arXiv subjects

Xiao-Ming Zhang

Publications and source records attributed to Xiao-Ming Zhang.

34 records · Page 2Linked to original sources

arXiv2022

Revisiting the centrality definition and observable centrality dependence of relativistic heavy-ion collisions in PACIAE model

We improve the centrality definition in impact parameter in PACIAE model responding the fact reported by the ALICE, ATLAS, and CMS collaborations that the maximum impact parameter in heavy ion collisions should be extended to 20 $fm$. Meanwhile the PACIAE program is updated to a new version of PACIAE 2.2.2 with convenience of studying the elementary nuclear collisions, proton-nucleus collisions, and the nucleus-nucleus collisions in one unified program version. The new impact parameter definition together with the optical Glauber model calculated impact parameter bin, $N_{part}$, and $N_{coll}$ in proton-nucleus and nucleus-nucleus collisions at relativistic energies are consistent with the improved MC-Glauber model ones within the error bar. The charged-particle pseudorapidity and the transverse momentum distributions in Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV simulated by PACIAE 2.2.2 well reproduce the ALICE experimental data.

nucl-th↗

arXiv2022

Linear scaling of lepton charge asymmetry in $W^\pm$ production in ultra-relativistic nuclear collisions

The lepton charge asymmetry in $W^\pm$ production in the nuclear collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV is investigated with a parton and hadron cascade model PACIAE. Recently published ALICE and the ATLAS data of lepton charge asymmetry are well reproduced. An interesting linear scaling behavior is observed in the lepton charge asymmetry as a function of the collision system valence quark number asymmetry among the different size of nuclear collision systems at $\sqrt{s_{\rm NN}}=5.02$ TeV. This linear scaling behavior may serve as an additional constraint on the PDF (nPDF) extractions.

hep-ph↗

arXiv2022

On Circuit Depth Scaling For Quantum Approximate Optimization

Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning. An important subclass of these algorithms, designed for combinatorial optimization on currrent quantum hardware, is the quantum approximate optimization algorithm (QAOA). It is known that problem density - a problem constraint to variable ratio - induces under-parametrization in fixed depth QAOA. Density dependent performance has been reported in the literature, yet the circuit depth required to achieve fixed performance (henceforth called critical depth) remained unknown. Here, we propose a predictive model, based on a logistic saturation conjecture for critical depth scaling with respect to density. Focusing on random instances of MAX-2-SAT, we test our predictive model against simulated data with up to 15 qubits. We report the average critical depth, required to attain a success probability of 0.7, saturates at a value of 10 for densities beyond 4. We observe the predictive model to describe the simulated data within a $3σ$ confidence interval. Furthermore, based on the model, a linear trend for the critical depth with respect problem size is recovered for the range of 5 to 15 qubits.

quant-ph↗

arXiv2022

Centrality dependence and isospin effect on $\Wpm$ and $\Zn$ productions in nucleus-nucleus collisions at $\sNN=5.02$~TeV

In this paper, the centrality dependent $\Zn$ and $\Wpm$ production and the isospin effect in $\Wpm$ production are investigated with a parton and hadron cascade model PACIAE in Pb--Pb collisions at $\sNN=5.02$~TeV. ALICE data of $\Zn$ production in Pb--Pb collisions at $\sNN=5.02$~TeV are found to be reproduced fairly well. The prediction on $\Wpm$ production in the same collision system is given as well. An interesting isospin effect is observed in exploring the charge asymmetry between $\Wp$ and $\Wm$ as a function of the asymmetry between number of valence $u$- and $d$-quarks varied from small to large collision systems at center-of-mass energy 5.02 TeV. The results serve as a important benchmarks for understanding the initial conditions of heavy-ion collisions.

