arXiv subjects

Yue Sun

Publications and source records attributed to Yue Sun.

At least 127 records · Page 7Linked to original sources

arXiv2024

A fully-integrated lattice Boltzmann method for fluid-structure interaction

We present a fully-integrated lattice Boltzmann (LB) method for fluid--structure interaction (FSI) simulations that efficiently models deformable solids in complex suspensions and active systems. Our Eulerian method (LBRMT) couples finite-strain solids to the LB fluid on the same fixed computational grid with the reference map technique (RMT). An integral part of the LBRMT is a new LB boundary condition for moving deformable interfaces across different densities. With this fully Eulerian solid--fluid coupling, the LBRMT is well-suited for parallelization and simulating multi-body contact without remeshing or extra meshes. We validate its accuracy via a benchmark of a deformable solid in a lid-driven cavity, then showcase its versatility through examples of soft solids rotating and settling. With simulations of complex suspensions mixing, we highlight potentials of the LBRMT for studying collective behavior in soft matter and biofluid dynamics.

physics.flu-dyn↗

arXiv2024

Study of $B_s \to ϕ(ρ^0, ω)$ Decays in Standard Model and Family Non-universal $Z^\prime$ Model

Within QCD factorization approach, we employ the new results of form factors of $B_s \to ϕ$ and calculate the branching fractions, CP asymmetries (CPAs) and polarization fractions of the decay modes $B_s \to ϕ(ρ^0, ω)$ in both the standard model (SM) and the family non-universal $Z^\prime$ model. We find that in SM the above observables are relate to two parameters $ρ_H$ and $ϕ_H$, which characterize the end-point divergence in the hard spectator-scattering amplitudes. When setting $ρ_H=0.5$ and $ϕ_H\in [-180,180]^\circ$, the theoretical uncertainties are large. By combining the experimental data, our results can be used to constrain these two parameters. Supposing $ρ_H=0$, we study the effects of $Z^\prime$ boson on the concerned observables. With the available branching fraction of $B_s \to ϕρ^0$, the possible ranges of parameter spaces are obtained. Within the allowed parameter spaces, the branching fraction of $B_s \to ϕω$ can be enlarged remarkably. Furthermore, the new introduced weak phase plays important roles in significantly affecting the CPAs and polarization fractions, which are important observables for probing the effects of NP. If these decay modes were measured in the on-going LHC-b and Belle-II experiments in future, the peculiar deviation from SM could provide a signal of the family non-universal $Z^\prime$ model, which can also be used to constrain the mass of $Z^\prime$ boson in turn.

hep-ph↗

arXiv2024

Quantum option pricing via the Karhunen-Loève expansion

We consider the problem of pricing discretely monitored Asian options over $T$ monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide two quantum algorithms with complexity poly-logarithmic in $T$ and polynomial in $1/ε$, where $ε$ is the additive approximation error. Our algorithms are obtained respectively by using an $O(\log T)$-qubit semi-digital quantum encoding of the Brownian motion that allows for exponentiation of the stochastic process and by analyzing classical Monte Carlo algorithms inspired by the semi-digital encodings. The best quantum algorithm obtained using this approach has complexity $\widetilde{O}(1/ε^{3})$ where the $\widetilde{O}$ suppresses factors poly-logarithmic in $T$ and $1/ε$. The methods proposed in this work generalize to pricing options where the underlying asset price is modeled by a smooth function of a sub-Gaussian process and the payoff is dependent on the weighted time-average of the underlying asset price.

