arXiv subjects

Shan Jin

Publications and source records attributed to Shan Jin.

33 records · Page 2Linked to original sources

arXiv2023

Using a virtual reality interview simulator to explore factors influencing people's behavior

Virtual reality interview simulator (VRIS) provides an effective and manageable approach for candidates prone to being very nervous during interviews, yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing electrodermal activity and questionnaire, we investigated the influence of five variables: (I) \textit{Realism}; (II) \textit{Question type}; (III) \textit{Interviewer attitude}; (IV) \textit{Timing}; and (V) \textit{Preparation}. As such, an orthogonal design $L_8(4^1 \times 2^4)$ with eight experiments ($O A_8$ matrix) was implemented, in which 19 college students took part in the experiments. Considering the anxiety, overall experience, and performance of the interviewees, results indicate that \textit{Question type} plays a major role; secondly, \textit{Realism}, \textit{Preparation}, and \textit{Interviewer attitude} all have some degree of influence; lastly, \textit{Timing} have little to no impact. Specifically, professional interview questions elicited a greater degree of anxiety than personal ones among the categories of interview questions. This work contributes to our understanding of anxiety-stimulating factors during job interviews in virtual reality and provides cues for designing future VRIS.

cs.HC↗

arXiv2022

A duplication-free quantum neural network for universal approximation

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved. Compared with other established proposals, our model requires significantly fewer qubits and a shallower circuit, substantially lowering the resource overhead for implementation. It is also more robust against noise and easier to implement on a near-term device. Simulations show that our model can solve a broad range of classical and quantum learning problems, demonstrating its broad application potential.

quant-ph↗

arXiv2022

Expected $H\toμ^{+}μ^{-}$ measurement precision with $e^{+}e^{-}\to Z(q\bar{q})H$ production at the CEPC

A search for the dimuon decay of the Standard Model Higgs boson is performed using the Monte Carlo simulated events to mimic data corresponding to an integrated luminosity of 5.6 ab$^{-1}$ collected with the Circular Electron-Positron Collider detector in $e^{+}e^{-}$ collisions at $\sqrt{s}=240$ GeV. The paper studies $e^{+}e^{-}\to ZH,\,Z\to q\bar{q},\,H\toμ^{+}μ^{-}$ process, and the expected significance considering only the data statistical uncertainty over the background-only hypothesis for a Higgs boson with a mass of 125 GeV is found to be 6.1$σ$, corresponding to the precision of 19%. The systematic impacts from the background Monte Carlo statistical fluctuations are estimated to be negligible. The dependence of the measurement accuracy on the muon momentum resolution of the CEPC detector has been investigated. It is found that the muon momentum resolution has to be better than 204 MeV to discover the $H\toμμ$ process at the nominal integrated luminosity. And if the resolution is 100% worse than the designed parameter, the integrated luminosity is needed to be greater than 7.2 ab$^{-1}$ to reach 5$σ$ significance.

hep-ex↗

arXiv2022

Quantum spectral clustering algorithm for unsupervised learning

Clustering is one of the most crucial problems in unsupervised learning, and the well-known $k$-means clustering algorithm has been shown to be implementable on a quantum computer with a significant speedup. However, many clustering problems cannot be solved by $k$-means, and a powerful method called spectral clustering is introduced to solve these problems. In this work, we propose a circuit design to implement spectral clustering on a quantum processor with a substantial speedup, by initializing the processor into a maximally entangled state and encoding the data information into an efficiently-simulatable Hamiltonian. Compared with the established quantum $k$-means algorithms, our method does not require a quantum random access memory or a quantum adiabatic process. It relies on an appropriate embedding of quantum phase estimation into Grover's search to gain the quantum speedup. Simulations demonstrate that our method is effective in solving clustering problems and will serve as an important supplement to quantum $k$-means for unsupervised learning.

quant-ph↗

arXiv2022

Detailed chemistry modelling of rotating detonations with dilute n-heptane sprays and preheated air

