Tootfinder

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

Enhancing Decoding Performance using Efficient Error Learning
Pavithran Iyer, Aditya Jain, Stephen D. Bartlett, Joseph Emerson
https://arxiv.org/abs/2507.08536

Enhancing Decoding Performance using Efficient Error Learning
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), …

"Can This Alternative Browser Make Search Good Again?" @ Katina Magazine: https://katinamagazine.org/content/article/resource-reviews/2025/can-this-alternative-browser-make-search-good-again
&…

Towards Understanding Bias in Synthetic Data for Evaluation
Hossein A. Rahmani, Varsha Ramineni, Nick Craswell, Bhaskar Mitra, Emine Yilmaz
https://arxiv.org/abs/2506.10301

Towards Understanding Bias in Synthetic Data for Evaluation
Test collections are crucial for evaluating Information Retrieval (IR) systems. Creating a diverse set of user queries for these collections can be challenging, and obtaining relevance judgments, which indicate how well retrieved documents match a query, is often costly and resource-intensive. Recently, generating synthetic datasets using Large Language Models (LLMs) has gained attention in various applications. While previous work has used LLMs to generate synthetic queries or documents to imp…

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Yi-Chun Liao, Chieh-Lin Tsai, Yuan-Hao Chang, Cam\'elia Slimani, Jalil Boukhobza, Tei-Wei Kuo
https://arxiv.org/abs/2506.05994

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Although deep learning has demonstrated remarkable capabilities in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant information and learning from structured datasets. While several efforts have been made to accelerate tree-based models, the inherent characteristics of the models pose significant challenges for conventional accelerators. Recent research leveraging content-addressable memory (CAM) offers a promising solution for acceleratin…

Measuring the co-evolution of online engagement with (mis)information and its visibility at scale
Yueting Han, Paolo Turrini, Marya Bazzi, Giulia Andrighetto, Eugenia Polizzi, Manlio De Domenico
https://arxiv.org/abs/2506.06106

Measuring the co-evolution of online engagement with (mis)information and its visibility at scale
Online attention is an increasingly valuable resource in the digital age, with extraordinary events such as the COVID-19 pandemic fuelling fierce competition around it. As misinformation pervades online platforms, users seek credible sources, while news outlets compete to attract and retain their attention. Here we measure the co-evolution of online "engagement" with (mis)information and its "visibility", where engagement corresponds to user interactions on social media, and visibility to fluct…

Intuitive dissection of the Gaussian information bottleneck method with an application to optimal prediction
Vahe Galstyan, Age Tjalma, Pieter Rein ten Wolde
https://arxiv.org/abs/2507.05183

Intuitive dissection of the Gaussian information bottleneck method with an application to optimal prediction
Efficient signal representation is essential for the functioning of living and artificial systems operating under resource constraints. A widely recognized framework for deriving such representations is the information bottleneck method, which yields the optimal strategy for encoding a random variable, such as the signal, in a way that preserves maximal information about a functionally relevant variable, subject to an explicit constraint on the amount of information encoded. While in its genera…

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04952

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information importance to risk aversion, we model agent behavior using Cumulative Prospect Theory (CPT), which incorporates risk-sensitive utility functions and nonlinear transformations of distributions, reflecting subjective perceptions of gains and losses. The objective is to maximize the aggregate utility across multiple CPT-mode…

Homeostatic Adaptation of Optimal Population Codes under Metabolic Stress
Yi-Chun Hung, Gregory Schwartz, Emily A. Cooper, Emma Alexander
https://arxiv.org/abs/2507.07874

Homeostatic Adaptation of Optimal Population Codes under Metabolic Stress
Information processing in neural populations is inherently constrained by metabolic resource limits and noise properties, with dynamics that are not accurately described by existing mathematical models. Recent data, for example, shows that neurons in mouse visual cortex go into a "low power mode" in which they maintain firing rate homeostasis while expending less energy. This adaptation leads to increased neuronal noise and tuning curve flattening in response to metabolic stress. We have develo…

ESWC 2025 has just started with the presentation of the submission numbers to the individual tracks:
research track: 98 papers
resource track: 36 papers
in-use track: 22 papers
Looking forward to great presentations and discussions!
#eswc2025 #semweb