hep-ph↗

arXiv2021

Experimental quantum state measurement with classical shadows

A crucial subroutine for various quantum computing and communication algorithms is to efficiently extract different classical properties of quantum states. In a notable recent theoretical work by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], a thrifty scheme showed how to project the quantum state into classical shadows and simultaneously predict $M$ different functions of a state with only $\mathcal{O}(\log_2 M)$ measurements, independent of the system size and saturating the information-theoretical limit. Here, we experimentally explore the feasibility of the scheme in the realistic scenario with a finite number of measurements and noisy operations. We prepare a four-qubit GHZ state and show how to estimate expectation values of multiple observables and Hamiltonians. We compare the measurement strategies with uniform, biased, and derandomized classical shadows to conventional ones that sequentially measure each state function exploiting either importance sampling or observable grouping. We next demonstrate the estimation of nonlinear functions using classical shadows and analyze the entanglement of the prepared quantum state. Our experiment verifies the efficacy of exploiting (derandomized) classical shadows and sheds light on efficient quantum computing with noisy intermediate-scale quantum hardware.

quant-ph↗

arXiv2021

Low-depth Quantum State Preparation

A crucial subroutine in quantum computing is to load the classical data of $N$ complex numbers into the amplitude of a superposed $n=\lceil \log_2N\rceil$-qubit state. It has been proven that any algorithm universally implementing this subroutine would need at least $\mathcal O(N)$ constant weight operations. However, the proof assumes that only $n$ qubits are used, whereas the circuit depth could be reduced by extending the space and allowing ancillary qubits. Here we investigate this space-time tradeoff in quantum state preparation with classical data. We propose quantum algorithms with $\mathcal O(n^2)$ circuit depth to encode any $N$ complex numbers using only single-, two-qubit gates and local measurements with ancillary qubits. Different variances of the algorithm are proposed with different space and runtime. In particular, we present a scheme with $\mathcal O(N^2)$ ancillary qubits, $\mathcal O(n^2)$ circuit depth, and $\mathcal O(n^2)$ average runtime, which exponentially improves the conventional bound. While the algorithm requires more ancillary qubits, it consists of quantum circuit blocks that only simultaneously act on a constant number of qubits and at most $\mathcal O(n)$ qubits are entangled. We also prove a fundamental lower bound $\mitΩ(n)$ for the minimum circuit depth and runtime with arbitrary number of ancillary qubits, aligning with our scheme with $\mathcal O(n^2)$. The algorithms are expected to have wide applications in both near-term and universal quantum computing.

quant-ph↗

arXiv2021

Experimental Quantum Target Detection Approaching the Fundamental Helstrom Limit

Quantum target detection is an emerging application that utilizes entanglement to enhance the sensing of the presence of an object. Although several experimental demonstrations for certain situations have been reported recently, the single-shot detection limit imposed by the Helstrom limit has not been reached because of the unknown optimum measurements. Here we report an experimental demonstration of quantum target detection, also known as quantum illumination, in the single-photon limit. In our experiment, one photon of the maximally entangled photon pair is employed as the probe signal and the corresponding optimum measurement is implemented at the receiver. We explore the detection problem in different regions of the parameter space and verify that the quantum advantage exists even in a forbidden region of the conventional illumination, where all classical schemes become useless. Our results indicate that quantum illumination breaks the classical limit for up to 40%, while approaching the quantum limit imposed by the Helstrom limit. These results not only demonstrate the advantage of quantum illumination, but also manifest its valuable potential of target detection.

quant-ph↗

arXiv2021

Dynamical simulation on production of ${\rm W}^{\pm}$ and ${\rm Z}^{0}$ bosons in p-p, p-Pb (Pb-p), and Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV with PACIAE

In this paper, production of ${\rm W}^{\pm}$ and ${\rm Z}^{0}$ vector bosons in p-p, p-Pb (Pb-p), and Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV is dynamically simulated with a parton and hadron cascade model PACIAE. ALICE data of ${\rm Z}^{0}$ production is found to be reproduced fairly well. A prediction for ${\rm W}^{\pm}$ production is given in the same collision systems, at the same energy and at the same energy. An interesting isospin-effect is observed in the sign-change of $μ^{\pm}$ charge asymmetry in pp, pn, np, and nn collisions and in minimum bias p-Pb, Pb-p and Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV, respectively.

nucl-th↗

arXiv2021

When does reinforcement learning stand out in quantum control? A comparative study on state preparation

