arXiv subjects

Oliver Tse

Publications and source records attributed to Oliver Tse.

At least 19 recordsLinked to original sources

arXiv2026

Probabilistic representation and limit theorems for particle numbers of quasi-free states

We study the particle number distribution of locally interacting bosonic quasi- free states, which arise in various areas of mathematical physics. We show that the particle number decomposes into an infinite sum of independent geometrically distributed random variables, confirming predictions from the physics literature. This representation yields exponential tail bounds for the particle number, a law of small numbers together with a large deviation principle at logarithmic speed when the lattice spacing diverges, and a central limit theorem when it vanishes. As an application, we obtain a detailed description of the statistics of the quantum depletion in Bose--Einstein condensates, with the constants explicit in terms of the scattering length of the interaction potential.

math-ph↗

arXiv2026

Stochastic convergence of a class of greedy-type algorithms for Configuration Optimization Problems

Greedy Sampling Methods (GSMs) are widely used to construct approximate solutions of Configuration Optimization Problems (COPs), where a loss functional is minimized over finite configurations of points in a compact domain. While effective in practice, deterministic convergence analyses of greedy-type algorithms are often restrictive and difficult to verify. We propose a stochastic framework in which greedy-type methods are formulated as continuous-time Markov processes on the space of configurations. This viewpoint enables convergence analysis in expectation and in probability under mild structural assumptions on the error functional and the transition kernel. For global error functionals, we derive explicit convergence rates, including logarithmic, polynomial, and exponential decay, depending on an abstract improvement condition. As a pedagogical example, we study stochastic greedy sampling for one-dimensional piecewise linear interpolation and prove exponential convergence of the $L^1$-interpolation error for $C^2$-functions. Motivated by this analysis, we introduce the Randomized Polytope Division Method (R-PDM), a randomized variant of the classical Polytope Division Method, and demonstrate its effectiveness and variance reduction in numerical experiments

math.OC↗

arXiv2025

Quantum relative entropy for unravelings of master equations

This work explores connections between the quantum relative entropy of two faithful states $\rho,\sigma$ (i.e. full-rank density matrices) and the Kullback-Leibler divergences of classical measures $\mu,\nu$. Here, $\mu$ and $\nu$ are measures on the space of pure states, realizing $\rho$ and $\sigma$ respectively. The motivation for this result is to establish a notion of quantum relative entropy in the space of pure state distributions, which are the resulting objects of unravelings of the Lindblad equation, such as the stochastic Schr\"{o}dinger equation. Our results show that the measures that achieve the minimal KL divergence are those supported on a (possibly non-orthogonal) common basis between $\rho$ and $\sigma$. Using the classical and quantum data-processing inequalities, our notion of quantum relative entropy is shown to be equivalent to the Belavkin-Staszewski entropy on states, revealing new insights on this quantity. Furthermore, the common basis is used to provide a novel proof of contraction of the relative entropy under Lindblad flow and offers insights into results from large deviation theory.

quant-ph↗

arXiv2025

Exponential and algebraic decay in Euler--alignment system with nonlocal interaction forces

We investigate the large-time behavior of the pressureless Euler system with nonlocal velocity alignment and interaction forces, with the aim of characterizing the asymptotic convergence of classical solutions under general interaction potentials $W$ and communication weights. We establish quantitative convergence in three settings. In one dimension with $(\lambda,\Lambda)$-convex potentials, i.e., potentials satisfying uniform lower and upper quadratic bounds, bounded communication weights yield exponential decay, while weakly singular ones lead to sharp algebraic rates. For the Coulomb--quadratic potential $W(x)=-|x|+\frac12 |x|^2$, we prove exponential convergence for bounded communication weights and algebraic upper bounds for singular communication weights. In a multi-dimensional setting with uniformly $(\lambda,\Lambda)$-convex potentials, we show exponential decay for bounded weights and improved algebraic decay for singular ones. In all cases, the density converges (up to translation) to the minimizer of the interaction energy, while the velocity aligns to a uniform constant. A unifying feature is that the convergence rate depends only on the local behavior of communication weights: bounded kernels yield exponential convergence, while weakly singular ones produce algebraic rates. Our results thus provide a comprehensive description of the asymptotic behavior of Euler--alignment dynamics with general interaction potentials.

