Tootfinder

Computable measures of non-Markovianity for Gaussian free fermion systems
Giuliano Chiriac\`o
https://arxiv.org/abs/2509.25953 https://arxiv.org/pdf/2509.2…

Computable measures of non-Markovianity for Gaussian free fermion systems
We investigate measures of non-Markovianity in open quantum systems governed by Gaussian free fermionic dynamics. Standard indicators of non-Markovian behavior, such as the BLP and LFS measures, are revisited in this context. We show that for Gaussian states, trace-based distances -- specifically the Hilbert-Schmidt norm -- and second-order Rényi mutual information can be efficiently expressed in terms of two-point correlation functions, enabling practical computation even in systems where the…

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Dennis Frauen, Stefan Feuerriegel
https://arxiv.org/abs/2509.26429

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential outcomes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individ…

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2510.01094

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors suc…

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
https://arxiv.org/abs/2509.25170

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
The performance of flow matching and diffusion models can be greatly improved at inference time using reward alignment algorithms, yet efficiency remains a major limitation. While several algorithms were proposed, we demonstrate that a common bottleneck is the sampling method these algorithms rely on: many algorithms require to sample Markov transitions via SDE sampling, which is significantly less efficient and often less performant than ODE sampling. To remove this bottleneck, we introduce GL…

Tensor Network Markov Chain Monte Carlo: Efficient Sampling of Three-Dimensional Spin Glasses and Beyond
Tao Chen, Jing Liu, Youjin Deng, Pan Zhang
https://arxiv.org/abs/2509.23945

Tensor Network Markov Chain Monte Carlo: Efficient Sampling of Three-Dimensional Spin Glasses and Beyond
Sampling the three-dimensional (3D) spin glass -- i.e., generating equilibrium configurations of a 3D lattice with quenched random couplings -- is widely regarded as one of the central and long-standing open problems in statistical physics. The rugged energy landscape, pronounced critical slowing down, and intrinsic ergodicity breaking render standard Monte Carlo methods severely inefficient, particularly for large systems at low temperatures. In this work, we introduce the Tensor Network Marko…

Scale-Wise VAR is Secretly Discrete Diffusion
Amandeep Kumar, Nithin Gopalakrishnan Nair, Vishal M. Patel
https://arxiv.org/abs/2509.22636 https://arxiv.or…

Scale-Wise VAR is Secretly Discrete Diffusion
Autoregressive (AR) transformers have emerged as a powerful paradigm for visual generation, largely due to their scalability, computational efficiency and unified architecture with language and vision. Among them, next scale prediction Visual Autoregressive Generation (VAR) has recently demonstrated remarkable performance, even surpassing diffusion-based models. In this work, we revisit VAR and uncover a theoretical insight: when equipped with a Markovian attention mask, VAR is mathematically e…

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Fatemeh Roshanzadeh, Hamid Barati, Ali Barati
https://arxiv.org/abs/2508.20272

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Named Data Networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management are critical to NDN performance, especially under dynamic and high-traffic conditions. This paper introduces DRR-MDPF, a novel hybrid strategy that integrates the Markov Decision Process Forwarding (MDPF) model with the Deficit Round Robin (DRR) algorithm. MDPF enables routers to intelligently…

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks
Chen Shang, Jiadong Yu, Dinh Thai Hoang
https://arxiv.org/abs/2508.19566 https://…

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks
This work proposes an energy-efficient, learning-based beamforming scheme for integrated sensing and communication (ISAC)-enabled V2X networks. Specifically, we first model the dynamic and uncertain nature of V2X environments as a Markov Decision Process. This formulation allows the roadside unit to generate beamforming decisions based solely on current sensing information, thereby eliminating the need for frequent pilot transmissions and extensive channel state information acquisition. We then…

Short-Term Guidance Algorithm on a Drone Road System
Zhouyu Qu, Andreas Willig, Xiaobing Wu
https://arxiv.org/abs/2509.23794 https://arxiv.org/pdf/2509.237…

Short-Term Guidance Algorithm on a Drone Road System
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced expanding use in urban environments in recent years. However, the growing density of drones raises significant challenges, such as avoiding collisions and managing air traffic efficiently, especially in congested areas. To address these issues, a structured road system and an effective guidance algorithm are essential. In this paper, we introduce a markup language allowing to describe drone road systems (DRS), in which …

Noise-Resilient Quantum Reinforcement Learning
Jing-Ci Yue, Jun-Hong An
https://arxiv.org/abs/2508.20601 https://arxiv.org/pdf/2508.20601

Noise-Resilient Quantum Reinforcement Learning
As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver. By investigating the non-Markovian decoherence effect on …