Quantifying imaginarity of quantum operations
Chuanfa Wu, Zhaoqi Wu
https://arxiv.org/abs/2506.09747 https://arxiv.org/pdf/2506.09747…

Quantifying imaginarity of quantum operations
Complex numbers are theoretically proved and experimentally confirmed as necessary in quantum mechanics and quantum information, and a resource theory of imaginarity of quantum states has been established. In this work, we establish a framework to quantify the imaginarity of quantum operations from the perspective of the ability to create or detect imaginarity, following the idea by Theurer {\it et al.} [Phys. Rev. Lett. \textbf{122}, 190405 (2019)] used in coherence theory. We present two type…

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, Enhong Chen
https://arxiv.org/abs/2507.04756

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Personalized text generation has become crucial for adapting language models to diverse and evolving users' personal context across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment, they struggle to achieve real-time adaptation under resource constraints inherent to personal devices. This limitation creates a dilemma: large cloud-based models lack access to localized user-specific information, while small …

Epidemic dynamics in physical-information-social multilayer networks
Mengshou Wang, Liangrong Peng, Baoguo Jia, Liu Hong
https://arxiv.org/abs/2506.00104 h…

Epidemic dynamics in physical-information-social multilayer networks
During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The basic reproduction number and epidemic th…

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
John TM Campbell, John Dooley
https://arxiv.org/abs/2506.07875

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
This paper discusses the relationships between the Fiedler vector, the Cheeger constant, and threshold behaviors in networks of quantum resource nodes represented as Quantum Directed Acyclic Graphs (QDAGs). We explore how these mathematical constructs can be applied to understand the dynamics of quantum information flow in QDAGs, especially in the context of routing problems with bottlenecks in graph signal processing, and how new eigenvalue-based rewiring techniques can optimize entanglement d…

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
Beining Wu, Jun Huang, Qiang Duan, Liang Dong, Zhipeng Cai
https://arxiv.org/abs/2507.00856

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learni…

This https://arxiv.org/abs/2204.07330 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems
This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against a potential attacker with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve c…

Securing AI Systems: A Guide to Known Attacks and Impacts
Naoto Kiribuchi, Kengo Zenitani, Takayuki Semitsu
https://arxiv.org/abs/2506.23296 https://

Securing AI Systems: A Guide to Known Attacks and Impacts
Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We…

It happened: the Trump regime just deleted NOAA's climate information site climate.gov, which was a great global resource.
https://fediscience.org/@ZLabe/114738627171856942

Zack Labe (@ZLabe@fediscience.org)
Attached: 1 image The new redirect when you search ➡️ https://climate.gov/ Yikes... ☹️

A distributed system perspective on Backscatter systems: A review
Tonghuan Xiao, Jiecheng Zhou
https://arxiv.org/abs/2506.04873 https://

A distributed system perspective on Backscatter systems: A review
This review investigates the pivotal role of distributed architectures and intelligent resource allocation in enabling robust and scalable wireless systems, with a particular emphasis on backscatter communication, indoor localization, battery-free networks, and Simultaneous Wireless Information and Power Transfer (SWIPT).

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Gabriel Aracena, Kyle Luster, Fabio Santos, Igor Steinmacher, Marco A. Gerosa
https://arxiv.org/abs/2506.00128

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious and lacks scalability. As an alternative, many open source software (OSS) projects employ automated processes for this task, yet this method often relies on large datasets for adequate training. Traditionally, machine learning techniques have been used for issue classification. More recently, large language models (LL…

Quantum Resource Correction
Mark Byrd, Daniel Dilley, Alvin Gonzales, Masaya Takahashi, Zain Saleem, Lian-Ao Wu
https://arxiv.org/abs/2506.19776 https://…

Quantum Resource Correction
Resource theories play a crucial role in characterizing states and properties essential for quantum information processing. A significant challenge is protecting resources from errors. We explore strategies for correcting quantum resources. We show that resource preserving operations in resource theory define a gauge freedom on code spaces, which allows for recovery strategies that can correct the resource while changing non-essential properties. This allows decoding to be simplified. The resul…