math.AP↗

arXiv2025

Singular jump processes as generalized gradient flows

We extend the generalized gradient-flow framework of Peletier, Rossi, Savar\'e, and Tse to singular jump processes on abstract metric spaces, moving beyond the translation-invariant kernels considered in $\mathbb{R}^d$ and $\mathbb{T}^d$ in previous contributions. To address the analytical challenges posed by singularities, we introduce reflecting solutions, a new solution concept inspired by reflected Dirichlet forms, which ensures the validity of a chain rule and restores uniqueness. We establish existence, stability, and compactness results for these solutions by approximating singular kernels with regularized ones, and we show their robustness under such approximations. The framework encompasses dissipative and balanced solutions, clarifies their relations, and highlights the role of density properties of Lipschitz functions in upgrading weak formulations to reflecting solutions. As an application, we demonstrate the versatility of our theory to nonlocal stochastic evolutions on configuration spaces.

math.AP↗

arXiv2025

Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow

This study leverages the basic insight that the gradient-flow equation associated with the relative Boltzmann entropy, in relation to a Gaussian reference measure within the Hellinger-Kantorovich (HK) geometry, preserves the class of Gaussian measures. This invariance serves as the foundation for constructing a reduced gradient structure on the parameter space characterizing Gaussian densities. We derive explicit ordinary differential equations that govern the evolution of mean, covariance, and mass under the HK-Boltzmann gradient flow. The reduced structure retains the additive form of the HK metric, facilitating a comprehensive analysis of the dynamics involved. We explore the geodesic convexity of the reduced system, revealing that global convexity is confined to the pure transport scenario, while a variant of sublevel semi-convexity is observed in the general case. Furthermore, we demonstrate exponential convergence to equilibrium through Polyak-Lojasiewicz-type inequalities, applicable both globally and on sublevel sets. By monitoring the evolution of covariance eigenvalues, we refine the decay rates associated with convergence. Additionally, we extend our analysis to non-Gaussian targets exhibiting strong log-lambda-concavity, corroborating our theoretical results with numerical experiments that encompass a Gaussian-target gradient flow and a Bayesian logistic regression application.

math.AP↗

arXiv2025

Consensus-based qubit configuration optimization for variational algorithms on neutral atom quantum systems

In this work, we report an algorithm that is able to tailor qubit interactions for individual variational quantum algorithm problems. Here, the algorithm leverages the unique ability of a neutral atom tweezer platform to realize arbitrary qubit position configurations. These configurations determine the degree of entanglement available to a variational quantum algorithm via the interatomic interactions. Good configurations will accelerate pulse optimization convergence and help mitigate barren plateaus. As gradient-based approaches are ineffective for position optimization due to the divergent $R^{-6}$ nature of neutral atom interactions, we opt to use a consensus-based algorithm to optimize the qubit positions. By sampling the configuration space instead of using gradient information, the consensus-based algorithm is able to successfully optimize the positions, yielding adapted variational quantum algorithm ansatzes that lead to both faster convergence and lower errors. In this work, we show that these optimized configurations generally result in large improvements in the system's ability to solve ground state minimization problems for both random Hamiltonians and small molecules.

quant-ph↗

arXiv2025

Derivation of nonlinear aggregation-diffusion equation from a kinetic BGK-type equation

This paper investigates the diffusion limit of a kinetic BGK-type equation, focusing on its relaxation to a nonlinear aggregation-diffusion equation, where the diffusion exhibits a porous-medium-type nonlinearity. Unlike previous studies by Dolbeault et al. [Arch. Ration. Mech. Anal., 186, (2007), 133-158] and Addala and Tayeb [J. Hyperbolic Differ. Equ., 16, (2019), 131-156], which required bounded initial data, our work considers initial data that need not be bounded. We develop new techniques for handling weak entropy solutions that satisfy the natural bounds associated with the kinetic entropy inequality. Our proof employs the relative entropy method and various compactness arguments to establish the convergence and properties of these solutions.

