Advanced quantum technologies最新文献

Quantum architecture search (QAS) has attracted increasing attention owing to its remarkable ability to automate the design of quantum circuits for variational quantum algorithms (VQAs). However, evaluating the performance of numerous quantum circuits is essential to provide feedback for the search strategy, which inevitably renders QAS computationally expensive. Performance predictors have emerged as highly efficient evaluation methods to mitigate this challenge. However, the performance predictor faces a critical challenge in reducing the required number of circuit-performance pairs for training. This study encodes circuit architecture by representing a quantum circuit as a relational graph that emphasizes message exchange. Subsequently, valuable information about circuit architecture is extracted through three types of graph measures, including distance-based, degree-based, and cluster-based measures. The graph measures define a smooth space related to circuit performance, facilitating the training of the performance predictor. The effectiveness of the proposed method is assessed across three tasks within variational quantum eigensolvers (VQE): identifying the ground states of the Transverse Field Ising Model (TFIM), the Heisenberg model, and the $�{\text{BeH}}_2$ molecule. The simulation results demonstrate notable enhancements in predictive accuracy achieved by our method, coupled with a substantial reduction in the required number of training samples for the predictor.

A scheme for quantum batteries consisting of an atom-cavity interacting system under a structured reservoir in the non-Markovian regime is proposed. A multi-parameter regime for the cavity-reservoir coupling and reveal how these parameters affect the performance of the quantum battery is investigated. Specifically, how two distinct types of environments impact the charging process of the quantum battery from a memory perspective is examined. The findings indicate that in a memory-influenced environment, both the stored energy and the extractable work are higher compared to when the cavity interacts with a memory-less environment. This proposed scheme is simple and may be achievable for realization and implementation.

Two cavity-magnon subsystems coupled via the two single-mode cavities mediated by a non-degenerate parametric down conversion and each cavity carrying a magnon confined in a Yttrium-iron-garnet sphere is proposed to study one-way quantum steering and asymmetric tripartite entanglement. The entanglement can be transferred from the two microwave cavities to the two separated magnon modes using magnetic dipole interaction. Different from previous schemes, the present study demonstrates efficient realization of controllable one-way quantum steering between two magnon modes through asymmetric frequency detunings of the two magnon modes. In addition, an asymmetric tripartite entanglement can also be achieved. Furthermore, the system exhibits robustness to temperatures up to 100 mK, providing a promising avenue for utilizing cavity magnonics systems in unidirectional transmission of quantum information.

A nonlocal quantum approach is presented to polarimetry, leveraging the phenomenon of entanglement in photon pairs to enhance the precision in sample property determination. By employing two distinct channels, one containing the sample of interest and the other serving as a reference, the conditions are explored under which the inherent correlation between entangled photons can increase measurement sensitivity. Specifically, the quantum Fisher information (QFI) is calculated and compare the accuracy and sensitivity for the cases of single sample channel versus two channel quantum state tomography measurements. The theoretical results are verified by experimental analysis. The theoretical and experimental framework demonstrates that the nonlocal strategy enables enhanced precision and accuracy in extracting information about sample characteristics more than the local measurements. Depending on the chosen estimators and noise channels present, theoretical and experimental results show that noise-induced bias decreases the precision for the estimated parameter. Such a quantum-enhanced nonlocal polarimetry holds promise for advancing diverse fields including material science, biomedical imaging, and remote sensing, via high-precision measurements through quantum entanglement.

Light-matter interactions provide an ideal testground for interplay of critical phenomena, topological transitions, quantum metrology, and non-Hermitian physics with high controllability and tunability. The present work considers two fundamental non-Hermitian Jaynes-Cummings models in light-matter interactions that possess real energy spectra in parity-time (PT) symmetry and anti-PT symmetry. The quantum Fisher information is shown to be critical around the transitions at the exceptional points and exhibit a super universality, with respect to different parameters, all energy levels, both models, symmetric phases, and symmetry-broken phases, which guarantees a universally high measurement precision in quantum metrology. In particular, the transitions are found to be both symmetry-breaking Landau-class transitions (LCTs) and symmetry-protected topological-class transitions (TCTs), thus realizing a simultaneous occurrence of critical LCTs and TCTs that are conventionally incompatible due to contrary symmetry requirements. Besides establishing a paradigmatic case to break the incompatibility of the LCTs and the TCTs in non-Hermitian systems, the both availabilities of the sensitive critical feature and the robust topological feature can also provide more potential for designing quantum devices or sensors.

