Relevant bibliographies by topics / Quantum Bayesian Networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Quantum Bayesian Networks'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Quantum Bayesian Networks"

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Wichert, Andreas, Catarina Moreira, and Peter Bruza. "Balanced Quantum-Like Bayesian Networks." Entropy 22, no. 2 (2020): 170. http://dx.doi.org/10.3390/e22020170.

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Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions. To address this problem, models based on quantum probability theory, such as the quantum-like Bayesian networks, have been proposed. However, this model makes use of a Bayes normalisation factor during probabilistic inference to convert the likelihoods that result from quantum interference effects into probability values. The interpretation of this operation is not clear and leads to extremely skewed intensity waves that make the task of prediction of these irrational decisions challenging. This article proposes the law of balance, a novel mathematical formalism for probabilistic inferences in quantum-like Bayesian networks, based on the notion of balanced intensity waves. The general idea is to balance the intensity waves resulting from quantum interference in such a way that, during Bayes normalisation, they cancel each other. With this representation, we also propose the law of maximum uncertainty, which is a method to predict these paradoxes by selecting the amplitudes of the wave with the highest entropy. Empirical results show that the law of balance together with the law of maximum uncertainty were able to accurately predict different experiments from cognitive psychology showing paradoxical or irrational decisions, namely in the Prisoner’s Dilemma game and the Two-Stage Gambling Game.
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TUCCI, ROBERT R. "QUANTUM BAYESIAN NETS." International Journal of Modern Physics B 09, no. 03 (1995): 295–337. http://dx.doi.org/10.1142/s0217979295000148.

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We begin with a review of a well-known class of networks, Classical Bayesian (CB) nets (also called causal probabilistic nets by some). Given a situation which includes randomness, CB nets are used to calculate the probabilities of various hypotheses about the situation, conditioned on the available evidence. We introduce a new class of networks, which we call Quantum Bayesian (QB) nets, that generalize CB nets to the quantum mechanical regime. We explain how to use QB nets to calculate quantum mechanical conditional probabilities (in case of either sharp or fuzzy observations), and discuss the connection of QB nets to Feynman Path integrals. We give examples of QB nets that involve a single spin-[Formula: see text] particle passing through a configuration of two or three Stern—Gerlach magnets. For the examples given, we present the numerical values of various conditional probabilities, as calculated by a general computer program specially written for this purpose.
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Moreira, Catarina, and Andreas Wichert. "Are quantum-like Bayesian networks more powerful than classical Bayesian networks?" Journal of Mathematical Psychology 82 (February 2018): 73–83. http://dx.doi.org/10.1016/j.jmp.2017.11.003.

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Borujeni, Sima E., Saideep Nannapaneni, Nam H. Nguyen, Elizabeth C. Behrman, and James E. Steck. "Quantum circuit representation of Bayesian networks." Expert Systems with Applications 176 (August 2021): 114768. http://dx.doi.org/10.1016/j.eswa.2021.114768.

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Fathallah, Walid, Nahla Ben Amor, and Philippe Leray. "Approximate inference on optimized quantum Bayesian networks." International Journal of Approximate Reasoning 175 (December 2024): 109307. http://dx.doi.org/10.1016/j.ijar.2024.109307.

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Zardini, Enrico, Massimo Rizzoli, Sebastiano Dissegna, Enrico Blanzieri, and Davide Pastorello. "Reconstructing Bayesian networks on a quantum annealer." Quantum Information and Computation 22, no. 15&16 (2022): 1320–50. http://dx.doi.org/10.26421/qic22.15-16-4.

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Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to encode this task, i.e., the Bayesian network structure learning (BNSL), into a form that can be solved through quantum annealing, but they have not provided an experimental evaluation of it. In this paper, we present (i) an implementation in Python of O'Gorman's algorithm, (ii) a divide et impera approach that allows addressing BNSL problems of larger sizes in order to overcome the limitations imposed by the current architectures, and (iii) their empirical evaluation. Specifically, several problems with an increasing number of variables have been used in the experiments. The results have shown the effectiveness of O'Gorman's formulation for BNSL instances of small sizes, and the superiority of the divide et impera approach on the direct execution of O'Gorman's algorithm.
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Maksimovic, Milan, and Ivan S. Maksymov. "Transforming Neural Networks into Quantum-Cognitive Models: A Research Tutorial with Novel Applications." Technologies 13, no. 5 (2025): 183. https://doi.org/10.3390/technologies13050183.