Utilization of liquid fuels is crucial to enabling commercialization of rotating detonation engines in the near future. In this study, Eulerian-Lagrangian simulations are conducted for rotating detonative combustion with dilute n-heptane sprays and preheated air. Two-dimensional flattened configuration is used and a skeletal chemical mechanism with 44 species and 112 elementary reactions for n-heptane combustion is adopted. The flow structure, droplet distribution, and thermochemical parameters in the refill zone are first analyzed. It is shown that the mixture in the refill zone is heterogeneous, including evaporating droplets, vapor, and air. When the total temperature is below 950 K, the average equivalence ratio increases with the total temperature. When it is higher than 950 K, the average equivalence ratio is almost constant. Subsequently, the chemical explosive mode analysis is applied to identify the controlling reactions and dominant combustion modes in the fuel refill zone and reaction fronts. Results demonstrate that the initiation reaction and low-temperature reaction are dominant in the upstream and downstream of the refill zone, respectively. The intermediate species from low-temperature chemistry is found to be important for the chemical explosive mode in the undetonated mixture. The influence of species diffusion and dispersed droplets is further analyzed. Results show that vapor autoignition facilitated by droplet evaporation occurs in the refill zone. Finally, the effects of the air total temperature on the detonation propagation speed and RDE propulsion performance are investigated. It is found that the detonation propagation speed and specific impulse increase with air total temperature.

physics.flu-dyn↗

arXiv2021

A universal duplication-free quantum neural network

Universality of neural networks describes the ability to approximate arbitrary function, and is a key ingredient to keep the method effective. The established models for universal quantum neural networks(QNN), however, require the preparation of multiple copies of the same quantum state to generate the nonlinearity, with the copy number increasing significantly for highly oscillating functions, resulting in a huge demand for a large-scale quantum processor. To address this problem, we propose a new QNN model that harbors universality without the need of multiple state-duplications, and is more likely to get implemented on near-term devices. To demonstrate the effectiveness, we compare our proposal with two popular QNN models in solving typical supervised learning problems. We find that our model requires significantly fewer qubits and it outperforms the other two in terms of accuracy and relative error.

quant-ph↗

arXiv2021

Rotating detonation combustion with in-situ evaporating bi-disperse n-heptane sprays

Eulerian-Lagrangian simulations are conducted for two-dimensional Rotating Detonative Combustion (RDC) fueled by bi-disperse n-heptane sprays without any fuel pre-vaporization. Parametric studies are performed to study the influences of droplet diameter and droplet distribution on detonation wave are studied. The extinction process of the detonation wave is also been analyzed. It is found that small n-heptane droplets (e.g., 2 um) are completely vaporized in the fuel refilling area. Increasing the droplet diameter causes the droplet to fail to evaporate completely within the fuel refilling area and exist after the detonation wave. A reflected shock can be obsevered after the detonation wave. When the droplet diameter is larger than 10 μm, the higher pressure after the detonation wave leads to the reactants cannot be sprayed into the combustor, eventually leading to extinction of the detonation wave. In bi-disperse n-heptane sprays, presence of droplets with small diameter maintains the stable propagation of the detonation wave. The average equivalence ratio in the fuel refilling area is lower than total equivalence ratio (up to 0.66 only), and the average equivalence ratio decreases with the increase of droplet diameter in bi-disperse n-heptane sprays. The increase in droplet diameter decreases the detonated fuel fraction and the speed of detonation wave. The speeds of detonation wave in bi-disperse n-heptane sprays are 3-9% lower than the respective gaseous cases. Moreover, propulsion performance of RDC such as thrust and specific impulse decreases with increasing droplet diameter.

physics.flu-dyn↗

arXiv2021

Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to optimize their common goal. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ASTC problem. Here we use independent reinforcement learning (IRL) to solve a complex traffic cooperative control problem in this study. One of the largest challenges of this problem is that the observation information of intersection is typically partially observable, which limits the learning performance of IRL algorithms. To this, we model the traffic control problem as a partially observable weak cooperative traffic model (PO-WCTM) to optimize the overall traffic situation of a group of intersections. Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. We also propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffer, which deals with the problem of experience failure caused by the strategy change of other agents. The latter mechanism increases the weight experiences with high estimation and `leniently' trains the DDQN neural network, which improves the probability of the selection of cooperative joint strategies. Experimental results show that CIL-DDQN outperforms other methods in almost all performance indicators of the traffic control problem.

cs.LG↗

arXiv2020

A Query-based Quantum Eigensolver

Solving eigenvalue problems is crucially important for both classical and quantum applications. Many well-known numerical eigensolvers have been developed, including the QR and the power methods for classical computers, as well as the quantum phase estimation(QPE) method and the variational quantum eigensolver for quantum computers. In this work, we present an alternative type of quantum method that uses fixed-point quantum search to solve Type II eigenvalue problems. It serves as an important complement to the QPE method, which is a Type I eigensolver. We find that the effectiveness of our method depends crucially on the appropriate choice of the initial state to guarantee a sufficiently large overlap with the unknown target eigenstate. We also show that the quantum oracle of our query-based method can be efficiently constructed for efficiently-simulated Hamiltonians, which is crucial for analyzing the total gate complexity. In addition, compared with the QPE method, our query-based method achieves a quadratic speedup in solving Type II problems.

