arXiv subjects

Min Jiang

Publications and source records attributed to Min Jiang.

At least 73 records · Page 4Linked to original sources

arXiv2023

Robust quantum teleportation via a non-Markovian channel

In this paper, we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system, where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Markovianity, enhanced effects of the channels on continuous-variable quantum teleportation are observed. Also, the logarithmic negativity of entangled states can be effectively maintained so that the decay of the average fidelity of quantum teleportation is mitigated. In addition, the analysis on different teleported quantum states shows that coherent states exhibit stronger robustness than those of squeezed states and cat states in quantum teleportation with a non-Markovian channel.

quant-ph↗

arXiv2023

Local to Global: A Distributed Quantum Approximate Optimization Algorithm for Pseudo-Boolean Optimization Problems

With the rapid advancement of quantum computing, Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy, which exponentially solves a class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, limited qubit availability and restricted coherence time challenge QAOA to solve large-scale pseudo-Boolean problems on currently available Near-term Intermediate Scale Quantum (NISQ) devices. In this paper, we propose a distributed QAOA which can solve a general pseudo-Boolean problem by converting it to a simplified Ising model. Different from existing distributed QAOAs' assuming that local solutions are part of a global one, which is not often the case, we introduce community detection using Louvian algorithm to partition the graph where subgraphs are further compressed by community representation and merged into a higher level subgraph. Recursively and backwards, local solutions of lower level subgraphs are updated by heuristics from solutions of higher level subgraphs. Compared with existing methods, our algorithm incorporates global heuristics into local solutions such that our algorithm is proven to achieve a higher approximation ratio and outperforms across different graph configurations. Also, ablation studies validate the effectiveness of each component in our method.

quant-ph↗

arXiv2023

Cooperative Spin Amplification

Quantum amplification is recognized as a key resource for precision measurements. However, most conventional paradigms employ an ensemble of independent particles that usually limit the performance of quantum amplification in gain, spectral linewidth, etc. Here we demonstrate a new signal amplification using cooperative 129Xe nuclear spins embedded within a feedback circuit, where the noble-gas spin coherence time is enhanced by at least one order of magnitude. Using such a technique, magnetic field can be substantially pre-enhanced by more than three orders and is in situ readout with an embedded 87Rb magnetometer. We realize an ultrahigh magnetic sensitivity of 4.0 fT/Hz$^{1/2}$ that surpasses the photon-shot noise and even below the spin-projection noise of the embedded atomic magnetometer, allowing for exciting applications including searches for dark matter with sensitivity well beyond supernova constraints. Our findings extend the physics of quantum amplification to cooperative spin systems and can be generalized to a wide variety of existing sensors, enabling a new class of cooperative quantum sensors.

quant-ph↗

arXiv2023

Structural pathway for nucleation and growth of topologically close-packed phase from parent hexagonal crystal

The solid diffusive phase transformation involving the nucleation and growth of one nucleus is universal and frequently employed but has not yet been fully understood at the atomic level. Here, our first-principles calculations reveal a structural formation pathway of a series of topologically close-packed (TCP) phases within the hexagonally close-packed (hcp) matrix. The results show that the nucleation follows a nonclassical nucleation process, and the whole structural transformation is completely accomplished by the shuffle-based displacements, with a specific 3-layer hcp-ordering as the basic structural transformation unit. The thickening of plate-like TCP phases relies on forming these hcp-orderings at their coherent TCP/matrix interface to nucleate ledge, but the ledge lacks the dislocation characteristics considered in the conventional view. Furthermore, the atomic structure of the critical nucleus for the Mg2Ca and MgZn2 Laves phases was predicted in terms of Classical Nucleation Theory (CNT), and the formation of polytypes and off-stoichiometry in TCP precipitates is found to be related to the nonclassical nucleation behavior. Based on the insights gained, we also employed high-throughput screening to explore several common hcp-metallic (including hcp-Mg, Ti, Zr, and Zn) systems that may undergo hcp-to-TCP phase transformations. These insights can deepen our understanding of solid diffusive transformations at the atomic level, and constitute a foundation for exploring other technologically important solid diffusive transformations.

