Tootfinder

Quantum Gambling: Best-Arm Strategies for Generator Selection in Adaptive Variational Algorithms
Rick Huang, Artur F. Izmaylov
https://arxiv.org/abs/2509.14917 https://

Quantum Gambling: Best-Arm Strategies for Generator Selection in Adaptive Variational Algorithms
Adaptive variational algorithms suffer from prohibitively high measurement costs during the generator selection step, since energy gradients must be estimated for a large operator pool. This scaling bottleneck limits their applicability to larger molecular systems on near-term quantum devices. We address this challenge by reformulating generator selection as a Best Arm Identification (BAI) problem, where the goal is to identify the generator with the largest energy gradient using as few measure…

Relativistic second gradient theory of continuous media
Mina Chapon (LMPS), Lionel Darondeau (IMJ-PRG), Rodrigue Desmorat (LMT, LMPS), Cl\'ement Ecker (LMPS), Boris Kolev (LMPS)
https://arxiv.org/abs/2507.13030

Relativistic second gradient theory of continuous media
Variational Relativity is a framework developed by Souriau in the sixties to better formulate General Relativity and its classical limit\,: Classical Continuum Mechanics. It has been used, for instance, to formulate Hyperelasticity in General Relativity. In that case, two primary variables are involved, the universe (Lorentzian) metric $g$ and the matter field $Ψ$. A Lagrangian density depending on the 1-jet of these variables is then introduced which must satisfy the principle of General Cova…

Forward Euler for Wasserstein Gradient Flows: Breakdown and Regularization
Yewei Xu, Qin Li
https://arxiv.org/abs/2509.13260 https://arxiv.org/pdf/2509.132…

Forward Euler for Wasserstein Gradient Flows: Breakdown and Regularization
Wasserstein gradient flows have become a central tool for optimization problems over probability measures. A natural numerical approach is forward-Euler time discretization. We show, however, that even in the simple case where the energy functional is the Kullback-Leibler (KL) divergence against a smooth target density, forward-Euler can fail dramatically: the scheme does not converge to the gradient flow, despite the fact that the first variation $\nabla\frac{δF}{δρ}$ remains formally well …

Fast Distributed Nash Equilibrium Seeking in Monotone Games
Tatiana Tatarenko, Angelia Nedich
https://arxiv.org/abs/2507.11703 https://

Fast Distributed Nash Equilibrium Seeking in Monotone Games
This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with merely monotone and restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [5] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no access to the full information but are able to communicate with neighbors over some communication graph. The convergenc…

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Yifeng Peng, Xinyi Li, Samuel Yen-Chi Chen, Kaining Zhang, Zhiding Liang, Ying Wang, Yuxuan Du
https://arxiv.org/abs/2509.15193

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck: (i) the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and (ii) deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads.…

The Dispersion Leverage Coronagraph (DLC): A nulling coronagraph for use on primary objective grating telescopes
Leaf Swordy, Heidi Jo Newberg, Becket Hill, Richard K. Barry, Marina Cousins, Kerrigan Nish, Frank Ravizza, Sarah Rickborn
https://arxiv.org/abs/2507.11745

The Dispersion Leverage Coronagraph (DLC): A nulling coronagraph for use on primary objective grating telescopes
We present the Dispersion Leverage Coronagraph (DLC), a novel variation of the Achromatic Interfero Coronagraph (AIC) that is designed for optical systems featuring large, dispersive primary objective gratings. DLC was originally designed for the Diffractive Interfero Coronagraph Exoplanet Resolver (DICER), a notional 20m class infrared space telescope utilizing the enhanced one-dimensional angular resolution of large diffraction gratings in order to discover and characterize near-Earth exoplan…

On the geometry of flat minima
C\'edric Josz
https://arxiv.org/abs/2509.11386 https://arxiv.org/pdf/2509.11386

On the geometry of flat minima
What does it mean to be flat? We propose to define it by measuring the maximal variation around a point, or from a dual perspective, the distance to neighboring level sets. After developing some calculus rules, we show how flat minima, conservation laws, and symmetries are intertwined. Gradient flows of conserved quantities are of particular interest, due to their flattening properties.

Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
Frederik M{\o}ller, Gabriel Fern\'andez-Fern\'andez, Thomas Schweigler, Paulin de Schoulepnikoff, J\"org Schmiedmayer, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2509.13821

Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hindered by measurement noise, limited observables, and incomplete knowledge of the underlying microscopic model. Here, we develop a machine learning approach based on a variational autoencoder (VAE) to analyze interference measurements of tunnel-coupled one-dimensional Bose gases, which realize the sine-Gordon quantum fi…

Statistical analysis of barren plateaus in variational quantum algorithms
Le Bin Ho, Jesus Urbaneja, Sahel Ashhab
https://arxiv.org/abs/2508.08915 https://…

Statistical analysis of barren plateaus in variational quantum algorithms
We investigate the barren plateau (BP) phenomenon in variational quantum algorithms using a statistical approach. Using Gaussian function models, we identify three distinct types of BPs. The first type, which we called localized-dip BPs, occurs in landscapes that are mostly flat but contain a dip point where the gradient is large in a small region around the minimum. The second type, called localized-gorge BPs, which are somewhat similar to the localized-dip BPs but contain a gorge line. The th…

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar, Akash Kundu
https://arxiv.org/abs/2507.12189

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and …