quant-ph↗

arXiv2024

Parameter Setting in Quantum Approximate Optimization of Weighted Problems

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challenge of parameter optimization is particularly acute in the case of weighted problems, for which the eigenvalues of the phase operator are non-integer and the QAOA energy landscape is not periodic. In this work, we develop parameter setting heuristics for QAOA applied to a general class of weighted problems. First, we derive optimal parameters for QAOA with depth $p=1$ applied to the weighted MaxCut problem under different assumptions on the weights. In particular, we rigorously prove the conventional wisdom that in the average case the first local optimum near zero gives globally-optimal QAOA parameters. Second, for $p\geq 1$ we prove that the QAOA energy landscape for weighted MaxCut approaches that for the unweighted case under a simple rescaling of parameters. Therefore, we can use parameters previously obtained for unweighted MaxCut for weighted problems. Finally, we prove that for $p=1$ the QAOA objective sharply concentrates around its expectation, which means that our parameter setting rules hold with high probability for a random weighted instance. We numerically validate this approach on general weighted graphs and show that on average the QAOA energy with the proposed fixed parameters is only $1.1$ percentage points away from that with optimized parameters. Third, we propose a general heuristic rescaling scheme inspired by the analytical results for weighted MaxCut and demonstrate its effectiveness using QAOA with the XY Hamming-weight-preserving mixer applied to the portfolio optimization problem. Our heuristic improves the convergence of local optimizers, reducing the number of iterations by 7.4x on average.

quant-ph↗

arXiv2024

Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups

Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 1209 (2022)] we study the maximum independent set problem on unit-disk graphs with a broader range of classical solvers beyond the scope of the original paper. We carry out extensive numerical studies and assess problem hardness, using both exact and heuristic algorithms. We find that quasi-planar instances with Union-Jack-like connectivity can be solved to optimality for up to thousands of nodes within minutes, with both custom and generic commercial solvers on commodity hardware, without any instance-specific fine-tuning. We also perform a scaling analysis, showing that by relaxing the constraints on the classical simulated annealing algorithms considered in Ebadi et al., our implementation is competitive with the quantum algorithms. Conversely, instances with larger connectivity or less structure are shown to display a time-to-solution potentially orders of magnitudes larger. Based on these results we propose protocols to systematically tune problem hardness, motivating experiments with Rydberg atom arrays on instances orders of magnitude harder (for established classical solvers) than previously studied.

quant-ph↗

arXiv2024

Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization

Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In this paper, we demonstrate that the intuition from the adiabatic algorithm applies to the task of choosing the QAOA initial state. Specifically, we observe that the best performance is obtained when the initial state of QAOA is set to be the ground state of the mixing Hamiltonian, as required by the adiabatic algorithm. We provide numerical evidence using the examples of constrained portfolio optimization problems with both low ($p\leq 3$) and high ($p = 100$) QAOA depth. Additionally, we successfully apply QAOA with XY mixer to portfolio optimization on a trapped-ion quantum processor using 32 qubits and discuss our findings in near-term experiments.

quant-ph↗

arXiv2023

Distance Guided Generative Adversarial Network for Explainable Binary Classifications

Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space. Specifically, we instantiate the idea of DisGAN by combining two ways. The first way is vertical distance GAN (VerDisGAN) where the inter-domain generation is conditioned on the vertical distances. The second way is horizontal distance GAN (HorDisGAN) where the intra-domain generation is conditioned on the horizontal distances. Furthermore, VerDisGAN can produce the class-specific regions by mapping the source images to the hyperplane. Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification. The proposed method can apply to different classification architectures and has potential to extend to multi-class classification.

cs.CV↗

arXiv2023

A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

cs.CV↗

arXiv2023

Quantum Deep Hedging

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

quant-ph↗

arXiv2023

On convergence and optimality of maximum-likelihood APA

Affine projection algorithm (APA) is a well-known algorithm in adaptive filtering applications such as audio echo cancellation. APA relies on three parameters: $P$ (projection order), $μ$ (step size) and $δ$ (regularization parameter). It is known that running APA for a fixed set of parameters leads to a tradeoff between convergence speed and accuracy. Therefore, various methods for adaptively setting the parameters have been proposed in the literature. Inspired by maximum likelihood (ML) estimation, we derive a new ML-based approach for adaptively setting the parameters of APA, which we refer to as ML-APA. For memoryless Gaussian inputs, we fully characterize the expected misalignment error of ML-APA as a function of iteration number and show that it converges to zero as $O({1\over t})$. We further prove that the achieved error is asymptotically optimal. ML-APA updates its estimate once every $P$ samples. We also propose incremental ML-APA (IML-APA), which updates the coefficients at every time step and outperforms ML-APA in our simulations results. Our simulation results verify the analytical conclusions for memoryless inputs and show that the new algorithms also perform well for strongly correlated input signals.

