arXiv subjects

Xiangkai Sun

Publications and source records attributed to Xiangkai Sun.

16 recordsLinked to original sources

arXiv2026

Inertial Primal-Dual Dynamics Methods Featuring Implicit Hessian-Driven Damping for Convex Optimization Problems in Continuous and Discrete Time

This paper investigates inertial primal-dual dynamics with implicit Hessian-driven damping for strongly convex optimization problems with linear equality constraints. We first establish fast convergence rates for the objective function value error, the feasibility measure, and both the trajectory and its corresponding velocity vector. By appropriately adjusting parameters, we show that the proposed system achieves exponential convergence rates. Through implicit time discretization of the dynamical system, we derive an inertial accelerated primal-dual algorithm for solving the strongly convex optimization problems. Using Lyapunov-based method, we show that the proposed algorithm achieves convergence rates consistent with those of its continuous-time counterpart. We also extend the obtained results to non-smooth convex optimization case. Furthermore, we conduct numerical experiments to illustrate theoretical results.

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arXiv2026

Primal-dual dynamics featuring Hessian-driven damping and variable mass for convex optimization problems

This paper deals with a new Tikhonov regularized primal-dual dynamical system with variable mass and Hessian-driven damping for solving a convex optimization problem with linear equality constraints. The system features several time-dependent parameters: variable mass, slow viscous damping, extrapolation, and temporal scaling. By employing the Lyapunov analysis approach, we obtain the strong convergence of the trajectory generated by the proposed system to the minimal norm solution of the optimization problem, as well as convergence rate results for the primal-dual gap, the objective residual, and the feasibility violation. We also show that the convergence rates of the primal-dual gap, the objective residual, and the feasibility violation can be improved by appropriately adjusting these parameters. Further, we conduct numerical experiments to demonstrate the effectiveness of the theoretical results.

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arXiv2026

Convergence analysis of accelerated algorithms via a mixed-order dynamical system for separable nonsmooth convex optimization

For a linear equality constrained convex optimization problem involving two objective functions with a ``nonsmooth" + ``nonsmooth" composite structure, we study two algorithms derived from a mixed-order dynamical system which incorporates time scales and a Tikhonov regularization term. We observe that different types of multipliers lead to distinct algorithms. For the implicit multiplier and semi-implicit multiplier, we develop a new primal-dual joint algorithm and a new splitting algorithm, respectively. Our proposed joint algorithm can reduce to an algorithm for solving the corresponding non-separable linearly constrained convex optimization problem. Then, we establish the nonergodic convergence properties of all our proposed algorithms. Moreover, we derive that the sequences generated by these algorithms strongly converge to the minimal norm solution. Finally, numerical experiments are conducted to validate the practical performance of the proposed algorithms.

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arXiv2026

Gate-based Readout and Cooling of Neutral Atoms

Neutral atom arrays have seen tremendous progress in quantum simulation, quantum metrology, and fault-tolerant quantum computing. However, hardware constraints such as atom loss and heating remain significant challenges. In this work, we introduce a comprehensive ancilla-based toolbox for optical tweezer experiments that utilizes high-fidelity Rydberg entangling gates and ancilla atoms to mitigate these physical limitations. First, we demonstrate repeated ancilla-based atom readout, achieving improved detection fidelity over multiple rounds with minimal perturbation to data atoms. Second, leveraging the quantized motional states in tweezer-trapped strontium atoms, we transduce quantum information from the electronic to the motional manifold. This enables us to perform mid-circuit ancilla-based atom loss detection in a coherence-preserving fashion. Finally, we demonstrate algorithmic cooling, a circuit-based sequence that deterministically cools data atoms by transferring their motional entropy to the electronic states of ancilla atoms. We observe a marked reduction in the atomic temperature of data atoms. These tools offer a pathway to continuous operation in tweezer clocks and complement recent developments in continuous reloading experiments.