math.AP↗

arXiv2025

Fidelity-Enhanced Variational Quantum Optimal Control

Creating robust quantum operations is a major challenge in the current noisy intermediate-scale quantum computing era. Recently, the importance of noise-resilient control methods has become more pronounced in the field. Ordinarily, noisy quantum systems are described by the Lindblad equation. However, minimizing noise susceptibility using this equation has proven challenging because of its irreversibility. In this study, we propose a new method for creating robust pulses based on the stochastic Schr\"{o}dinger equation. This equation describes individual noise realizations under any colored noise process, contrary to the Lindblad equation, which describes mean system behavior under white noise. Using stochastic optimal control techniques, our method, Fidelity-Enhanced Variational Quantum Optimal Control (F-VQOC), is able to construct higher fidelity paths than its non-stochastic counterpart (VQOC). By accounting for both environmental noise sources as well as noise sources inherent to the control system, highly significant increases in fidelity are noted for both single and multiqubit state preparations.

quant-ph↗

arXiv2024

Polytope Division Method: A Scalable Sampling Method for Problems with High-dimensional Parameters

Configuration Optimization Problems (COPs), which involve minimizing a loss function over a set of discrete points $\boldsymbol{\gamma} \subset P$, are common in areas like Model Order Reduction, Active Learning, and Optimal Experimental Design. While exact solutions are often infeasible, heuristic methods such as the Greedy Sampling Method (GSM) provide practical alternatives, particularly for low-dimensional cases. GSM recursively updates $\boldsymbol{\gamma}$ by solving a continuous optimization problem, which is typically approximated by a search over a discrete sample set $S \subset P$. However, as the dimensionality grows, the sample size suffers from the curse of dimensionality. To address this, we introduce the Polytope Division Method (PDM), a scalable greedy-type approach that adaptively partitions the parameter space and targets regions of high loss. PDM achieves linear scaling with problem dimensionality and offers an efficient solution approach for high-dimensional COPs, overcoming the limitations of traditional methods.

math.NA↗

arXiv2024

Large population limit of interacting population dynamics via generalized gradient structures

This chapter focuses on the derivation of a doubly nonlocal Fisher-KPP model, which is a macroscopic nonlocal evolution equation describing population dynamics in the large population limit. The derivation starts from a microscopic individual-based model described as a stochastic process on the space of atomic measures with jump rates that satisfy detailed balance w.r.t. to a reference measure. We make use of the so-called `cosh' generalized gradient structure for the law of the process to pass to the large population limit using evolutionary Gamma-convergence. In addition to characterizing the large population limit as the solution of the nonlocal Fisher-KPP model, our variational approach further provides a generalized gradient flow structure for the limit equation as well as an entropic propagation of chaos result.

math.AP↗

arXiv2024

Existence of weak solutions and long-time asymptotics for hydrodynamic model of swarming

We consider a one-dimensional hydrodynamic model featuring nonlocal attraction-repulsion interactions and singular velocity alignment. We introduce a two-velocity reformulation and the corresponding energy-type inequality, in the spirit of the Bresch-Desjardins estimate. We identify a dependence between the communication weight and interaction kernel and between the pressure and viscosity term allowing for this inequality to be uniform in time. It is then used to study long-time asymptotics of solutions.

math.AP↗

arXiv2024

Qubit fidelity under stochastic Schr\"odinger equations driven by colored noise

Environmental noise on a controlled quantum system is generally modeled by a dissipative Lindblad equation. This equation describes the average state of the system via the density matrix $\rho$. One way of deriving this Lindblad equation is by introducing a stochastic operator evolving under white noise in the Schr\"odinger equation. However, white noise, where all noise frequencies contribute equally in the power spectral density, is not a realistic noise profile as lower frequencies generally dominate the spectrum. Furthermore, the Lindblad equation does not fully describe the system as a density matrix $\rho$ does not uniquely describe a probabilistic ensemble of pure states $\{\psi_j\}_j$. In this work, we introduce a method for solving for the full distribution of qubit fidelity driven by important stochastic Schr\"odinger equation cases, where qubits evolve under more realistic noise profiles, e.g. Ornstein-Uhlenbeck noise. This allows for predictions of the mean, variance, and higher-order moments of the fidelities of these qubits, which can be of value when deciding on the allowed noise levels for future quantum computing systems, e.g. deciding what quality of control systems to procure. Furthermore, these methods will prove to be integral in the optimal control of qubit states under (classical) control system noise.

