arXiv subjects

Yuquan Chen

Publications and source records attributed to Yuquan Chen.

At least 19 recordsLinked to original sources

arXiv2026

Hyperon-Nucleon Spectrometer

Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $\Lambda$ polarization puzzle, in which $\Lambda$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China.

physics.ins-det↗

arXiv2025

High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins

Physical reservoir computing provides a powerful machine learning paradigm that exploits nonlinear physical dynamics for efficient information processing. By incorporating quantum effects, quantum reservoir computing offers superior potential for machine learning applications, as quantum dynamics are exponentially costly to simulate classically. Here, we present a novel quantum reservoir computing approach based on correlated quantum spin systems, exploiting natural quantum many-body interactions to generate reservoir dynamics, thereby circumventing the practical challenges of deep quantum circuits. Our experimental implementation supports nontrivial quantum entanglement and exhibits sufficient dynamical complexity for high-performance machine learning. We achieve state-of-the-art performance in experiments on standard time-series benchmarks, reducing prediction error by 1 to 2 orders of magnitude compared to previous quantum reservoir experiments. In long-term weather forecasting, our 9-spin quantum reservoir delivers greater prediction accuracy than classical reservoirs with thousands of nodes. This represents the first experimental demonstration of quantum machine learning outperforming large-scale classical models on real-world tasks.

quant-ph↗

arXiv2024

Design of a large-scale superconducting dipole magnet for the CEE spectrometer

The CSR External-target Experiment (CEE) is a large-scale spectrometer under construction at the Heavy Ion Research Facility in Lanzhou (HIRFL) for studying the phase structure of nuclear matter at high baryon density and the equation of states of nuclear matter at supra-saturation densities. One of the key components is a large acceptance dipole magnet with a central field of 0.5 T and the homogeneity of 5% within a 1 m long, 1.2 m wide, and 0.9 m high aperture. Detectors will be installed within this aperture. An innovative design for the superconducting detector magnet is proposed that goes beyond the conventional approach. The magnet is designed as a coil-dominant type, with conductors discretized on a racetrack-shaped cross-section to generate the necessary fields. A warm iron yoke is used to enhance the central field and minimize the stray field. The magnet has overall dimensions of 3.4 meters in length, 2.7 meters in height, and 4.3 meters in width. The coils will be wound using a 19-strand rope cable comprised of 12 NbTi superconducting wires and 7 copper wires. The ratio of copper to superconductor of the cable is 6.9. The keel supports serve as the primary structural support for the coils to withstand the electromagnetic force. The coils will be indirectly cooled by liquid helium within three external helium vessels. To ensure reliable protection of the magnet during a quench, an active protection method combined with quench-back effect is employed. In this paper, we mainly present the detailed design of the magnetic field, structure, quench protection and cryostat for the spectrometer magnet.

physics.ins-det↗

arXiv2022

Iterative Gradient Ascent Pulse Engineering algorithm for quantum optimal control

Gradient ascent pulse engineering algorithm (GRAPE) is a typical method to solve quantum optimal control problems. However, it suffers from an exponential resource in computing the time evolution of quantum systems with the increasing number of qubits, which is a barrier for its application in large-qubit systems. To mitigate this issue, we propose an iterative GRAPE algorithm (iGRAPE) for preparing a desired quantum state, where the large-scale, resource-consuming optimization problem is decomposed into a set of lower-dimensional optimization subproblems by disentanglement operations. Consequently these subproblems can be solved in parallel with less computing resources. For physical platforms such as nuclear magnetic resonance (NMR) and superconducting quantum systems, we show that iGRAPE can provide up to 13-fold speedup over GRAPE when preparing desired quantum states in systems within 12 qubits. Using a four-qubit NMR system, we also experimentally verify the feasibility of the iGRAPE algorithm.

quant-ph↗

arXiv2022

Variational Quantum Metrology with Loschmidt Echo

By utilizing quantum mechanical effects, such as superposition and entanglement, quantum metrology promises higher precision than the classical strategies. It is, however, practically challenging to realize the quantum advantages. This is mainly due to the difficulties in engineering non-classical probe state and performing nontrivial measurement in practise, particularly with a large number of particles. Here we propose a scalable scheme with a symmetrical variational quantum circuit which, same as the Loschmidt echo, consists of a forward and a backward evolution. We show that in this scheme the quantum Fisher information, which quantifies the precision limit, can be efficiently obtained from a measurement signal of the Loschmidt echo. We experimentally implement the scheme on an ensemble of 10-spin quantum processor and successfully achieves a precision near the theoretical limit which outperforms the standard quantum limit with 12.4 dB. The scheme can be efficiently implemented on various noisy intermediate-scale quantum devices which provides a promising routine to demonstrate quantum advantages.