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arXiv2026

Primal-dual dynamical systems with closed-loop control for convex optimization in continuous and discrete time

This paper develops a primal-dual dynamical system where the coefficients are designed in closed-loop way for solving a convex optimization problem with linear equality constraints. We first introduce a ``second-order primal" + ``first-order dual'' continuous-time dynamical system, in which both the time scaling and Hessian-driven damping are governed by a feedback control of the gradient for the Lagrangian function. This system achieves the fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual along its trajectory. Subsequently, by time discretization of this system, we develop an accelerated primal-dual algorithm with a gradient-defined adaptive step size. We also obtain convergence rates for the primal-dual gap, the feasibility violation, and the objective residual. Furthermore, we provide numerical results to demonstrate the practical efficacy and superior performance of the proposed algorithm.

math.OC↗

arXiv2026

Inertial dynamical systems and accelerated algorithms with implicit Hessian-driven damping for nonconvex optimization

This paper is devoted to the investigation of inertial dynamical systems with implicit Hessian-driven damping for strongly quasiconvex optimization which is a specific class of nonconvex optimization problems. We first establish exponential convergence rate properties for this system without requiring Lipschitz continuity of the gradient on the function. Then, we obtain an inertial accelerated algorithm for minimizing strongly quasiconvex functions through natural explicit time discretization to the dynamical system. Meanwhile, we consider an exogenous additive perturbation term to this dynamical system and obtain the corresponding algorithm. By utilizing the Lyapunov method, we establish convergence rates of iterative sequences and their function values. Furthermore, we conduct numerical experiments to illustrate the theoretical results.

math.OC↗

arXiv2026

Experimental observation of conformal field theory spectra

Conformal field theories (CFTs) feature prominently in high-energy physics, statistical mechanics, and condensed matter. For example, CFTs govern emergent universal properties of systems tuned to quantum phase transitions, including their entanglement, correlations, and low-energy excitation spectra. Much of the rich structure predicted by CFTs nevertheless remains unobserved in experiment. Here we directly observe the energy excitation spectra of emergent CFTs at quantum phase transitions -- recovering universal energy ratios characteristic of the underlying field theories. Specifically, we develop and implement a modulation technique to resolve a Rydberg chain's finite-size spectra, variably tuned to quantum phase transitions described by either Ising or tricritical Ising CFTs. We also employ local control to distinguish parities of excitations under reflection and, in the tricritical Ising chain, to induce transitions between distinct CFT spectra associated with changing boundary conditions. By utilizing a variant of the modulation technique, we furthermore study the dynamical structure factor of the critical system, which is closely related to the correlation of an underlying Ising conformal field. Our work not only probes the emergence of CFT features in a quantum simulator, but also provides a technique for diagnosing a priori unknown universality classes in future experiments.

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arXiv2025

Inertial accelerated primal-dual algorithms for non-smooth convex optimization problems with linear equality constraints

This paper is devoted to the study of an inertial accelerated primal-dual algorithm, which is based on a second-order differential system with time scaling, for solving a non-smooth convex optimization problem with linear equality constraints. We first introduce a second-order differential system with time scaling associated with the non-smooth convex optimization problem, and then obtain fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual along the trajectory generated by this system. Subsequently, based on the setting of the parameters involved, we propose an inertial accelerated primal-dual algorithm from the time discretization of this system. We also establish fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual. Furthermore, we demonstrate the efficacy of the proposed algorithm through numerical experiments.

math.OC↗

arXiv2025

Convergence analysis of a Tikhonov regularized inertial dynamical system and algorithm for convex optimization problems

This paper deals with a Tikhonov regularized second-order inertial dynamical system that incorporates time scaling, asymptotically vanishing damping and Hessian-driven damping for solving convex optimization problems. Under appropriate setting of the parameters, we first obtain fast convergence results of the function value along the trajectory generated by the dynamical system. Then, we show that the trajectory generated by the dynamical system converges weakly to a minimizer of the convex optimization problem. We also demonstrate that, by properly tuning these parameters, both fast convergence rates of the function value and strong convergence of the trajectory towards the minimum norm solution of the convex optimization problem can be achieved simultaneously. Furthermore, we study convergence properties of an inertial proximal gradient algorithm obtained by the temporal discretization of the dynamical system. Finally, we present numerical experiments to illustrate the obtained results.