Reinforcement learning has been widely used in many problems, including quantum control of qubits. However, such problems can, at the same time, be solved by traditional, non-machine-learning methods, such as stochastic gradient descent and Krotov algorithms, and it remains unclear which one is most suitable when the control has specific constraints. In this work, we perform a comparative study on the efficacy of three reinforcement learning algorithms: tabular Q-learning, deep Q-learning, and policy gradient, as well as two non-machine-learning methods: stochastic gradient descent and Krotov algorithms, in the problem of preparing a desired quantum state. We found that overall, the deep Q-learning and policy gradient algorithms outperform others when the problem is discretized, e.g. allowing discrete values of control, and when the problem scales up. The reinforcement learning algorithms can also adaptively reduce the complexity of the control sequences, shortening the operation time and improving the fidelity. Our comparison provides insights into the suitability of reinforcement learning in quantum control problems.

quant-ph↗

arXiv2021

Generic detection-based error-mitigation using quantum autoencoders

Efficient error-mitigation techniques demanding minimal resources is key to quantum information processing. We propose a generic protocol to mitigate quantum errors using detection-based quantum autoencoders. In our protocol, the quantum data are compressed into a latent subspace while leaving errors outside, the latter of which is then removed by a measurement and post-selection. Compared to previously developed methods, our protocol on the one hand requires no extra qubits, and on the other hand has a near-optimal denoising power, in which under reasonable requirements all errors detected outside of the latent subspace can be removed, while those inside the subspace cannot be removed by any means. Our detection-based quantum autoencoders are therefore particularly useful for near-term quantum devices in which controllable qubits are limited while noise reduction is important.

quant-ph↗

arXiv2020

The Tsinghua University-Ma Huateng Telescopes for Survey: Overview and Performance of the System

Over the past decade, time-domain astronomy in optical bands has developed rapidly with the operations of some wide-field survey facilities. However, most of these surveys are conducted with only a single band, and simultaneous color information is usually unavailable for the objects monitored during the survey. Here we present introductions to the system of Tsinghua University-Ma Huateng Telescopes for Survey (TMTS), which consists of an array of four optical telescopes installed on a single equatorial mount. Such a system is designed to get multiband photometry simultaneously for stars and transients discovered during the survey. The optics of each telescope is a modified Hamilton-Newtonian system, covering the wavelengths from 400 to 900 nm, with a field of view (FoV) of about 4.5 deg2 and a plate scale of 1.86"/pixel when combining with a 4K*4K QHY4040 CMOS detector. The TMTS system can have a FoV of about 9 deg2 when monitoring the sky with two bands (i.e., SDSS g and r filters) at the same time, and a maximum FoV of ~18 deg2 when four telescopes monitor different sky areas in monochromatic filter mode. For an exposure time of 60 s, the average 3σ detection limit of the TMTS system can reach at ~19.4 mag in Luminous filter and at ~18.7 mag in SDSS r filter. The preliminary discovery obtained during the first few months' survey is briefly discussed. As this telescope array is located at the Xinglong Observatory of NAOC, it can have an excellent synergy with the spectroscopic survey by the LAMOST (with a FoV of about 20 deg2) at the same site, which will benefit the studies of stellar and binary physics besides the transient sciences.

astro-ph.IM↗

arXiv2020

Quantum Slide and NAND Tree on a Photonic Chip

In the age of post-Moore era, the next-generation computing model would be a hybrid architecture consisting of different physical components such as photonic chips. In 2008, it has been proposed that the solving of NAND-tree problem can be sped up by quantum walk. Such scheme is groundbreaking due to the universality of NAND gate. However, experimental demonstration has never been achieved so far, mostly due to the challenge in preparing the propagating initial state. Here we propose an alternative solution by including a structure called "quantum slide", where a propagating Gaussian wave-packet can be generated deterministically along a properly-engineered chain. In this way, the optical computation can be achieved with ordinary laser light instead of single photon, and the output can be obtained by single-shot measurements instead of repeated quantum measurements. In our experimental demonstration, the optical NAND-tree is capable of solving computational problems with a total of four input bits, based on the femtosecond laser 3D direct-writing technique on a photonic chip. These results remove one main roadblock to photonic NAND-tree computation, and the construction of quantum slide may find other interesting applications in quantum information and quantum optics.