cs.IT↗

arXiv2023

Private Inputs for Leader-Follower Game with Feedback Stackelberg Strategy

In this paper, the two-player leader-follower game with private inputs for feedback Stackelberg strategy is considered. In particular, the follower shares its measurement information with the leader except its historical control inputs while the leader shares none of the historical control inputs and the measurement information with the follower. The private inputs of the leader and the follower lead to the main obstacle, which causes the fact that the estimation gain and the control gain are related with each other, resulting that the forward and backward Riccati equations are coupled and making the calculation complicated. By introducing a kind of novel observers through the information structure for the follower and the leader, respectively, a kind of new observer-feedback Stacklberg strategy is designed. Accordingly, the above-mentioned obstacle is also avoided. Moreover, it is found that the cost functions under the presented observer-feedback Stackelberg strategy are asymptotically optimal to the cost functions under the optimal feedback Stackelberg strategy with the feedback form of the state. Finally, a numerical example is given to show the efficiency of this paper.

math.OC↗

arXiv2023

Fast Simulation of High-Depth QAOA Circuits

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA). Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization and supports both CPU and GPU execution. Our central observation is that the computational cost of both simulating the QAOA state and computing the QAOA objective to be optimized can be reduced by precomputing the diagonal Hamiltonian encoding the problem. We reduce the time for a typical QAOA parameter optimization by eleven times for $n = 26$ qubits compared to a state-of-the-art GPU quantum circuit simulator based on cuQuantum. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit

quant-ph↗

arXiv2023

Universal spin wavepacket transport in van der Waals antiferromagnets

Antiferromagnets (AFMs) are promising platforms for the transmission of quantum information via magnons (the quanta of spin waves), offering advantages over ferromagnets with regard to dissipation, speed of response, and immunity to external fields. Recently, it was shown that in the insulating van der Waals (vdW) semiconductor, CrSBr, strong spin-exciton coupling enables readout of magnon density and propagation using photons of visible light. This exciting observation came with a puzzle: photogenerated magnons were observed to propagate 10$^3$ times faster than the velocity inferred from neutron scattering, leading to a conjecture that spin wavepackets are carried along by coupling to much faster elastic modes. Here we show, through a combination of theory and experiment, that the propagation mechanism is, instead, coupling within the magnetic degrees of freedom through long range dipole-dipole coupling. This mechanism is an inevitable consequence of Maxwell's equations, and as such, will dominate the propagation of spin at long wavelengths in the entire class of vdW magnets currently under intense investigation. Moreover, identifying the mechanism of spin propagation provides a set of optimization rules, as well as caveats, that are essential for any future applications of these promising systems.

cond-mat.str-el↗

arXiv2023

Fractional Quantum Zeno Effect Emerging from Non-Hermitian Physics

Exploring non-Hermitian phenomenology is an exciting frontier of modern physics. However, the demonstration of a non-Hermitian phenomenon that is quantum in nature has remained elusive. Here, we predict quantum non-Hermitian phenomena: the fractional quantum Zeno (FQZ) effect and FQZ-induced photon antibunching. We consider a quantum optics platform with reservoir engineering, where nonlinear emitters are coupled to a bath of decaying bosonic modes whose own decay rates form band structures. By engineering the dissipation band, the spontaneous emission of emitters can be suppressed by strong dissipation through an algebraic scaling with fractional exponents - the FQZ effect. This fractional scaling originates uniquely from the divergent dissipative density of states near the dissipation band edge, different from the traditional closed-bath context. We find FQZ-induced strong photon antibunching in the steady state of a driven emitter even for weak nonlinearities. Remarkably, we identify that the sub-Poissonian quantum statistics of photons, which has no classical analogs, stems here from the key role of non-Hermiticity. Our setup is experimentally feasible with the techniques used to design lattice models with dissipative couplings.

