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2025, RG
https://doi.org/10.13140/RG.2.2.15681.67688…
7 pages
1 file
New application of our AI Abstract Engineering techniques in quantum theory of entanglement is considered. We design AI experiment with Conway’s quantum particle equipped with mathematical free will ( predicted by Conway’s Strong Free Will Theorem (2009)) based on GRPO - model used in AI.
Artificial Intelligence, 2010
The main purpose of this paper is to examine some (potential) applications of quantum computation in AI and to review the interplay between quantum theory and AI. For the readers who are not familiar with quantum computation, a brief introduction to it is provided, and a famous but simple quantum algorithm is introduced so that they can appreciate the power of quantum computation. Also, a (quite personal) survey of quantum computation is presented in order to give the readers a (unbalanced) panorama of the field. The author hopes that this paper will be a useful map for AI researchers who are going to explore further and deeper connections between AI and quantum computation as well as quantum theory although some parts of the map are very rough and other parts are empty, and waiting for the readers to fill in.
ESANN 2023 proceesdings
Artificial Intelligence (AI), a discipline with decades of history, is living its golden era due to striking developments that solve problems that were unthinkable just a few years ago, like generative models of text, images and video. The broad range of AI applications has also arrived to Physics, providing solutions to bottleneck situations, e.g., numerical methods that could not solve certain problems or took an extremely long time, optimization of quantum experimentation, or qubit control. Besides, Quantum Computing has become extremely popular for speeding up AI calculations, especially in the case of data-driven AI, i.e., Machine Learning (ML). The term Quantum ML is already known and deals with learning in quantum computers or quantum annealers, quantum versions of classical ML models and different learning approaches for quantum measurement and control. Quantum AI (QAI) tries to take a step forward in order to come up with disruptive concepts, such as, human-quantum-computer interfaces, sentiment analysis in quantum computers or explainability of quantum computing calculations, to name a few. This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.
ADI Journal on Recent Innovation (AJRI)
Quantum computing is one of the emerging technologies. Different communities and research organizations are working to bring quantum computing applications into reality. Artificial Intelligence is another emerging area and getting stable with time. This paper, the main objective is to find out the impact of quantum computing research growth for AI applications. Thus, the method used in this study uses computational methods. so that this research can be concluded regarding the growing impact of quantum computing research for a given AI application. This paper also presents the impact and possibilities of quantum computing in the field of artificial intelligence.
Journal , 2023
This paper takes the audience through an exploration of artificial intelligence and physics, and how they can integrate. Further to this, in this paper, I am also using Quantum Field theory to further prove my theory. When combining the knowledge of the applications and principles, you must also consider the mathematical component that integrates quantum field theory. To uncover the theory and understand it, you need to introduce yourself to the functions of AI and how it came to be to start with. Furthermore, you must also understand the use of quantum field theory and its reference to physics. Lastly, the goal of the theoretical study is to make a statement in the field of general physics and impact the research being done on AI machine learning.
Logic and Logical Philosophy, 2010
The aim of this paper is to present some basic notions of the theory of quantum computing and to compare them with the basic notions of the classical theory of computation. I am convinced, that the results of quantum computation theory (QCT) are not only interesting in themselves, but also should be taken into account in discussions concerning the nature of mathematical knowledge. The philosophical discussion will however be postponed to another paper. QCT seems not to be well-known among philosophers (at least not to the degree it deserves), so the aim of this paper is to provide the necessary technical preliminaries presented in a way accessible to the general philosophical audience.
Eprint Arxiv Quant Ph 0511158, 2005
This paper discusses the important primitives of superposition and entanglement in QIP from physics of spin-1/2 particles. System of spin-1/2 particles present a logical and conceptual candidate to understand Quantum Computing. A pedagogical approach to abstract quantum information processing is considered in more concrete physical terms here.
Quantum computer science in combination with paradigms from computational neuroscience, specifically those from the field of artificial neural networks, seems to be promising for providing an outlook on a possible future of artificial intelligence. Within this elaboration, a quantum artificial neural network not only apportioning effects from quantum mechanics simulated on a von Neumann computer is proposed, but indeed for being processed on a quantum computer. Sooner or later quantum computers will replace classical von Neumann machines, which has been the motivation for this research. Although the proposed quantum artificial neural network is a classical feed forward one making use of quantum mechanical effects, it has, according to its novelty and otherness, been dedicated an own paper. Training such can only be simulated on von Neumann machines, which is pretty slow and not practically applicable (but nonetheless required for proofing the theorem), although the latter ones may be used to simulate an environment suitable for quantum computation. This is what has been realized during the SHOCID (Neukart, 2010) project for showing and proofing the advantages of quantum computers for processing artificial neural networks.