This https://arxiv.org/abs/2406.15119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation
Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and computational resources. Moreover, emotional speech data often contains private information, raising concerns about privacy leakage during the deployment of SER models. To address these challenges, we propose a data distillation framework to facilitate…

This https://arxiv.org/abs/2502.17826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Feedback-Free Resource Scheduling for Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands. However, its practical implementation is hindered by the current radio access network (RAN), which relies on the coupling of uplink and downlink transmissions and channel state information feedback with inherent issues such as overheads and delays. To overcome these limitations, we consider the fully-decoupled RAN (FD-RAN), in which uplink and downlink functi…

Geo-Disasters: geocoding climate-related events in the international disaster database EM-DAT
Khalil Teber, M\'elanie Weynants, Fabian Gans, Miguel D. Mahecha
https://arxiv.org/abs/2506.03797

Geo-Disasters: geocoding climate-related events in the international disaster database EM-DAT
Climate hazards can escalate into humanitarian disasters. Understanding their trajectories -- considering hazard intensity, human exposure, and societal vulnerability -- is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it lacks exact geospatial information, limiting its use for climate hazard impact research. Here, we provide geocoding of 9,217 climate-related disa…

Geometric Invariants of Quantum Metrology
Christopher Wilson, John Drew Wilson, Luke Coffman, Shah Saad Alam, Murray J. Holland
https://arxiv.org/abs/2507.06128

Geometric Invariants of Quantum Metrology
We establish a previously unexplored conservation law for the Quantum Fisher Information Matrix (QFIM) expressed as follows; when the QFIM is constructed from a set of observables closed under commutation, i.e., a Lie algebra, the spectrum of the QFIM is invariant under unitary dynamics generated by these same operators. Each Lie algebra therefore endows any quantum state with a fixed "budget" of metrological sensitivity -- an intrinsic resource that we show, like optical squeezing in interfero…

This https://arxiv.org/abs/2505.22299 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overlook the real query intents. Thus, they often retrieve numerous irrelevant documents. Particularly in the scenarios of complex queries such as \emph{negative-constraint queries}, their retrieval perfo…

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
Ruilin Xu, Zongxuan Xie, Pengfei Chen
https://arxiv.org/abs/2506.02007

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware components, including the GPU and network communication layer, as well as from key software stacks such as CUDA, Python, and PyTorch, all without requiring any code instrumentation or modifications. Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidime…

Metadata Enrichment of Long Text Documents using Large Language Models
Manika Lamba, You Peng, Sophie Nikolov, Glen Layne-Worthey, J. Stephen Downie
https://arxiv.org/abs/2506.20918

Metadata Enrichment of Long Text Documents using Large Language Models
In this project, we semantically enriched and enhanced the metadata of long text documents, theses and dissertations, retrieved from the HathiTrust Digital Library in English published from 1920 to 2020 through a combination of manual efforts and large language models. This dataset provides a valuable resource for advancing research in areas such as computational social science, digital humanities, and information science. Our paper shows that enriching metadata using LLMs is particularly benef…

Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems
Zakir Hussain Shaik, Sai Subramanyam Thoota, Emil Bj\"ornson, Erik G. Larsson
https://arxiv.org/abs/2506.00655

Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems
We propose a novel resource-efficient over-the-air(OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate…

Misinformation by Omission: The Need for More Environmental Transparency in AI
Sasha Luccioni, Boris Gamazaychikov, Theo Alves da Costa, Emma Strubell
https://arxiv.org/abs/2506.15572

Misinformation by Omission: The Need for More Environmental Transparency in AI
In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this trend, transparency around the costs and impacts of these models has decreased, meaning that the users of these technologies have little to no information about their resource demands and subsequent impacts on the environment. Despite this dearth of adequate data, escalating demand for figures quantifying AI's environme…

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Angelo Josey Caldeira, Sajan Maharjan, Srijoni Majumdar, Evangelos Pournaras
https://arxiv.org/abs/2506.02706