cond-mat.mtrl-sci↗

arXiv2023

Quantum Approximate Optimization Algorithm in Non-Markovian Quantum Systems

Although quantum approximate optimization algorithm (QAOA) has demonstrated its quantum supremacy, its performance on Noisy Intermediate-Scale Quantum (NISQ) devices would be influenced by complicated noises, e.g., quantum colored noises. To evaluate the performance of QAOA under these noises, this paper presents a framework for running QAOA on non-Markovian quantum systems which are represented by an augmented system model. In this model, a non-Markovian environment carrying quantum colored noises is modelled as an ancillary system driven by quantum white noises which is directly coupled to the corresponding principal system; i.e., the computational unit for the algorithm. With this model, we mathematically formulate QAOA as piecewise Hamiltonian control of the augmented system, where we also optimize the control depth to fit into the circuit depth of current quantum devices. For efficient simulation of QAOA in non-Markovian quantum systems, a boosted algorithm using quantum trajectory is further presented. Finally, we show that non-Markovianity can be utilized as a quantum resource to achieve a relatively good performance of QAOA, which is characterized by our proposed exploration rate.

quant-ph↗

arXiv2023

Enhanced quantum sensing with amplification and deamplification

Quantum sensing is a fundamental building block of modern technology that employs quantum resources and creates new opportunities for precision measurements. However, previous methods usually have a common assumption that detection noise levels should be below the intrinsic sensitivity provided by quantum resources. Here we report the first demonstration of Fano resonance between coupled alkali-metal and noble gases through rapid spin-exchange collisions. The Fano resonance gives rise to two intriguing phenomena: spin amplification and deamplification, which serve as crucial resources for enhanced sensing. Further we develop a novel scheme of quantum sensing enhanced by amplification and deamplification, with relaxed requirements on the detection noise. The coupled systems of alkali-metal and noble gases act as amplifiers or de-amplifiers, enabling to extract small signals above the detection noise before final detection. We demonstrate magnetic-field measurement about 54 decibels below the photon-shot noise, which outperforms the state-of-the-art squeezed-light technology and realizes femtotesla-level sensitivity. Our work opens new avenues to applications in searches for ultralight dark matter with sensitivity well beyond the supernova-observation constraints.

quant-ph↗

arXiv2023

A Unified Probabilistic Framework for Spatiotemporal Passenger Crowdedness Inference within Urban Rail Transit Network

This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM), a framework to infer the passenger distribution inside the whole urban rail transit (URT) system in real-time. Our model is practical since the model is designed in a probabilistic manner and only based on the entry and exit timestamps information collected by the automatic fare collection (AFC) system. Firstly, the entire URT system is decomposed into several components of stations and segments. By decomposing a passenger's travel actions into entering, traveling, transferring, and exiting, we build a statistical model to estimate the passengers' lingering time within each component and the passengers' destination based on historical AFC data. Then, the passengers' spatial distribution is predicted in real-time based on each passenger's elapsed travel time and their entry station. The effectiveness of the scheme is validated with a real dataset from a real URT system.

stat.AP↗

arXiv2023

Dielectric metasurface-assisted cavity ring-down spectroscopy for thin-film circular dichroism analysis

Chiral molecules show differences in their chemical and optical properties due to different spatial arrangements of the atoms in the two enantiomers. A common way to optically differentiate them is to detect the disparity in the absorption of light by the two enantiomers, i.e. the absorption circular dichroism (CD). However, the CD of typical molecules is very small, limiting the sensitivity of chiroptical analysis based on CD. Cavity ring-down spectroscopy (CRDS) is a well-known ultrasensitive absorption spectroscopic method for low-absorbing gas-phase samples because the multiple reflections of light in the cavity greatly increase the absorption path. By inserting a prism into the cavity, the optical mode undergoes total internal reflection (TIR) at the prism surface and the evanescent wave (EW) enables the absorption detection of condensed-phase samples within a very thin layer near the prism surface, called EW-CRDS. Here, we propose an ultrasensitive chiral absorption spectroscopy platform using a dielectric metasurface-assisted EW-CRDS. We theoretically show that, upon linearly polarized and oblique incidence, the metasurface exhibits minimum scattering and absorption loss, introduces negligible polarization change, and locally converts the linearly polarized light into near fields with finite optical chirality, enabling CD detection with EW-CRDS that only works with linearly polarized light. We evaluate the ring-down time in the presence of chiral molecules and determine the sensitivity of the cavity as a function of total absorption from the molecules. The findings open the avenue for an ultrasensitive thin film detection of the chiral molecules using the CRDS techniques.

