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cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Quantum Blockchain Miners Provide Massive Energy Savings
Joseph Kearney, Carlos A Perez-Delgado · 2023-06-06 · via cs.SI updates on arXiv.org

Blockchain-based cryptocurrencies have become an extremely important, highly-used, technology. A major criticism of cryptocurrencies, however, is their energy consumption. In May 2022 Bitcoin alone was reported to be consuming 150 terawatt-hours of electricity annually; more than many entire countries. Hence, any meaningful efficiency increase in this process would have a tremendous positive impact. Meanwhile, practical applications of quantum information technologies, and in particular of near-term quantum computers (NISQ) continue to be an important research question. Here, we study the efficiency benefits of moving cryptocurrency mining from current ASIC-based miners to quantum, and in particular NISQ, miners. While the time-efficiency benefits of quantum technologies is extremely well-studied, here we focus on energy savings. We show that the transition to quantum-based mining could incur an energy saving, by relatively conservative estimates, of about roughly 126.7TWH, or put differently the total energy consumption of Sweden in 2020.