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arXiv2024

Inertial primal-dual dynamics with Hessian-driven damping and Tikhonov regularization for convex-concave bilinear saddle point problems

This paper deals with a second-order primal-dual dynamical system with Hessian-driven damping and Tikhonov regularization terms in connection with a convex-concave bilinear saddle point problem. We first obtain a fast convergence rate of the primal-dual gap along the trajectory generated by the dynamical system, and provide some integral estimates. Then, based on the setting of the parameters involved, we demonstrate that both the convergence rate of the primal-dual gap and the strong convergence of the trajectory can be achieved simultaneously. Furthermore, we evaluate the performance of the proposed system using two numerical examples.

math.OC↗

arXiv2024

Tikhonov regularized inertial primal-dual dynamics for convex-concave bilinear saddle point problems

In this paper, for a convex-concave bilinear saddle point problem, we propose a Tikhonov regularized second-order primal-dual dynamical system with slow damping, extrapolation and general time scaling parameters. Depending on the vanishing speed of the rescaled regularization parameter (i.e., the product of Tikhonov regularization parameter and general time scaling parameter), we analyze the convergence properties of the trajectory generated by the dynamical system. When the rescaled regularization parameter decreases rapidly to zero, we obtain convergence rates of the primal-dual gap and velocity vector along the trajectory generated by the dynamical system. In the case that the rescaled regularization parameter tends slowly to zero, we show the strong convergence of the trajectory towards the minimal norm solution of the convex-concave bilinear saddle point problem. Further, we also present some numerical experiments to illustrate the theoretical results.

math.OC↗

arXiv2024

Tikhonov regularization of second-order plus first-order primal-dual dynamical systems for separable convex optimization

This paper deals with a Tikhonov regularized second-order plus first-order primal-dual dynamical system with time scaling for separable convex optimization problems with linear equality constraints. This system consists of two second-order ordinary differential equations for the primal variables and one first-order ordinary differential equation for the dual variable.By utilizing the Lyapunov analysis approach, we obtain the convergence properties of the primal-dual gap, the objective function error, the feasibility measure and the gradient norm of the objective function along the trajectory. We also establish the strong convergence of the primal trajectory generated by the dynamical system towards the minimal norm solution of the separable convex optimization problem. Furthermore, we give numerical experiments to illustrate the theoretical results, showing that our dynamical system performs better than those in the literature in terms of convergence rates.

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arXiv2024

Benchmarking and fidelity response theory of high-fidelity Rydberg entangling gates

The fidelity of entangling operations is a key figure of merit in quantum information processing, especially in the context of quantum error correction. High-fidelity entangling gates in neutral atoms have seen remarkable advancement recently. A full understanding of error sources and their respective contributions to gate infidelity will enable the prediction of fundamental limits on quantum gates in neutral atom platforms with realistic experimental constraints. In this work, we implement the time-optimal Rydberg CZ gate, design a circuit to benchmark its fidelity, and achieve a fidelity, averaged over symmetric input states, of 0.9971(5), downward-corrected for leakage error, which together with our recent work forms a new state-of-the-art for neutral atoms. The remaining infidelity is explained by an ab initio error model, consistent with our experimental results over a range of gate speeds, with varying contributions from different error sources. Further, we develop a fidelity response theory to efficiently predict infidelity from laser noise with non-trivial power spectral densities and derive scaling laws of infidelity with gate speed. Besides its capability of predicting gate fidelity, we also utilize the fidelity response theory to compare and optimize gate protocols, to learn laser frequency noise, and to study the noise response for quantum simulation tasks. Finally, we predict that a CZ gate fidelity of ${\gtrsim} 0.999$ is feasible with realistic experimental upgrades.