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Gaming environments are popular testbeds for studying human interactions and behaviors in complex artificial intelligence systems. Particularly, in multiplayer online battle arena (MOBA) games, individuals collaborate in virtual environments of high realism that involves real-time strategic decision-making and trade-offs on resource management, information collection and sharing, team synergy and collective dynamics. This paper explores whether collective intelligence, emerging from cooperative…

Efficient Approximate Temporal Triangle Counting in Streaming with Predictions
Giorgio Venturin, Ilie Sarpe, Fabio Vandin
https://arxiv.org/abs/2506.13173 …

Efficient Approximate Temporal Triangle Counting in Streaming with Predictions
Triangle counting is a fundamental and widely studied problem on static graphs, and recently on temporal graphs, where edges carry information on the timings of the associated events. Streaming processing and resource efficiency are crucial requirements for counting triangles in modern massive temporal graphs, with millions of nodes and up to billions of temporal edges. However, current exact and approximate algorithms are unable to handle large-scale temporal graphs. To fill such a gap, we int…

Quantum Resource Theories of Anyonic Entanglement
Wenhao Ye, Li You, Cheng-Qian Xu
https://arxiv.org/abs/2506.19735 https://arxiv.org…

Quantum Resource Theories of Anyonic Entanglement
As information carriers for fault-tolerant quantum computing, systems composed of anyons exhibit non-tensor product state spaces due to their distinctive fusion rules, leading to fundamentally different entanglement properties from conventional quantum systems. However, a quantitative characterization of entanglement for general anyonic states remains elusive. In this Letter, within the framework of resource theory, we propose three measures that quantify total entanglement, conventional entang…

This https://arxiv.org/abs/2501.18799 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresses the need for adaptable and resource-efficient spike encoding hardware by presenting an area-optimized hardware implementation of the Spiketrum algorithm, which encodes time-vary…

This https://arxiv.org/abs/2412.00193 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Spacetime Markov length: a diagnostic for fault tolerance via mixed-state phases
We establish a correspondence between the fault-tolerance of local stabilizer codes experiencing measurement and physical errors and the mixed-state phases of decohered resource states in one higher dimension. Drawing from recent developments in mixed-state phases of matter, this motivates a diagnostic of fault-tolerance, which we refer to as the spacetime Markov length. This is a length scale determined by the decay of the (classical) conditional mutual information of repeated syndrome measure…

LiSec-RTF: Reinforcing RPL Resilience Against Routing Table Falsification Attack in 6LoWPAN
Shefali Goel, Vinod Kumar Verma, Abhishek Verma
https://arxiv.org/abs/2506.17911

LiSec-RTF: Reinforcing RPL Resilience Against Routing Table Falsification Attack in 6LoWPAN
Routing Protocol for Low-Power and Lossy Networks (RPL) is an energy-efficient routing solution for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), recommended for resource-constrained devices. While RPL offers significant benefits, its security vulnerabilities pose challenges, particularly due to unauthenticated control messages used to establish and maintain routing information. These messages are susceptible to manipulation, enabling malicious nodes to inject false routing dat…

Making deep neural networks work for medical audio: representation, compression and domain adaptation
Charles C Onu
https://arxiv.org/abs/2506.13970 https:…

Making deep neural networks work for medical audio: representation, compression and domain adaptation
This thesis addresses the technical challenges of applying machine learning to understand and interpret medical audio signals. The sounds of our lungs, heart, and voice convey vital information about our health. Yet, in contemporary medicine, these sounds are primarily analyzed through auditory interpretation by experts using devices like stethoscopes. Automated analysis offers the potential to standardize the processing of medical sounds, enable screening in low-resource settings where physici…

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features
Qianchao Wang, Leena Heistrene, Yoash Levron, Yuxuan Ding, Yaping Du
https://arxiv.org/abs/2505.23097

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features
In practical resource-constrained environments, efficiently extracting the potential high-frequency fault-critical information is an inherent problem. To overcome this problem, this work suggests leveraging a bi-residual neural network named Bi-ResNet to extract the inner spatial-temporal high-frequency features using embedded spatial-temporal convolution blocks and intra-link layers. It can be considered as embedding a high-frequency extractor into networks without adding any parameters, helpi…

