Quantum-Inspired Neural Network Model of Optical Illusions

Algorithms, Journal Year: 2024, Volume and Issue: 17(1), P. 30 - 30

Published: Jan. 10, 2024

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology video games. However, accurate computational models perception ambiguous figures elusive. In this paper, we design train deep neural network model to simulate human the Necker cube, an drawing with several alternating possible interpretations. Defining weights connection using quantum generator truly random numbers, agreement emerging concepts artificial intelligence cognition, reveal that actual perceptual state cube is qubit-like superposition two fundamental states predicted by classical theories. Our results finds applications games virtual reality systems employed for training astronauts operators unmanned aerial vehicles. They are also useful researchers working fields machine learning vision, quantum–mechanical mind decision making.

Language: Английский

---

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

---

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

---

Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

Big Data and Cognitive Computing, Journal Year: 2025, Volume and Issue: 9(1), P. 12 - 12

Published: Jan. 14, 2025

Contemporary machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs deferring to human operators interpret the results make final decisions. In this paper, we employ recently proposed quantum tunnelling neural networks (QT-NNs) inspired brain processes alongside cognition theory classify image datasets while emulating perception judgment. Our findings suggest that QT-NN model provides compelling evidence of its potential replicate human-like decision-making. We also reveal be trained up 50 times faster than classical counterpart.

Language: Английский

---

A high-performance deep reservoir computer experimentally demonstrated with ion-gating reservoirs

Communications Engineering, Journal Year: 2024, Volume and Issue: 3(1)

Published: June 19, 2024

Abstract While physical reservoir computing is a promising way to achieve low power consumption neuromorphic computing, its computational performance still insufficient at practical level. One approach improving deep in which the component reservoirs are multi-layered. However, all of deep-reservoir schemes reported so far have been effective only for simulation and limited reservoirs, there no reports nanodevice implementations. Here, as an ionics-based implementation we report demonstration with maximum four layers using ion gating reservoir, small high-performance reservoir. previously scheme did not improve our deep-ion achieved normalized mean squared error 9.08 × 10 −3 on second-order nonlinear autoregressive moving average task, best any this task. More importantly, device outperformed full computing. The dramatic improvement architecture paves high-performance, large-scale, neural network devices.

Language: Английский

---

Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Dynamics, Journal Year: 2024, Volume and Issue: 4(1), P. 119 - 134

Published: Feb. 8, 2024

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network random connections. The versatility RC has motivated further research on both hardware counterparts traditional algorithms and more-efficient RC-like schemes. Inspired by processes in a living biological brain solitary waves excited surface flowing liquid film, this paper, we experimentally validated physical system that substitutes effect randomness underpins operation algorithm for transformation input data. Carrying out all operations microcontroller with minimal computational power, demonstrate so-designed serves as technically simple counterpart to ‘next-generation’ improvement algorithm.

Language: Английский

---

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

---

Optical Extreme Learning Machines with Atomic Vapors

Atoms, Journal Year: 2024, Volume and Issue: 12(2), P. 10 - 10

Published: Feb. 6, 2024

Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the layer, approach has potential speed up process and capacity turn any physical system into a platform. Yet, requiring strong dynamics, optical solutions operating fast processing rates low power can be hard achieve with conventional materials. In this context, manuscript explores possibility of using atomic gases in near-resonant conditions implement an extreme machine leveraging their enhanced properties. Our results suggest that these systems have not work as but also computations few-photon level, paving opportunities for energy-efficient solutions.

Language: Английский

---

Magnetism-Inspired Quantum-Mechanical Model of Gender Fluidity

Published: Feb. 8, 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

---

Large sampling intervals for learning and predicting chaotic systems with reservoir computing

Journal of Physics A Mathematical and Theoretical, Journal Year: 2024, Volume and Issue: 57(29), P. 295702 - 295702

Published: July 9, 2024

Abstract Reservoir computing (RC) is an efficient artificial neural network for model-free prediction and analysis of dynamical systems time series. As a data-based method, the capacity RC strongly affected by sampling interval training data. In this paper, taking Lorenz system as example, we explore influence on performance in predicting chaotic sequences. When increases, first enhanced then weakened, presenting bell-shaped curve. By slightly revising calculation method output matrix, with small can be improved. Furthermore, learn reproduce state large interval, which almost five times larger than that classic fourth-order Runge–Kutta method. Our results show applications where intervals are constrained laid foundation building fast algorithm iteration steps.

Language: Английский

---