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Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing, and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics.
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Marcot, Bruce G. "EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks." Entropy 23, no. 4 (2021): 441. http://dx.doi.org/10.3390/e23040441.

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A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be >1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo’s paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures—such as state-and-transition models, predator–prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more—could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.
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Mutlu, Ece C. "Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13730–31. http://dx.doi.org/10.1609/aaai.v34i10.7137.

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This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.
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Pothos, Emmanuel M., Stephan Lewandowsky, Irina Basieva, Albert Barque-Duran, Katy Tapper, and Andrei Khrennikov. "Information overload for (bounded) rational agents." Proceedings of the Royal Society B: Biological Sciences 288, no. 1944 (2021): 20202957. http://dx.doi.org/10.1098/rspb.2020.2957.

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Bayesian inference offers an optimal means of processing environmental information and so an advantage in natural selection. We consider the apparent, recent trend in increasing dysfunctional disagreement in, for example, political debate. This is puzzling because Bayesian inference benefits from powerful convergence theorems, precluding dysfunctional disagreement. Information overload is a plausible factor limiting the applicability of full Bayesian inference, but what is the link with dysfunctional disagreement? Individuals striving to be Bayesian-rational, but challenged by information overload, might simplify by using Bayesian networks or the separation of questions into knowledge partitions, the latter formalized with quantum probability theory. We demonstrate the massive simplification afforded by either approach, but also show how they contribute to dysfunctional disagreement.
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Dissertations / Theses on the topic "Quantum Bayesian Networks"

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Pejic, Michael. "Quantum Bayesian networks with application to games displaying Parrondo's paradox." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3685984.

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<p> Bayesian networks and their accompanying graphical models are widely used for prediction and analysis across many disciplines. We will reformulate these in terms of linear maps. This reformulation will suggest a natural extension, which we will show is equivalent to standard textbook quantum mechanics. Therefore, this extension will be termed <i>quantum.</i> However, the term <i> quantum</i> should not be taken to imply this extension is necessarily only of utility in situations traditionally thought of as in the domain of quantum mechanics. In principle, it may be employed in any modelling situation, say forecasting the weather or the stock market&mdash;it is up to experiment to determine if this extension is useful in practice. Even restricting to the domain of quantum mechanics, with this new formulation the advantages of Bayesian networks can be maintained for models incorporating quantum and mixed classical-quantum behavior. The use of these will be illustrated by various basic examples. </p><p> Parrondo's paradox refers to the situation where two, multi-round games with a fixed winning criteria, both with probability greater than one-half for one player to win, are combined. Using a possibly biased coin to determine the rule to employ for each round, paradoxically, the previously losing player now wins the combined game with probabilitygreater than one-half. Using the extended Bayesian networks, we will formulate and analyze classical observed, classical hidden, and quantum versions of a game that displays this paradox, finding bounds for the discrepancy from naive expectations for the occurrence of the paradox. A quantum paradox inspired by Parrondo's paradox will also be analyzed. We will prove a bound for the discrepancy from naive expectations for this paradox as well. Games involving quantum walks that achieve this bound will be presented.</p>
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Venkataramani, Praveen. "Sequential quantum dot cellular automata design and analysis using Dynamic Bayesian Networks." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002787.

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Srivastava, Saket. "Probabilistic modeling of quantum-dot cellular automata." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002399.

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Leelar, Bhawani Shankar. "Machine Learning Algorithms Using Classical And Quantum Photonics." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4303.