quant-ph↗

arXiv2019

Low-Depth Optical Neural Networks

Optical neural network (ONN) is emerging as an attractive proposal for machine-learning applications, enabling high-speed computation with low-energy consumption. However, there are several challenges in applying ONN for industrial applications, including the realization of activation functions and maintaining stability. In particular, the stability of ONNs decrease with the circuit depth, limiting the scalability of the ONNs for practical uses. Here we demonstrate how to compress the circuit depth of ONN to scale only logarithmically, leading to an exponential gain in terms of noise robustness. Our low-depth (LD) ONN is based on an architecture, called Optical CompuTing Of dot-Product UnitS (OCTOPUS), which can also be applied individually as a linear perceptron for solving classification problems. Using the standard data set of Letter Recognition, we present numerical evidence showing that LD-ONN can exhibit a significant gain in noise robustness, compared with a previous ONN proposal based on singular-value decomposition [Nature Photonics 11, 441 (2017)].

physics.optics↗

arXiv2019

Minimal Non-orthogonal Gate Decomposition for Qubits with Limited Control

In quantum control theory, a question of fundamental and practical interest is how an arbitrary unitary transformation can be decomposed into minimum number of elementary rotations for implementation, subject to various physical constraints. Examples include the singlet-triplet (ST) and exchange-only (EO) qubits in quantum-dot systems, and gate construction in the Solovay-Kitaev algorithm. For two important scenarios, we present complete solutions to the problems of optimal decomposition of single-qubit unitary gates with non-orthogonal rotations. For each unitary gate, we give the criteria for determining the minimal number of pieces, the explicit gate construction procedure, as well as a computer code for practical uses. Our results include an analytic explanation to the four-gate decomposition of EO qubits, previously determined numerically by Divincenzo et al [Nature, 408, 339 (2000)]. Furthermore, compared with the approaches of Ramon sequence and its variant [Phys. Rev. Lett., 118, 216802 (2017)], our method can reduce about 50% of gate time for ST qubits. Finally, our approach can be extended to solve the problem of optimal control of topological qubits, where gate construction is achieved through the braiding operations.

cond-mat.mes-hall↗

arXiv2018

Automatic spin-chain learning to explore the quantum speed limit

One of the ambitious goals of artificial intelligence is to build a machine that outperforms human intelligence, even if limited knowledge and data are provided. Reinforcement Learning (RL) provides one such possibility to reach this goal. In this work, we consider a specific task from quantum physics, i.e. quantum state transfer in a one-dimensional spin chain. The mission for the machine is to find transfer schemes with fastest speeds while maintaining high transfer fidelities. The first scenario we consider is when the Hamiltonian is time-independent. We update the coupling strength by minimizing a loss function dependent on both the fidelity and the speed. Compared with a scheme proven to be at the quantum speed limit for the perfect state transfer, the scheme provided by RL is faster while maintaining the infidelity below $5\times 10^{-4}$. In the second scenario where a time-dependent external field is introduced, we convert the state transfer process into a Markov decision process that can be understood by the machine. We solve it with the deep Q-learning algorithm. After training, the machine successfully finds transfer schemes with high fidelities and speeds, which are faster than previously known ones. These results show that Reinforcement Learning can be a powerful tool for quantum control problems.

cond-mat.mes-hall↗

arXiv2015

Unprecedentedly Wide Curie-Temperature Windows as Phase-Transition Design Platform for Tunable Magneto-Multifunctional Materials

A series of unprecedentedly wide Curie-temperature windows (CTWs) between 40 and 450 K are realized by employing the isostructural alloying principle for the strongly coupled magnetostructural phase transitions in a single host system. The CTWs provide a design platform for magneto-multifunctional multiferroic alloys that can be manipulated in a quite large temperature space in various scales and patterns, as well as by multiple physical fields.

cond-mat.mtrl-sci↗