Tootfinder

This https://arxiv.org/abs/2504.15265 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Realization of maximally-entangling two-qutrit gates using the Cross-Resonance scheme
In this letter, we present a comprehensive theoretical framework for realizing maximally entangling two-qutrit gates on fixed-frequency transmons that generalize the qubit-centric cross-resonance (CR) interaction beyond the 0-1 subspace, which we call as generalized cross-resonance (GCR) scheme. We use the GCR scheme to design parametric two-qutrit gates, namely, $U_{CR}^{01}$ and $U_{CR}^{12}$, that act on the $0{-}1$ and $1{-}2$ energy transitions of transmons, respectively. Our gates improve…

On the construction of a gradient method of quadratic optimization, optimal from the point of view of minimizing the distance to the exact solution
N. V. Pletnev
https://arxiv.org/abs/2506.04866

On the construction of a gradient method of quadratic optimization, optimal from the point of view of minimizing the distance to the exact solution
Problems of quadratic optimization in Hilbert space often arise when solving ill-posed problems for differential equations. In this case, the target value of the functional is known. In addition, the structure of the functional allows calculating the gradient by solving well-posed problems, which allows applying first-order methods. This article is devoted to the construction of the $m$-moment minimum error method -- an effective method that minimizes the distance to the exact solution. The con…

Dass die Kantone und der Bund danach lechzen, unsere Daten zu ausländischen Cloudprovidern zu schicken, das ist ja schon länger ein Thema, nicht nur bei DNIP.
https://dnip.ch/2025/05/21/der-microso

Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games
Alejandro Sanchez Roncero, Olov Andersson, Petter Ogren
https://arxiv.org/abs/2506.02849 …

Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games
The increasing proliferation of small UAVs in civilian and military airspace has raised critical safety and security concerns, especially when unauthorized or malicious drones enter restricted zones. In this work, we present a reinforcement learning (RL) framework for agile 1v1 quadrotor pursuit-evasion. We train neural network policies to command body rates and collective thrust, enabling high-speed pursuit and evasive maneuvers that fully exploit the quadrotor's nonlinear dynamics. To mitigat…

This https://arxiv.org/abs/2502.18284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Nested Expectations with Kernel Quadrature
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to be consistent but require a large number of samples at both inner and outer levels to converge. Instead, we propose a novel estimator consisting of nested kernel quadrature estimators and we prove that it has a faster convergence rate than all baseline methods when the integrands have sufficient smoothness. We then dem…

This https://arxiv.org/abs/2505.15724 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Gravitational Bremsstrahlung in Black-Hole Scattering at $\mathcal{O}(G^3)$: Quadratic-in-Spin Effects
We are employing a supersymmetric variant of the worldline quantum field theory (WQFT) formalism to compute the far-field momentum-space gravitational waveform emitted during the scattering of two spinning black holes at next-to-leading order (NLO) in the post-Minkowskian expansion. Our results are accurate up to quadratic-in-spin contributions, which means we report for the very first time the waveform observable at the order $\mathcal{O}(G^3\mathcal{S}^2)$. Our computation is based on mapping…

This https://arxiv.org/abs/2506.02635 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended pairwise conditional gradients and fully-corrective Frank-Wolfe, and propose a highly efficient corrective algorithm in the case of convex quadratic objectives based on linear optimization or linear system solving, akin to Wolfe's minimum-norm point. Beyond optimization problems that are directly quadratic, we revisit two algorithms, split conditional gradient for the intersection of two…

A Hybrid Perspective on Suboptimal Mixed-Integer Quadratic Programming
Luke Fina, Christopher Petersen
https://arxiv.org/abs/2507.02772 https://

A Hybrid Perspective on Suboptimal Mixed-Integer Quadratic Programming
This work solves suboptimal mixed-integer quadratic programs recursively for feedback control of dynamical systems. The proposed framework leverages parametric mixed-integer quadratic programming (MIQP) and hybrid systems theory to model a recursive MIQP feedback controller and a feedback controlled dynamical system. The proposed hybrid framework theoretically encodes the suboptimal part via solver parameters as bounded perturbations from the optimal solution set. The stability of the proposed …

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
Jannis Halbey, Seta Rakotomandimby, Mathieu Besan\c{c}on, Sebastian Pokutta
https://arxiv.org/abs/2506.02635

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended pairwise conditional gradients and fully-corrective Frank-Wolfe, and propose a highly efficient corrective algorithm in the case of convex quadratic objectives based on linear optimization or linear system solving, akin to Wolfe's minimum-norm point. Beyond optimization problems that are directly quadratic, we revisit two algorithms, split conditional gradient for the intersection of two…

This https://arxiv.org/abs/2503.13965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

First-Order Projected Algorithms With the Same Linear Convergence Rate Bounds as Their Unconstrained Counterparts
In this paper, we propose a systematic approach for constructing first-order projected algorithms for optimization problems with general set constraints, building upon their unconstrained counterparts. We show that these projected algorithms retain the same linear convergence rate bounds, when the latter are obtained for the unconstrained algorithms via quadratic Lyapunov functions arising from integral quadratic constraint (IQC) characterizations. The projected algorithms are constructed by ap…