physics.optics↗

arXiv2023

Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search

The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement usually means that the results from the algorithm runs should not only be good for every run in terms of performance but also that the performance of multiple runs should not fluctuate too much, i.e., the algorithm shows good insensitivity. Considering that substantial computational resources are requested for each run, it is essential to improve upon the performance of the large-scale multiobjective optimization algorithm, as well as the insensitivity of the algorithm. However, existing large-scale multiobjective optimization algorithms solely focus on improving the performance of the algorithms, leaving the insensitivity characteristics unattended. In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems. The proposed method samples the decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation. It selects nodes with good evaluation for further search to reduce the performance sensitivity caused by large-scale decision variables. We compare the proposed algorithm with several state-of-the-art designs on different benchmark functions. We also propose two metrics to measure the sensitivity of the algorithm. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving large-scale multiobjective optimization problems.

cs.NE↗

arXiv2023

Self-Calibrated Atom-Interferometer Gyroscope by Modulating Atomic Velocities

Atom-interferometer gyroscopes have attracted much attention for their potential superior long-term stability and extremely low drift. For such high precision instrument, a self-calibration to achieve an absolute rotation measurement is highly demanded. Here we propose and demonstrate a self-calibration of the atomic gyroscope. The calibration is realized by using the detuning of laser frequency to control the atomic velocity thus to modulate the scale factor of the gyroscope. The modulation determines the order and the initial phase of the interference stripe, thus eliminates the ambiguity caused by the periodicity of the interferometric signal. The calibration method is verified by measuring the Earth's rotation. Long-term stable and self-calibrated atom-interferometer gyros can find important applications in the fields of fundamental physics and long-time navigation.

physics.atom-ph↗

arXiv2023

Compact multi-channel radio frequency pulse sequence generator with fast switching capability for cold atom interferometers

Cold atom interferometers have matured to a powerful tool in fundamental physics research, and they are currently on their way from realizations in the laboratory to applications in the real world. The radio frequency (RF) generator is an indispensable component to control lasers and then manipulate atoms. We developed a highly compact RF generator for fast switching and sweeping frequencies/amplitudes of atomic interference pulse sequences. Multi-channel RF signals are generated by using a field-programmable gate array (FPGA) to control eight direct digital synthesizers (DDSs). We further proposed and demonstrated a method of preloading the parameters of all RF pulse sequences to the DDS registers before the execution of the pulse sequences, which eliminates the data transfer between the FPGA and DDSs to change RF signals and thus sharply shortens the delay of frequency switching when the pulse sequences are running. The characterized performance shows the generated RF signals achieve 119 ns frequency switching delay, and 40 dB harmonic rejection ratio. The generated RF pulse sequences are applied to a cold atom-interferometer gyroscope, and the contrast of atomic interference fringes reaches 38%. This compact multi-channel generator with the fast frequency/amplitude switching or/and sweeping capability has beneficial applications for the real-world atom interferometers.

physics.atom-ph↗

arXiv2022

An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.

cs.NE↗

arXiv2022

Dissecting axion and dark photon with a network of vector sensors

We develop formalisms for a network of vector sensors, sensitive to certain spatial components of the signals, to identify the properties of a light axion or a dark photon background. These bosonic fields contribute to vector-like signals in the detectors, including effective magnetic fields triggering the spin precession, effective electric currents in a shielded room, and forces on the matter. The interplay between a pair of vector sensors and a baseline that separates them can potentially uncover rich information of the bosons, including angular distribution, polarization modes, source localization, and macroscopic circular polarization. Using such a network, one can identify the microscopic nature of a potential signal, such as distinguishing between the axion-fermion coupling and the dipole couplings with the dark photon.