quant-ph↗

arXiv2020

Multi-layer quantum search and inclusion of NP into BQP

In this work, we present a multi-layer quantum search method that generates an exponential speedup of the standard Grover's algorithm. As direct applications, any NP problems can be solved efficiently on a quantum circuit with only polynomial gate complexity. In particular, such multi-layer search can solve the factoring problem with an exponential speedup, providing an alternative to Shor's algorithm. Our results show that the exponential speedup of quantum circuits is ubiquitous, and Grover's search is much more powerful than that has been demonstrated. With no contradiction to the quadratic optimality of single-layer query complexity, the great potential of Grover's search is fully released by such multi-layer search design.

quant-ph↗

arXiv2019

Precision Higgs Physics at CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

hep-ex↗

arXiv2019

Two-loop $N$-jettiness soft function for $pp \to 2j$ production

We calculate the two-loop soft function for $pp \to jj$, $e^+e^- \to 4j$ or $e p \to 3j$ when the $N$-jettiness observable is measured. The result presented here makes up the necessary piece for realizing the full next-to-next-to-leading order predictions for these processes using the $N$-jettiness subtraction scheme.

hep-ph↗

arXiv2016

Cross Section and Higgs Mass Measurement with Higgsstrahlung at the CEPC

The Circular Electron Positron Collider (CEPC) is a future Higgs factory proposed by the Chinese high energy physics community. It will operate at a center-of-mass energy of 240-250 GeV. The CEPC will accumulate an integrated luminosity of 5 ab$^{\rm{-1}}$ in ten years' operation, producing one million Higgs bosons via the Higgsstrahlung and vector boson fusion processes. This sample allows a percent or even sub-percent level determination of the Higgs boson couplings. With GEANT4-based full simulation and dedicated fast simulation tool, we evaluated the statistical precisions of the Higgstrahlung cross section $σ_{ZH}$ and the Higgs mass $m_{H}$ measurement at the CEPC in the $Z\rightarrowμ^+μ^-$ channel. The statistical precision of $σ_{ZH}$ ($m_{H}$) measurement could reach 0.97\% (6.9 MeV) in the model-independent analysis which uses only the information of Z boson decay. For the standard model Higgs boson, the $m_{H}$ precision could be improved to 5.4 MeV by including the information of Higgs decays. Impact of the TPC size to these measurements is investigated. In addition, we studied the prospect of measuring the Higgs boson decaying into invisible final states at the CEPC. With the standard model $ZH$ production rate, the upper limit of ${\cal B}(H\rightarrow \rm{inv.})$ could reach 1.2\% at 95\% confidence level.

hep-ex↗

arXiv2003

The Zero-Dispersion Limit for the Odd Flows in the Focusing Zakharov-Shabat Hierarchy

We present a numerical and theoretical study of the zero-dispersion limit of the focusing Zakharov-Shabat hierarchy, which includes NLS and mKdV flows as its second and third members. All the odd flows in the hierachy are shown to preserve real-valued data. We establish the zero-dispersion limit of all the nontrivial conserved densities and associated fluxes for these odd flows for a large class of real-valued initial data which includes all ``single hump'' initial data. In particular, it is done for the ``focusing'' mKdV flow. The method is based on the Lax-Levermore KdV strategy, but here it is carried out in the context of a nonselfadjoint spectral problem. We find that after an algebraic transformation the limiting dynamics of the mKdV equation is identical to that of the KdV equation.

nlin.SI↗

arXiv1995

Glueball Interpretation of $ξ$(2230)

On the basis of the results of $ξ(2230)\rightarrowπ^{+}π^{-}, p\bar{p}$ and $K\bar{K}$, measured by the BES Collaboration in radiative J/$ψ$ decays, combined with the upper limit of Br($ξ\rightarrow p\bar{p}$)Br($ξ\rightarrow K\bar{K}$), measured by PS185 experiment, we argue that the distinctive properties of $ξ$(2230), the flavor-symmetric decays and the narrow partial decay widths to $ππ$ and $K\bar{K}$ as well as its copious production in radiative J/$ψ$ decay, would strongly favor the glueball interpretation of $ξ$(2230).

hep-ph↗