Quantum Correlation Sharing: A Review On Recent Progress From Nonlocality To Other Non-Classical Correlations

This review offers a comprehensive exploration and synthesis of recent advancements in the domain of quantum correlation sharing facilitated through sequential measurements. We initiate our inquiry by delving into the interpretation of the joint probability, laying the foundation for an examination of quantum correlations within the context of specific measurement methods. The subsequent section meticulously explores nonlocal sharing under diverse measurement strategies and scenarios, with a specific focus on investigating the impact of these strategies on the dissemination of quantum nonlocality. Key perspectives such as "asymmetry" and "weak value" are scrutinized through detailed analyses across various scenarios, allowing us to evaluate the potential of nonlocality sharing. We also provide a retrospective overview of experimental endeavors associated with this phenomenon. The third part of our exploration presents research findings on steering sharing, offering clarity on the feasibility of steering sharing and summarizing the distinctive properties of quantum steering sharing in different scenarios. Continuing our journey, the fourth section delves into discussions on the sharing of diverse quantum correlations, encompassing network nonlocality, quantum entanglement, and quantum contextuality. Moving forward, the fifth section conducts a comprehensive review of the progress in the application of quantum correlation sharing, specifically based on sequential measurement strategies. Applications such as quantum random access coding, random number generation, and self-testing tasks are highlighted. Finally, we discuss and list some of the key unresolved issues in this research field, and conclude the entire article

Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed

Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive Knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4-5 times faster) with acceptable performance drop (2-3\%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.Comment: 11 pages, 5 figures, accepted by AAAI2

Investigation on hydrodynamic characteristics of a hydrofoil based on γ-Re_θttransition model

The complex flow field caused by the dynamic stall can affect the operational stability of hydrodynamic machinery. In this paper, the NACA0009 blunt trailing edge hydrofoil is used as the object of study, and the dynamic stall characteristics of the hydrofoil are investigated by using the transition model and the dynamic mesh method. It is found that the hydrofoil deep stall calculated by the transition model is delayed compared to that calculated without the transition model. The hydrofoil dynamic stall can be divided into four stages, initial stage, development stage, stall inception stage and deep stall stage. In the initial stage and the development stage, the lift and drag characteristics are influenced by the shedding vortex. In the stall inception stage and the deep stall stage, the lift and drag characteristics are influenced by the leading edge separation vortex and the trailing edge vortex. The increase of angular velocity and Reynolds number of the dynamic hydrofoil delay the onset of the deep stall while accelerating the boundary layer transition. The research in this paper has a certain guiding effect for the safe and stable operation of hydrodynamic machinery.</p

Wide input-voltage range boost three-level DC-DC converter with quasi-Z source for fuel cell vehicles

To solve the problem of the mismatched voltage levels between the dynamic lower voltage of the fuel cell stack and the required constant higher voltage (400V) of the DC link bus of the inverter for fuel cell vehicles, a Boost three-level DC-DC converter with a diode rectification quasi-Z source (BTL-DRqZ) is presented in this paper, based on the conventional flying-capacitor Boost three-level DC-DC converter. The operating principle of a wide range voltage-gain for this topology is discussed according to the effective switching states of the converter and the multi-loop energy communication characteristic of the DRqZ source. The relationship between the quasi-Z source net capacitor voltages, the modulation index and the output voltage, is deduced and then the static and dynamic self-balance principle of the flying-capacitor voltage is presented. Furthermore, a Boost three-level DC-DC converter with a synchronous rectification quasi-Z source (BTL-SRqZ) is additionally proposed to improve the conversion efficiency. Finally, a scale-down 1.2 kW BTL-SRqZ prototype has been created, and the maximum efficiency is improved up to 95.66% by using synchronous rectification. The experimental results validate the feasibility of the proposed topology and the correctness of its operating principles. It is suitable for the fuel cell vehicles