hep-ph↗

arXiv2022

The Statistical Similarity of Repeating and Non-Repeating Fast Radio Bursts

In this paper, we present a sample of 21 repeating fast radio bursts (FRBs) detected by different radio instruments before September 2021. Using the Anderson--Darling test, we compared the distributions of extra-Galactic dispersion measure ($DM_{\rm E}$) of non-repeating FRBs, repeating FRBs and all FRBs. It was found that the $ DM_{\rm E}$ values of three sub-samples are log-normally distributed. The $DM_{\rm E}$ of repeaters and non-repeaters were drawn from a different distribution on basis of the Mann--Whitney--Wilcoxon test. In addition, assuming that the non-repeating FRBs identified currently may be potentially repeators, i.e., the repeating FRBs to be universal and representative, one can utilize the averaged fluence of repeating FRBs as an indication from which to derive an apparent intensity distribution function (IDF) with a power-law index of $a_1=$ $1.10\pm 0.14$ ($a_2=$ $1.01\pm 0.16$, the observed fluence as a statistical variant), which is in good agreement with the previous IDF of 16 non-repeating FRBs found by Li et al. Based on the above statistics of repeating and non-repeating FRBs, we propose that both types of FRBs may have different cosmological origins, spatial distributions and circum-burst environments. Interestingly, the differential luminosity distributions of repeating and non-repeating FRBs can also be well described by a broken power-law function with the same power-law index of $-$1.4.

astro-ph.HE↗

arXiv2022

Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism

Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple conflicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multiobjective evolutionary algorithms consider the balance between exploration and exploitation from the individual's perspective. However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm. In this paper, we propose a large-scale multiobjective optimization algorithm based on the attention mechanism, called (LMOAM). The attention mechanism will assign a unique weight to each decision variable, and LMOAM will use this weight to strike a balance between exploration and exploitation from the decision variable level. Nine different sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this paper, and the experimental results validate the effectiveness of our design.

cs.NE↗

arXiv2022

Exact Penalty Algorithm of Strong Convertible Nonconvex Optimization

This paper defines a strong convertible nonconvex(SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth(nondifferentiable) function. First, many examples of SCN function are given, where the SCN functions are nonconvex or nonsmooth. Second, the operational properties of the SCN functions are proved, including addition, multiplication, compound operations and so on. Third, the SCN forms of some special functions common in machine learning and engineering applications are presented respectively where these SCN function optimization problems can be transformed into minmax problems with a convex and concave objective function. Fourth,a minmax optimization problem of SCN function and its penalty function are defined. The optimization condition,exactness and stability of the minmax optimization problem are proved. Finally, an algorithm of penalty function to solve the minmax optimization problem and its convergence are given. This paper provides an efficient technique for solving unconstrained nonconvex or nonsmooth(nondifferentiable) optimization problems to avoid using subdifferentiation or smoothing techniques.

math.OC↗

arXiv2022

SAPPHIRE: Search for exotic parity-violation interactions with quantum spin amplifiers

Quantum sensing provides sensitive tabletop tools to search for exotic spin-dependent interactions beyond the Standard Model, which has attracted great attention in theories and experiments. Here we develop a technique based on quantum Spin Amplifier for Particle PHysIcs REsearch (SAPPHIRE) to resonantly search for exotic interactions, specifically parity-odd spin-spin interactions. The present technique effectively amplifies the pseudomagnetic field generated by exotic interactions by a factor of about 200 while being insensitive to spurious external magnetic fields. Our studies, using such a quantum amplification technique, open the doors to exploring the parity-violation interactions mediated by Z' bosons in the challenging parameter space (force range between 3 mm and 0.1 km) and set the most stringent constraints on Z'-mediated electron-neutron couplings, significantly improving previous limits by up to five orders of magnitude. Moreover, our bounds on Z'-mediated couplings between nucleons reaches into a hitherto unexplored parameter space (force range below 1 m), complementing the existing astrophysical and laboratory studies.

quant-ph↗

arXiv2022

A Cooperation-Aware Lane Change Method for Autonomous Vehicles

Lane change for autonomous vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV's decision and surrounding vehicles' interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8$\%$ and 2.6$\%$ respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.

cs.RO↗