuming or, in some cases, an infeasible task. Consequently, unsupervised learning has gained substantial traction and has made noteworthy advancements. It empowers models to undergo training solely on unlabeled data while still achieving comparable or even superior performance compared to supervised alternatives. Among the array of unsupervised methods, contrastive learning has asserted its effectiveness in feature extraction for a variety of downstream tasks. Conversely, auto-encoders are extensively employed to acquire indispensable representations that faithfully reconstruct input data. In this study, we introduce a novel approach that amalgamates contrastive learning with auto-encoders for OOD detection using unlabeled data. Contrastive learning diligently tightens the grouping of in-distribution data while meticulously segregating OOD data, and the auto-encoder augments the feature space with increased refinement. Within this framework, data undergo implicit classification into in-distribution and OOD categories with a notable degree of precision. Our experimental findings manifest that this method surpasses most of the existing detectors reliant on unlabeled data or even labeled data. By incorporating an auto-encoder into an unsupervised learning framework and training it on the CIFAR-100 dataset, our model enhances the detection rate of unsupervised learning methods by an average of 5.8%. Moreover, it outperforms the supervised-based OOD detector by an average margin of 11%.

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.

In the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only measures single-qubit observables. It is applicable on both time-independent and time-dependent Hamiltonians and can simultaneously capture the magnitude and sign of Hamiltonian parameters. Taking the Hamiltonians with the nearest-neighbor interactions as numerical examples, we trained our recurrent neural networks to learn different types of Hamiltonians with high accuracy. The study also shows that our method has good robustness against the measurement noise and decoherence effect. Therefore, it has widespread applications in estimating the parameters of quantum devices and characterizing the Hamiltonian-based quantum dynamics.

Quantum computation provides great speedup over its classical counterpart for certain tasks. Spin system is one of the most important candidates to realize quantum computations. The initialization, readout and quantum gate operations of spin-qubits can be accomplished by advanced spin resonance techniques, which include nuclear magnetic resonance, electron paramagnetic resonance and optically detected magnetic resonance. This review aims to summarize recent experimental progresses in spin-based quantum computing. Preserving quantum states, precise quantum operations, quantum algorithms and quantum simulations on spin systems are reviewed. The fast improvement of quantum technology motivated by quantum computations results in recent progress in spin magnetic resonance in nanoscale. This review also introduces the achievements in nanoscale magnetic resonance with Nitrogen vacancy centers in diamonds.

The Leggett–Garg (LG) test of macroscopic realism involves a series of dichotomic non-invasive measurements that are used to calculate a function which has a fixed upper bound for a macrorealistic system and a larger upper bound for a quantum system. The quantum upper bound depends on both the details of the measurement and the dimension of the system. Here we present an LG experiment on a three-level quantum system, which produces a larger theoretical quantum upper bound than that of a two-level quantum system. The experiment is carried out in nuclear magnetic resonance and consists of the LG test as well as a test of the ideal assumptions associated with the experiment, such as measurement non-invasiveness. The non-invasive measurements are performed via the modified ideal negative result measurement scheme on a three-level system. Once these assumptions are tested, the violation becomes small, despite the fact that the LG value itself is large. Our results showcase the advantages of using the modified measurement scheme that can reach higher LG values, since these leave a larger margin for violating the inequality in the face of experimental imperfections.

Anyons, quasiparticles living in two-dimensional spaces with exotic exchange statistics, can serve as the fundamental units for fault-tolerant quantum computation. However, experimentally demonstrating anyonic statistics is a challenge due to the technical limitations of current experimental platforms. Here, we take a state perpetration approach to mimic anyons in the toric code using a seven-qubit nuclear magnetic resonance quantum simulator. Anyons are created by dynamically preparing the ground and excited states of a seven-qubit planar version of the toric code, and are subsequently braided along two distinct, but topologically equivalent paths. We observe that the phase acquired by the anyons is independent of the path, and coincides with the ideal theoretical predictions when decoherence and implementation errors are taken into account. As the first demonstration of the topological path independence of anyons, our experiment helps to study and exploit the anyonic properties towards the goal of building a topological quantum computer.

The ability to post-select the outcomes of an experiment is a useful theoretical concept and experimental tool. In the context of weak measurements, post-selection can lead to surprising results such as complex weak values outside the range of eigenvalues. Usually post-selection is realized by a projective measurement, which is hard to implement in ensemble systems such as NMR. We demonstrate the first experiment of a weak measurement with post-selection on an NMR quantum information processor. Our setup is used for measuring complex weak values and weak values outside the range of eigenvalues. The scheme for overcoming the problem of post-selection in an ensemble quantum computer is general and can be applied to any circuit-based implementation. This experiment paves the way for studying and exploiting post-selection and weak measurements in systems where projective measurements are hard to realize experimentally.