quant-ph↗

arXiv2023

Optimal control formulation of transition path problems for Markov Jump Processes

We formulate the transition path problem for Markov jump processes as a stochastic optimal control problem on path space. Transitions between metastable sets are induced by an unbounded terminal cost at a stopping time together with controlled modification of jump rates. The running cost takes an entropic form arising naturally from the Girsanov transform for jump processes, allowing both finite- and infinite-horizon formulations to be expressed as optimal changes of measure relative to a reference process. We prove that the optimal path measure admits an explicit representation in terms of the committor function, which solves an elliptic boundary value problem. The singular control induced by the terminal cost is obtained via $\Gamma$-convergence, yielding a limiting controlled process whose transition rates correspond to a Doob-h transform. The optimally controlled process generates transition paths almost surely while preserving the bridges of the reference process.

math.OC↗

arXiv2023

Accelerating optimization over the space of probability measures

The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While much attention has been directed towards optimizing within Euclidean space, the need to optimize over spaces of probability measures in machine learning motivates exploration of accelerated gradient methods in this context too. To this end, we introduce a Hamiltonian-flow approach analogous to momentum-based approaches in Euclidean space. We demonstrate that, in the continuous-time setting, algorithms based on this approach can achieve convergence rates of arbitrarily high order. We complement our findings with numerical examples.

math.OC↗

arXiv2023

Variational convergence of the Scharfetter-Gummel scheme to the aggregation-diffusion equation and vanishing diffusion limit

In this paper, we explore the convergence of the semi-discrete Scharfetter-Gummel scheme for the aggregation-diffusion equation using a variational approach. Our investigation involves obtaining a novel gradient structure for the finite volume space discretization that works consistently for any non-negative diffusion constant. This allows us to study the discrete-to-continuum and zero-diffusion limits simultaneously. The zero-diffusion limit for the Scharfetter-Gummel scheme corresponds to the upwind finite volume scheme for the aggregation equation. In both cases, we establish a convergence result in terms of gradient structures, recovering the Otto gradient flow structure for the aggregation-diffusion equation based on the 2-Wasserstein distance.

math.NA↗

arXiv2022

Fourier-Cattaneo equation: stochastic origin, variational formulation, and asymptotic limits

We introduce a variational structure for the Fourier-Cattaneo (FC) system which is a second-order hyperbolic system. This variational structure is inspired by the large-deviation rate functional for the Kac process which is closely linked to the FC system. Using this variational formulation we introduce appropriate solution concepts for the FC equation and prove an a priori estimate which connects this variational structure to an appropriate Lyapunov function and Fisher information, the so-called FIR inequality. Finally, we use this formulation and estimate to study the diffusive and hyperbolic limits for the FC system.

math.AP↗

arXiv2022

Generalized gradient structures for measure-valued population dynamics and their large-population limit

We consider the forward Kolmogorov equation corresponding to measure-valued processes stemming from a class of interacting particle systems in population dynamics, including variations of the Bolker-Pacala-Dieckmann-Law model. Under the assumption of detailed balance, we provide a rigorous generalized gradient structure, incorporating the fluxes arising from the birth and death of the particles. Moreover, in the large population limit, we show convergence of the forward Kolmogorov equation to a Liouville equation, which is a transport equation associated with the mean-field limit of the underlying process. In addition, we show convergence of the corresponding gradient structures in the sense of Energy-Dissipation Principles, from which we establish a propagation of chaos result for the particle system and derive a generalized gradient-flow formulation for the mean-field limit.

math.AP↗