Tootfinder

Integrating Quantum Computing with Multiconfiguration Pair-Density Functional Theory for Biological Electron Transfer
Yibo Chen, Zirui Sheng, Weitang Li, Yong Zhang, Xun Xu, Jun-Han Huang, Yuxiang Li
https://arxiv.org/abs/2508.07359

Integrating Quantum Computing with Multiconfiguration Pair-Density Functional Theory for Biological Electron Transfer
Accurate calculation of strongly correlated electronic systems requires proper treatment of both static and dynamic correlations, which remains challenging for conventional methods. To address this, we present VQE-PDFT, a quantum-classical hybrid framework that integrates variational quantum eigensolver with multiconfiguration pair-density functional theory (MC-PDFT). This framework strategically employs quantum circuits for multiconfigurational wavefunction representation while utilizing densi…

Dynamical Quantum Multigraphs
Kassahun H. Betre, Nathan Lewis
https://arxiv.org/abs/2509.08296 https://arxiv.org/pdf/2509.08296

Dynamical Quantum Multigraphs
Motivated by applications in background-independent quantum gravity, we discuss the quantization of labeled and unlabeled finite multigraphs with a maximum edge count. We provide a unified way to represent quantum multigraphs with labeled or unlabeled vertices, which enables the study of quantum multigraphs as dynamical microscopic degrees of freedom and not just as representations of relations among quantum states of particles. The quantum multigraphs represent a quantum mechanical treatment o…

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning
Bingning Huang, Tu Nguyen, Matthieu Zimmer
https://arxiv.org/abs/2509.09284 https://

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning
Recent advances in reasoning with large language models (LLMs) have shown the effectiveness of Monte Carlo Tree Search (MCTS) for generating high-quality intermediate trajectories, particularly in math and symbolic domains. Inspired by this, we explore how MCTS-derived trajectories, traditionally used for training value or reward models, can be repurposed to improve policy optimization in preference-based reinforcement learning (RL). Specifically, we focus on Group Relative Policy Optimization …

Core Devices CEO Eric Migicovsky unveils the Pebble Time 2 in four colors with a 1.5" e-paper display, multicolor RGB backlighting, and 30-day battery life (Victoria Song/The Verge)
https://www.theverge.com/news/758592/pebble-time-2-final-design-we…

Here’s a look at the final Pebble Time 2 design
Pebble is Pebble again, and the new Time 2 sports a sleeker profile and RGB backlighting.

Channel Estimation and Analog Precoding for Pixel-based Fluid-Antenna-Assisted Multiuser MIMO-OFDM Systems
Huayan Guo, Jichen Zhang, Junhui Rao, Ross Murch, Vincent K. N. Lau
https://arxiv.org/abs/2509.09373

Channel Estimation and Analog Precoding for Pixel-based Fluid-Antenna-Assisted Multiuser MIMO-OFDM Systems
Pixel-based fluid antennas provide enhanced multiplexing gains and quicker radiation pattern switching than traditional designs. However, this innovation introduces challenges for channel estimation and analog precoding due to the state-non-separable channel response problem. This paper explores a multiuser MIMO-OFDM system utilizing pixel-based fluid antennas, informed by measurements from a real-world prototype. We present a sparse channel recovery framework for uplink channel sounding, emplo…

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
https://arxiv.org/abs/2506.10636

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
The high dimensionality of kinetic equations with stochastic parameters poses major computational challenges for uncertainty quantification (UQ). Traditional Monte Carlo (MC) sampling methods, while widely used, suffer from slow convergence and high variance, which become increasingly severe as the dimensionality of the parameter space grows. To accelerate MC sampling, we adopt a multiscale control variates strategy that leverages low-fidelity solutions from simplified kinetic models to reduce …

MultiCoSim: A Python-based Multi-Fidelity Co-Simulation Framework
Quinn Thibeault, Giulia Pedrielli
https://arxiv.org/abs/2506.10869 https://

MultiCoSim: A Python-based Multi-Fidelity Co-Simulation Framework
Simulation is a foundational tool for the analysis and testing of cyber-physical systems (CPS), underpinning activities such as algorithm development, runtime monitoring, and system verification. As CPS grow in complexity and scale, particularly in safety-critical and learning-enabled settings, accurate analysis and synthesis increasingly rely on the rapid use of simulation experiments. Because CPS inherently integrate hardware, software, and physical processes, simulation platforms must suppor…

Optimal convergence rates in multiscale elliptic homogenization
Weisheng Niu, Yao Xu, Jinping Zhuge
https://arxiv.org/abs/2509.09410 https://arxiv.org/pdf/…

Optimal convergence rates in multiscale elliptic homogenization
This paper is devoted to the quantitative homogenization of multiscale elliptic operator $-\nabla\cdot A_\varepsilon \nabla$, where $A_\varepsilon(x) = A(x/\varepsilon_1, x/\varepsilon_2,\cdots, x/\varepsilon_n)$, $\varepsilon = (\varepsilon_1, \varepsilon_2,\cdots, \varepsilon_n) \in (0,1]^n$ and $\varepsilon_i > \varepsilon_{i+1}$. We assume that $A(y_1,y_2,\cdots, y_n)$ is 1-periodic in each $y_i \in \mathbb{R}^d$ and real analytic. Classically, the method of reiterated homogenization has be…

Multigranular Evaluation for Brain Visual Decoding
Weihao Xia, Cengiz Oztireli
https://arxiv.org/abs/2507.07993 https://arxiv.org/pdf…

Multigranular Evaluation for Brain Visual Decoding
Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground truth images. For the structural level, we introduce a hierarchic…

Topological A-Model for $AdS_5\times S^5$ Superstring and the Maldacena Conjecture
Nathan Berkovits (ICTP-SAIFR/IFT-UNESP, Sao Paulo)
https://arxiv.org/abs/2506.10907

Topological A-Model for $AdS_5\times S^5$ Superstring and the Maldacena Conjecture
A topological A-model constructed from $CP^{3|4}$ supertwistor variables is proposed for the $AdS_5\times S^5$ superstring. At zero $AdS$ radius, free N=4 d=4 super-Yang-Mills amplitudes are reproduced by topological amplitudes of the corresponding gauged linear sigma model where the closed superstring vertex operator for a trace of $k$ super-Yang-Mills fields is described by a boundary state with U(1) worldsheet instanton number $k$. After turning on a Fayet-Iliopoulis term in the sigma m…