Fiber transmission of cluster states via multi-level time-bin encoding
Philip R\"ubeling, Robert Johanning, Jan Heine, Oleksandr V. Marchukov, Michael Kues
https://arxiv.org/abs/2507.01497

Fiber transmission of cluster states via multi-level time-bin encoding
The next generation of telecommunication networks will rely on the transmission of complex quantum states to enable secure and transformative information processing, utilizing entanglement and superposition. Cluster states - multipartite entangled states that retain entanglement under local measurements - are a vital resource for quantum networking applications such as blind photonic quantum computing, quantum state teleportation and all-photonic quantum repeaters. However, the transmission of …

Identification for Molecular Communication Based on Diffusion Channel with Poisson Reception Process
Yaning Zhao, Luca Miszewski, Christian Deppe, Massimiliano Pierobon
https://arxiv.org/abs/2506.14360

Identification for Molecular Communication Based on Diffusion Channel with Poisson Reception Process
Molecular communication (MC) enables information exchange at the nano- and microscale, with applications in areas like drug delivery and health monitoring. These event-driven scenarios often require alternatives to traditional transmission. Identification communication, introduced by Ahlswede and Dueck, offers such an approach, in which the receiver only determines whether a specific message was sent, suiting resource-limited and event-triggered systems. This paper combines MC with identificati…

Quantum Resource Complementarity in Finite-Dimensional Systems
Justin K. Edmondson
https://arxiv.org/abs/2506.11741 https://arxiv.org…

Quantum Resource Complementarity in Finite-Dimensional Systems
Quantum resources such as entanglement, information redundancy, and coherence enable revolutionary advantages but obey fundamental tradeoffs. We present a unified geometric constraint governing three core operational tasks: teleportation ($q_1$), cloning ($q_2$), and coherence-based metrology ($q_3$). For any tripartite quantum state $ρ_{ABC}$, we show the tight inequality $q_1^2 + q_2^2 + q_3^2 \leq 1$ confines all physically achievable resources to the positive octant of the unit ball. This …

Fidelity of entanglement and quantum entropies: unveiling their relationship in quantum states and channels
Komal Kumar, Bivas Mallick, Tapaswini Patro, Nirman Ganguly
https://arxiv.org/abs/2506.11506

Fidelity of entanglement and quantum entropies: unveiling their relationship in quantum states and channels
Entanglement serves as a fundamental resource for various quantum information processing tasks. Fidelity of entanglement (which measures the proximity to a maximally entangled state) and various quantum entropies are key indicators for certifying entanglement in a quantum state. Quantum states with high fidelity are particularly useful for numerous information-theoretic applications. Similarly, states possessing negative conditional entropy provide significant advantages in several quantum info…

Experimental violation of a Bell-like inequality for causal order
Yu Guo, Hao Tang, Xiao-Min Hu, Yun-Feng Huang, Chuan-Feng Liu, Guang-Can Guo, Giulio Chiribella, Bi-Heng Liu
https://arxiv.org/abs/2506.20516

Experimental violation of a Bell-like inequality for causal order
Quantum mechanics allows for coherent control over the order in which different processes take place on a target system, giving rise to a new feature known as indefinite causal order. Indefinite causal order provides a resource for quantum information processing, and can be in principle be detected by the violation of certain inequalities on the correlations between measurement outcomes observed in the laboratory, in a similar way as quantum nonlocality can be detected by the violation of Bell …

Breaking even with magic: demonstration of a high-fidelity logical non-Clifford gate
Shival Dasu, Simon Burton, Karl Mayer, David Amaro, Justin A. Gerber, Kevin Gilmore, Dan Gresh, Davide DelVento, Andrew C. Potter, David Hayes
https://arxiv.org/abs/2506.14688

Breaking even with magic: demonstration of a high-fidelity logical non-Clifford gate
Encoding quantum information to protect it from errors is essential for performing large-scale quantum computations. Performing a universal set of quantum gates on encoded states demands a potentially large resource overhead and minimizing this overhead is key for the practical development of large-scale fault-tolerant quantum computers. We propose and experimentally implement a magic-state preparation protocol to fault-tolerantly prepare a pair of logical magic states in a [[6,2,2]] quantum er…