quant-ph↗

arXiv2023

Constrained Optimization via Quantum Zeno Dynamics

Constrained optimization problems are ubiquitous in science and industry. Quantum algorithms have shown promise in solving optimization problems, yet none of the current algorithms can effectively handle arbitrary constraints. We introduce a technique that uses quantum Zeno dynamics to solve optimization problems with multiple arbitrary constraints, including inequalities. We show that the dynamics of quantum optimization can be efficiently restricted to the in-constraint subspace on a fault-tolerant quantum computer via repeated projective measurements, requiring only a small number of auxiliary qubits and no post-selection. Our technique has broad applicability, which we demonstrate by incorporating it into the quantum approximate optimization algorithm (QAOA) and variational quantum circuits for optimization. We evaluate our method numerically on portfolio optimization problems with multiple realistic constraints and observe better solution quality and higher in-constraint probability than state-of-the-art techniques. We implement a proof-of-concept demonstration of our method on the Quantinuum H1-2 quantum processor.

quant-ph↗

arXiv2023

Observation of zero resistance above 100$^\circ$ K in Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O

Room-temperature superconductivity has always been regarded as the ultimate goal in the fields of solid-state physics and materials science, with its realization holding revolutionary significance, capable of triggering significant changes in energy transmission and storage. However, achieving it poses various challenges. Recent research revealed that material Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O displays room-temperature superconductivity under atmospheric pressure, sparking global interest in further exploration. Here, we utilized solid-phase synthesis to obtain a polycrystalline sample of Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O. X-ray diffraction confirmed its structural consistency with referenced literature. Zero resistance, which is important evidence for superconductivity, was observed above 100$^\circ$ K under ambient pressure in our experiment. Our finding indicates that Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O is a possible candidate for searching high-temperature superconductors.

cond-mat.supr-con↗

arXiv2023

Quantum computing for finance

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle.

quant-ph↗

arXiv2023

Novel Anisotropy of Upper Critical Fields in Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$

Studying the upper critical field ($μ_0$$H$$_{\rm{c2}}$) and its anisotropy of superconductors is of great importance because it can provide an unusual insight into the pair-breaking mechanism. Since Fe$_{1+y}$Te$_{1-x}$Se$_x$ exhibits the high $μ_0$$H$$_{\rm{c2}}$ and small anisotropic superconductivity, it has attracted considerable attention. However, some issues related to $μ_0$$H$$_{\rm{c2}}$ are still unknown, including the effect of excess Fe content on $μ_0$$H$$_{\rm{c2}}$ behavior and the origin of the crossover of the $μ_0H_{\rm{c2}}^c $ -- $ T$ and $μ_0H_{\rm{c2}}^{ab}$ -- $T$ curves. In this work, the value of $μ_0$$H$$_{\rm{c2}}$ of Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ single crystals with controlled amounts of excess Fe was obtained by resistivity measurements over a wide range of temperatures down to $\sim$ 1.5 K, and magnetic fields up to $\sim$ 60 T. The crossover of the $μ_0H_{\rm{c2}}^c $ -- $ T$ and $μ_0H_{\rm{c2}}^{ab}$ -- $T$ curves was found to be independent of the excess Fe content. The angle dependence of $μ_0H_{\rm{c2}}$ was also checked. The $μ_0H_{\rm{c2}}(θ)$ symmetry at higher temperature near $T_c$ could be fitted by anisotropic G-L model, and novel fourfold symmetry of $μ_0H_{\rm{c2}}$ at lower temperature was found. Based on our spin-locking pairing model, the crossover behavior originates from the anisotropic spin-paramagnetic effect, and the novel fourfold symmetry of $μ_0H_{\rm{c2}}$ could be understood by our extended anisotropic G-L model.

cond-mat.supr-con↗