quant-ph↗

arXiv2024

Universal quantum operations and ancilla-based readout for tweezer clocks

Enhancing the precision of measurements by harnessing entanglement is a long-sought goal in the field of quantum metrology. Yet attaining the best sensitivity allowed by quantum theory in the presence of noise is an outstanding challenge, requiring optimal probe-state generation and readout strategies. Neutral atom optical clocks, leading systems for measuring time, have shown recent progress in terms of entanglement generation, but currently lack the control capabilities to realize such schemes. Here we show universal quantum operations and ancilla-based readout for ultranarrow optical transitions of neutral atoms. Our demonstration in a tweezer clock platform enables a circuit-based approach to quantum metrology with neutral atom optical clocks. To this end, we demonstrate two-qubit entangling gates with 99.62(3)% fidelity - averaged over symmetric input states - via Rydberg interactions and dynamical connectivity for optical clock qubits, which we combine with local addressing to implement universally programmable quantum circuits. Using this approach, we generate a near-optimal entangled probe state, a cascade of Greenberger-Horne-Zeilinger (GHZ) states of different sizes, and perform dual-quadrature GHZ readout. We also show repeated fast phase detection with non-destructive conditional reset of clock qubits and minimal dead time between repetitions by implementing ancilla-based quantum logic spectroscopy (QLS) for neutral atoms. Finally, we extend this to multi-qubit parity checks and measurement-based, heralded, Bell state preparation. Our work lays the foundation for hybrid processor-clock devices with neutral atoms and more generally points to a future of practical applications for quantum processors linked with quantum sensors.

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arXiv2023

Quantum Computation and Simulation using Fermion-Pair Registers

We propose and analyze an approach to realize quantum computation and simulation using fermionic particles under quantum gas microscopes. Our work is inspired by a recent experimental demonstration of large-scale quantum registers, where tightly localized fermion pairs are used to encode qubits exhibiting long coherence time and robustness against laser intensity noise. We describe how to engineer the SWAP gate and high-fidelity controlled-phase gates by adjusting the fermion hopping as well as Feshbach interaction strengths. Combined with previously demonstrated single-qubit rotations, these gates establish the computational universality of the system. Furthermore, we show that 2D quantum Ising Hamiltonians with tunable transverse and longitudinal fields can be efficient simulated by modulating Feshbach interaction strengths. We present a sample-efficient protocol to characterize engineered gates and Hamiltonian dynamics based on an improved classical shadow process tomography that requires minimal experimental controls. Our work opens up new opportunities to harness existing ultracold quantum gases for quantum information sciences.

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arXiv2022

Finite-Volume Pionless Effective Field Theory for Few-Nucleon Systems with Differentiable Programming

Finite-volume pionless effective field theory provides an efficient framework for the extrapolation of nuclear spectra and matrix elements calculated at finite volume in lattice QCD to infinite volume, and to nuclei with larger atomic number. In this work, it is demonstrated how this framework may be implemented via a set of correlated Gaussian wavefunctions optimised using differentiable programming and via solution of a generalised eigenvalue problem. This approach is shown to be significantly more efficient than a stochastic implementation of the variational method based on the same form of correlated Gaussian wavefunctions, yielding comparably accurate representations of the ground-state wavefunctions with an order of magnitude fewer terms. The efficiency of representation allows such calculations to be extended to larger systems than in previous work. The method is demonstrated through calculations of the binding energies of nuclei with atomic number $A\in\{2,3,4\}$ in finite volume, matched to lattice QCD calculations at quark masses corresponding to $m_\pi=806$ MeV, and infinite-volume effective field theory calculations of $A\in\{2,3,4,5,6\}$ systems based on this matching.

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