Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social Networks

Information, Journal Year: 2024, Volume and Issue: 15(3), P. 170 - 170

Published: March 20, 2024

We propose a quantum-mechanical model that represents human system of beliefs as the quantised energy levels physical system. This novel perspective on opinion dynamics, recreating broad range experimental and real-world data exhibit an asymmetry radicalisation. In particular, demonstrates phenomena pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings polarisation via social media exposure. Advancing this model, we establish robust framework integrates elements from physics, psychology, behavioural science, decision-making theory, philosophy. also emphasise inherent advantages quantum approach over traditional models, suggesting number new directions for future research work models cognition decision-making.

Language: Английский

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Reservoir Computing Using Measurement-Controlled Quantum Dynamics

Electronics, Journal Year: 2024, Volume and Issue: 13(6), P. 1164 - 1164

Published: March 21, 2024

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce quantum RC probed atom in cavity. The experiences coherent driving at particular rate, leading measurement-controlled evolution. proposed can make fast reliable forecasts using small number artificial neurons compared with traditional algorithm. We theoretically validate operation reservoir, demonstrating its potential be used error-tolerant applications, where approximate approaches may feasible conditions limited computational energy resources.

Language: Английский

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The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics

Information, Journal Year: 2024, Volume and Issue: 15(7), P. 413 - 413

Published: July 18, 2024

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation in ferromagnetic nanostructures, we developed a model that closely reflects dynamics. Tested against spectrum psychological data, our adeptly captures complexities inherent individual choices. This blend physics and psychology paves way for fresh perspectives on understanding imprecision processes, extending reach current classical quantum models behaviour decision making.

Language: Английский

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Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Dynamics, Journal Year: 2024, Volume and Issue: 4(3), P. 643 - 670

Published: Aug. 12, 2024

Artificial intelligence (AI) systems of autonomous such as drones, robots and self-driving cars may consume up to 50% the total power available onboard, thereby limiting vehicle’s range functions considerably reducing distance vehicle can travel on a single charge. Next-generation onboard AI need an even higher since they collect process larger amounts data in real time. This problem cannot be solved using traditional computing devices become more power-consuming. In this review article, we discuss perspectives development neuromorphic computers that mimic operation biological brain nonlinear–dynamical properties natural physical environments surrounding vehicles. Previous research also demonstrated quantum processors (QNPs) conduct computations with efficiency standard computer while consuming less than 1% battery power. Since QNPs are semi-classical technology, their technical simplicity low cost compared make them ideally suited for applications systems. Providing perspective future progress unconventional reservoir surveying outcomes 200 interdisciplinary works, article will interest broad readership, including both students experts fields physics, engineering, technologies computing.

Language: Английский

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Quantum-tunneling deep neural network for optical illusion recognition

APL Machine Learning, Journal Year: 2024, Volume and Issue: 2(3)

Published: Aug. 22, 2024

The discovery of the quantum tunneling (QT) effect—the transmission particles through a high potential barrier—was one most impressive achievements mechanics made in 1920s. Responding to contemporary challenges, I introduce deep neural network (DNN) architecture that processes information using effect QT. demonstrate ability QT-DNN recognize optical illusions like human. Tasking simulate human perception Necker cube and Rubin’s vase, provide arguments favor superiority QT-based activation functions over optimized for modern applications machine vision, also showing that, at fundamental level, is closely related biology-inspired DNNs models based on principles processing.

Language: Английский

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Optical Illusions Recognition Intelligence

Published: July 20, 2024

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Magnetism-Inspired Quantum-Mechanical Model of Gender Fluidity

Published: Jan. 27, 2024

Quantum-mechanical models of human cognition, opinion formation and decision-making have changed the way we understand predict behaviour in many practical situations, including political elections, financial decisions international affairs. Yet, at present, such overlook certain essential social aspects self-identification. In this paper, introduce a magnetism-inspired quantum-mechanical model gender fluidity, concept that challenges norms across globe. Addressing number independent suggestions made by members general public concerning potential analogy between quantum superposition non-binary self-identification, explore new territories, demonstrating physic magnetism can help explain fluidity similar phenomena better than traditional cognition perception. We anticipate proposed be used to analyse experimental datasets aimed develop sexual orientation identity legal definitions as well create artificial intelligence systems sensibly identify both binary genders.

Language: Английский

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Quantum Machine Learning, Leveraging AI, and Semiconductor Technology

Advances in mechatronics and mechanical engineering (AMME) book series, Journal Year: 2024, Volume and Issue: unknown, P. 57 - 78

Published: Oct. 11, 2024

This chapter explores the intersection of quantum computing, artificial intelligence (AI), and semiconductor technology, focusing specifically on emerging field machine learning (QML). Quantum computing promises to revolutionize traditional algorithms by leveraging principles mechanics perform computations at exponentially faster speeds. will delve into fundamentals technologies relevant QML, highlighting challenges opportunities in scaling up integrated AI-quantum systems. It discuss convergence AI exploring development tailored for information processing hardware implementations acceleration. Case studies industry applications illustrate potential QML cybersecurity, drug discovery, material science, other domains, while addressing ethical societal implications future trends challenges.

Language: Английский

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