Romanian Physics, 2007
Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The effi-ciency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread ...
Quantum Models of Cognition and Decision
Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, “contextuality,” is away to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, “quantum entanglement,” allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision
Foundations of Physics
This article may be seen as a summary and a final discussion of the work that the author has done in recent years on the foundation of quantum theory. It is shown that quantum mechanics as a model follows under certain specific conditions from a quite different, much simpler model. This model is connected to the mind of an observer, or to the joint minds of a group of communicating observers. The model is based upon conceptual variables, and an important aspect is that an observer (a group of observers) must decide on which variable to measure. The model is then linked more generally to a theory of decisions. The results are discussed from several angles.
IRJET, 2022
Quantum theory is one of the most advanced and progressive fields of science today. It has given way to new horizons in modern technology. It has also opened the possibility of expressing and communicating information in different ways. Up until now the information was always expressed and communicated through physical or digital ways. In this paper we provide an in-depth look into the major concerns of Quantum computing and Quantum machine learning.
Foundations of Physics, 2023
This article may be seen as a summary and a final discussion of the work that the author has done in recent years on the foundation of quantum theory. It is shown that quantum mechanics as a model follows under certain specific conditions from a quite different, much simpler model. This model is connected to the mind of an observer, or to the joint minds of a group of communicating observers. The model is based upon conceptual variables, and an important aspect is that an observer (a group of observers) must decide on which variable to measure. The model is then linked more generally to a theory of decisions. The results are discussed from several angles. In particular, macroscopic consequences are treated briefly.
Cognitive Computation, 2011
Is quantum computing suitable for modeling problem solving, a domain which is traditionally reserved for the symbolic approach? We propose a hybrid quantum problem solving model. Our approach is motivated by several important theories from the fields of physics, computer science and psychology. We demonstrate our approach through a model for a quantum production system, based on the n-puzzle. The developed model can be extended in order to tackle any N -level depth search required by other problems. No preliminary knowledge concerning quantum computation is required.
Handbook of Quantum Logic and Quantum Structures, 2007
The mathematical formalism of quantum mechanics has been successfully employed in the last years to model situations in which the use of classical structures gives rise to problematical situations, and where typically quantum effects, such as 'contextuality' and 'entanglement', have been recognized. This 'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in particular, on the quantum models that have been elaborated to describe how concepts combine in cognitive science, and on the ensuing identification of a quantum structure in human thought. We point out that these results provide interesting insights toward the development of a unified theory for meaning and knowledge formalization and representation. Then, we analyze the technological aspects and implications of our approach, and a particular attention is devoted to the connections with symbolic artificial intelligence, quantum computation and robotics.
2020
All the quantum algorithms are based on a certain quantum computing model, varying from the quantum circuit, one-way quantum computation, adiabatic quantum computation and topological quantum computation. These four models are equivalent in computational power; among them, the quantum circuit model is most frequently used. In the circuit model, it has been proved that arbitrary single-qubit rotations plus twoqubit controlled-NOT gates are universal, i.e. they can provide a set of gates to implement any quantum algorithm. This article discusses the goal for this research: it is to given a lightning-fast (as-barebones-as-possible) definition of the quantum circuit model computing and leisurely development of quantum computation before actually getting around to sophisticated algorithms. In this article the main ideas of quantum software engineering is described.
2020
This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems. A dynamic system is a system where the rates of changes of its state variables are not negligible. Quantum entanglement is realized by the Spontaneous Parametric Down-conversion process. Two entangled autonomous systems exhibit correlated behavior without any classical communication in between them due to the quantum entanglement phenomenon. Specifically, the behavior of a system, Bob, at a distance, is correlated with a corresponding system, Alice. In an automation scenario, the 'Bob Robot' is entangled with the 'Alice Robot' in performing autonomous tasks without any classical connection between them. Quantum cryptography is a capability that allows guaranteed security. Such capabilities can be implemented in control of autonomous mechanical systems where, for instance, an 'Alice Autonomous System' can control a 'Bob Autonomous Sy...
2011
A simple example is provided showing that violation of free will allows to reproduce the quantum mechanical predictions, and that the Clauser-Horne parameter can take the maximum value 4 for a proper choice.
The main advantages of quantum computing are exponential computing power it provides. This exponential computing is derived from the supposition of the states, and possibility to use entanglement of particles to communicate over large distances. This paper shall attempt to identify what constitute quantum computer, its benefits, obstacle and research and in conclusion, I shall address the future direction of quantum computation.
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