quant-ph↗

arXiv2022

Detection of arbitrary quantum correlations via synthesized quantum channels

Quantum correlations are key information about the structures and dynamics of quantum many-body systems. There are many types of high-order quantum correlations with different time orderings, but only a few of them are accessible to the existing detection methods. Recently, a quantum-sensing approach based on sequential weak measurement was proposed to selectively extract arbitrary types of correlations. However, its experimental implementation is still elusive. Here we demonstrate the extraction of arbitrary types of quantum correlations. We generalized the original weak measurement scheme to a protocol using synthesized quantum channels, which can be applied to more universal scenarios including both single and ensemble quantum systems. In this quantum channel method, various controls on the sensors are superimposed to select the sensor-target evolution along a specific path for measuring a desired quantum correlation. Using the versatility of nuclear magnetic resonance techniques, we successfully extract the second- and fourth-order correlations of a nuclear-spin target by another nuclear-spin sensor. The full characterization of quantum correlations provides a new tool for understanding quantum many-body systems, exploring fundamental quantum physics, and developing quantum technologies.

quant-ph↗

arXiv2021

Finite-time and Fixed-time Convergence in Continuous-time Optimization

It is known that the gradient method can be viewed as a dynamic system where various iterative schemes can be designed as a part of the closed loop system with desirable properties. In this paper, the finite-time and fixed-time convergence in continuous-time optimization are mainly considered. By the advantage of sliding mode control, a finite-time gradient method is proposed, whose convergence time is dependent on initial conditions. To make the convergence time robust to initial conditions, two different designs of fixed-time gradient methods are then provided. One is designed using the property of sine function, whose convergence time is dependent on the frequency of a sine function. The other one is designed using the property of Mittag-Leffler function, whose convergence time is determined by the first positive zero of a Mittag-Leffler function. All the results are extended to more general cases and finally demonstrated by some dedicated simulation examples.

math.OC↗

arXiv2021

A novel class of fixed-time consensus protocols for multi-agent systems with simple dynamics

This paper investigates the fixed-time consensus problem for a class of multi-agent systems with simple dynamics. Unlike the traditional way to realize fixed-time convergence, a novel strategy using the property of periodic functions is proposed to achieve fixed-time convergence. On this basis, novel protocols for achieving fixed-time consensus and fixed-time average consensus are then given, where the upper bound of the consensus time is independent of initial conditions. Moreover, the result of fixed-time average consensus is extended to a more general case, where the weights of different states can be allocated in advance. Finally, the fixed-time consensus in the presence of disturbances is derived with the help of sliding mode control, where a fixed-time sliding manifold and fixed-time reaching law are designed. All the conclusions are demonstrated by dedicated simulation examples.

eess.SY↗

arXiv2020

Lyapunov functions for nabla discrete fractional order systems

This paper focuses on the fractional difference of Lyapunov functions related to Riemann-Liouville, Caputo and Grunwald-Letnikov definitions. A new way of building Lyapunov functions is introduced and then five inequalities are derived for each definition. With the help of the developed inequalities, the sufficient conditions can be obtained to guarantee the asymptotic stability of the nabla discrete fractional order nonlinear systems. Finally, three illustrative examples are presented to demonstrate the validity and feasibility of the proposed theoretical results.

math.DS↗

arXiv2020

Some fundamental properties on the sampling free nabla Laplace transform

Discrete fractional order systems have attracted more and more attention in recent years. Nabla Laplace transform is an important tool to deal with the problem of nabla discrete fractional order systems, but there is still much room for its development. In this paper, 14 lemmas are listed to conclude the existing properties and 14 theorems are developed to describe the innovative features. On one hand, these properties make the N-transform more effective and efficient. On the other hand, they enrich the discrete fractional order system theory

math.OC↗

arXiv2019

Analytical calculation of the inverse nabla Laplace transform

The inversion of nabla Laplace transform, corresponding to a causal sequence, is considered. Two classical methods, i.e., residual calculation method and partial fraction method are developed to perform the inverse nabla Laplace transform. For the first method, two alternative formulae are proposed when adopting the poles inside or outside of the contour, respectively. For the second method, a table on the transform pairs of those popular functions is carefully established. Besides illustrating the effectiveness of the developed methods with two illustrative examples, the applicability are further discussed in the fractional order case.