The measurement statistics for spatial and temporal quantum processes are produced through distinct mechanisms. Measurements that are space-like separated exhibit non-signaling behavior. However, time-like separated measurements can only result in one-way non-signaling, as the past is independent of the future, but the opposite is not true. This work presents the doubled-density operator as a comprehensive framework for studying quantum processes in space-time. It effectively captures all the physical information of the process, with the measurement and Born rule showing uniformity for both spatial and temporal cases. It is demonstrated that the equal-time density operator can be derived by performing a partial trace operation on the doubled-density operator. Furthermore, the temporality of the quantum process can be detected by conducting a partial trace operation on either the left or right half of the doubled-density operator.

This work presents an innovative approach for measuring the spin polarizations of coupled atomic ensembles in spin-exchange relaxation-free (SERF) comagnetometers, using the phase-frequency response of the magnetic field. The zero-phase point in the phase-frequency response of the magnetic field along the $�y$-axis is examined to determine the deceleration factor and electronic magnetic field. Ultimately, the spin polarizations of electrons and noble-gas atoms are calculated. The method is applied to test vapor cells with different parameters under various temperatures and pumping light intensities. The measurement errors caused by transverse electron relaxation of electronic spin polarization and nuclear spin polarization are $�\approx$5.59% and 1.95% under high polarization, and 8.06% and 2.62% under low polarization. The measurement method features minimal impact on nuclear spin polarization and wide applicability compared to other methods, making it more applicable and suitable for SERF comagnetometers. This method has great significance in better understanding the system state of the SERF comagnetometer and improving its sensitivity.

This work proposes a novel method for in situ measurement of the non-orthogonal angles of magnetic field coils based on single-beam magnetic resonance. The orthogonality of magnetic field coils is crucial for ensuring the accuracy of atomic magnetometers. The theoretical principles are derived to establish a measurement method for the non-orthogonal angles. The proposed method requires only one single beam of light to perform in situ measurements of non-orthogonal angles in the YZ and XZ directions. The experimental results demonstrate that the angle between the Y and Z coils is 89.3001 ± 0.0045°, a value that remains stable despite variations in experimental parameters such as temperature and light power, thereby confirming the robustness of the method. Additionally, the measured gyromagnetic ratio is 6.9963 ± 0.0005 Hz nT⁻¹, which closely matches the theoretical value of 6.9958 Hz nT⁻¹, further validating the method's accuracy. The proposed method provides a more straightforward approach and has significant potential for widespread application across various atomic magnetometers, especially in miniaturized configurations. Furthermore, this method may be extended to the field of atomic gyroscopes.

Text classification, a key process in natural language processing (NLP), relies on the k-nearest neighbors (KNN) algorithm for its simplicity and effectiveness. Traditional methods often grapple with the high-dimensional nature of textual data, leading to substantial computational demands. This study introduces a novel classical quantum k-nearest neighbors (CQKNN) algorithm, which integrates quantum circuits into a conventional machine-learning framework to enhance computational efficiency and reduce storage requirements. This hybrid approach uses a unified quantum circuit that simplifies multiple similarity calculations through mid-circuit measurements and qubit reset operations, significantly improving upon traditional multi-circuit quantum k-nearest neighbors (QKNN) models. The CQKNN algorithm, tested on datasets such as SMS Spam Collection, Twitter US Airline Sentiment, and IMDB Movie Reviews, not only outperforms classical KNN but also addresses challenges posed by noisy intermediate-scale quantum (NISQ) devices through advanced error mitigation techniques. This work highlights resource efficiency and reduced gate complexity and demonstrates the practical application of fidelity in quantum similarity calculations, setting new standards for quantum-enhanced machine learning and advancing current quantum technology capabilities in complex data classification tasks.