math.GM↗

arXiv2019

Convolutional neural networks with fractional order gradient method

This paper proposes a fractional order gradient method for the backward propagation of convolutional neural networks. To overcome the problem that fractional order gradient method cannot converge to real extreme point, a simplified fractional order gradient method is designed based on Caputo's definition. The parameters within layers are updated by the designed gradient method, but the propagations between layers still use integer order gradients, and thus the complicated derivatives of composite functions are avoided and the chain rule will be kept. By connecting every layers in series and adding loss functions, the proposed convolutional neural networks can be trained smoothly according to various tasks. Some practical experiments are carried out in order to demonstrate fast convergence, high accuracy and ability to escape local optimal point at last.

math.OC↗

arXiv2019

Optimal Randomness in Swarm-Based Search

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments, and also movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from that the Lévy distribution, has an infinite second moment, and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation, or diversification and intensification. Therefore, it's necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, CS is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.

cs.NE↗

arXiv2019

Fractional order [PI] Controller and Smith-like Predictor Design for A Class of High Order Systems

To handle the control difficulties caused by high-order dynamics, a control structure based on fractional order [proportional integral] (PI) controller and fractional order Smith-like predictor for a class of high order systems in the type of K/(Ts+1)n is proposed in this paper. The analysis of the tracking and disturbance rejection is illustrated based on the terminal value theorem and shows that the proposed control structure can ensure that the closed-loop system converges to the set point without static error and the closed-loop system recovers to its original state when the input disturbance occurs. Then, simulations about the influence on the control performance and control signal with different are carried out based on multi-objective genetic algorithm (MO-GA). The results show that the control performance can be improved and the energy of the control signal can be reduced simultaneously when the order is chosen no more than one. This can verify that the fractional order Smith-like predictor with has an advantage over that of the integral order Smith-like predictor.

eess.SY↗

arXiv2019

A novel perspective to gradient method: the fractional order approach

In this paper, we give some new thoughts about the classical gradient method (GM) and recall the proposed fractional order gradient method (FOGM). It is proven that the proposed FOGM holds a super convergence capacity and a faster convergence rate around the extreme point than the conventional GM. The property of asymptotic convergence of conventional GM and FOGM is also discussed. To achieve both a super convergence capability and an even faster convergence rate, a novel switching FOGM is proposed. Moreover, we extend the obtained conclusion to a more general case by introducing the concept of p-order Lipschitz continuous gradient and p-order strong convex. Numerous simulation examples are provided to validate the effectiveness of proposed methods.

math.OC↗

arXiv2019

A universal framework of GKYP lemma for singular fractional order systems

The well-known GKYP is widely used in system analysis, but for singular systems, especially singular fractional order systems, there is no corresponding theory, for which many control problems for this type of system can not be optimized in the limited frequency ranges. In this paper, a universal framework of finite frequency band GKYP lemma for singular fractional order systems is established. Then the bounded real lemma in the sense of L is derived for different frequency ranges. Furthermore, the corresponding controller is designed to improve the L performance index of singular fractional order systems. Three illustrative examples are given to demonstrate the correctness and effectiveness of the theoretical results.

eess.SY↗

arXiv2018

On optimal tempered Lévy flight foraging

Optimal random foraging strategy has gained increasing concentrations. It is shown that Lévy flight is more efficient compared with the Brownian motion when the targets are sparse. However, standard Lévy flight generally cannot be followed in practice. In this paper, we assume that each flight of the forager is possibly interrupted by some uncertain factors, such as obstacles on the flight direction, natural enemies in the vision distance, and restrictions in the energy storage for each flight, and introduce the tempered Lévy distribution $p(l)\sim {\rm e}^{-ρl}l^{-μ}$. It is validated by both theoretical analyses and simulation results that a higher searching efficiency can be derived when a smaller $ρ$ or $μ$ is chosen. Moreover, by taking the flight time as the waiting time, the master equation of the random searching procedure can be obtained. Interestingly, we build two different types of master equations: one is the standard diffusion equation and the other one is the tempered fractional diffusion equation.

cond-mat.stat-mech↗

arXiv2015

Design and fabrication of a 2.5T superconducting dipole prototype based on tilted solenoids

This paper describes a new design of superconducting dipole magnet prototype by the use of tilted solenoids. The magnet prototype, which consists of four layers of superimposed tilted solenoids with operating current of 3708 A, will produce a 2.5 T magnetic field in an aperture of 50 mm diameter. The detailed magnetic field design by using two kinds of software is presented. And their results show a good agree in the magnetic fields. So far we have accomplished the prototype construction and expect a cryogenic test. The process of the magnet fabrication is also reported in detail.

physics.ins-det↗