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ABSTRACT In the modern day , we are witnessing two complementary trends, exponential growth in data and shrinking of chip size. The Data is approaching to 44 zettabytes by 2020 and the chips are now available with 10nm technology. The hyperconnectivity between machine-to-machine and humanto- machine creates multi-dimensional data which is more complex. Our thesis addresses the quantum meta layer abstraction which provides the interface to the Application layer to design quantum and classical algorithms. The first part of the thesis addresses the quantum algorithms and second part address classical algorithms running on top of quantum meta layer. In the first part of our thesis we explored quantum stochastic algorithm for ranking Quantum Webpages, analogous to the classical Google PageRank. The architecture is a six-waveguide photonic lattice that runs finely-tuned quantum stochastic walk. The evolution of density matrix solves the ranking of quantum webpages. We force the photon stochastic walk for quantum PageRank by matching the entries of Google matrix with parameters of the Kossakowski-Lindblad master equation. We have done extensive simulation to observe the density matrix evolution with different parameter settings. We have used noise in the Kossakowski-Lindblad master equation to break the symmetry (reciprocity) property of quantum system, which helps in distinguishable measurement of the quantum PageRank. We next propose a new quantum deep learning with photonic lattice waveguide as a feedforward neural network. The proposed deep photonic neural network uses the quantum properties for learning. The hidden layers of our deep photonic neural network can be designed to learn object representation and mentains the quantum quantum properties for longer time for optimal learning. The second part of the thesis discusses the data based learning. We have used data graph method which captures the system representation. The proposed data graph model captures and encodes the data efficiently and then the data graph is updated and trained with new data to provide efficient predictions. The model retains the previously learned knowledge by transfer learning and improves it with new training. The proposed method is highly adaptive and scalable for different real-time scenarios. Data graph models the system where every node (object) is associated with data and if two objects are related then they are linked with a data edge. The proposed algorithm is an incremental algorithm which learns hidden objects and hidden relationships through the data pattern over time and updates the model accordingly. We have used algebraic graph transformation methods to trigger the mutation of the Data Graph. This new updated Data Graph behaves differently for the data it observes. We explore more into machine learning algorithms and have proposed a complete framework to predict the state of the system based on the system parameters. We have proposed the discretization of the data points using the symbol algebra and used Bayesian machine learning algorithm to select the best model to represent the new data. Symbol algebra provides unified language platform to different sensor data and it can process both, the discrete and continuous data. The portability of unified language platform in processing heterogeneous and homogeneous data increases the hypotheses space and Bayesian machine learning gets more degrees of freedom in choosing the best model with high measure of confidence level in the predicted state.
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Books on the topic "Quantum Bayesian Networks"

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Tiwari, Sandip. Information mechanics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198759874.003.0001.

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Information is physical, so its manipulation through devices is subject to its own mechanics: the science and engineering of behavioral description, which is intermingled with classical, quantum and statistical mechanics principles. This chapter is a unification of these principles and physical laws with their implications for nanoscale. Ideas of state machines, Church-Turing thesis and its embodiment in various state machines, probabilities, Bayesian principles and entropy in its various forms (Shannon, Boltzmann, von Neumann, algorithmic) with an eye on the principle of maximum entropy as an information manipulation tool. Notions of conservation and non-conservation are applied to example circuit forms folding in adiabatic, isothermal, reversible and irreversible processes. This brings out implications of fluctuation and transitions, the interplay of errors and stability and the energy cost of determinism. It concludes discussing networks as tools to understand information flow and decision making and with an introduction to entanglement in quantum computing.
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Book chapters on the topic "Quantum Bayesian Networks"

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Moreira, Catarina, and Andreas Wichert. "The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks." In Quantum Interaction. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28675-4_10.

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Fathallah, Walid, Nahla Ben Amor, and Philippe Leray. "An Optimized Quantum Circuit Representation of Bayesian Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45608-4_13.

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Nayak, Padmil, and Karthick Seshadri. "Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks." In Big Data and Artificial Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49601-1_10.

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"Quantum Bayesian Networks and Quasi-Probability." In Quantum Models of Cognition and Decision, 2nd ed. Cambridge University Press, 2024. http://dx.doi.org/10.1017/9781009205351.013.

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Rajeshwari, G., S. Mownika, G. Anupriya, and R. Kishore. "Fraud Detection in E-Commerce Transactions Using Machine Learning Techniques and Quantum Networks." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5832-0.ch010.

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Fraud poses a significant threat across various sectors, with the e-commerce industry being particularly vulnerable based on quantum network. Using quantum networks for detecting fraud in e-commerce transactions has the potential to completely change online security. Quantum networks rely on the principles of quantum mechanics to provide the highest level of security when transmitting data. Companies facilitating online payments gather extensive data on user transactions, leveraging machine learning techniques to differentiate between legitimate and fraudulent activities. To enhance expertise in fraud detection, machine learning methods are employed to identify online payment fraud within e-commerce transactions. The dataset, structured at the transaction level, is analysed to uncover patterns distinguishing fraudulent behaviour from normal transactions. Feature engineering, such as incorporating user-level statistics like mean and standard deviation, aids in pattern recognition—a common practice in models like LGBMs (light gradient boosting machines). Detecting fraud presents a challenge due to the imbalance between fraudulent and non-fraudulent data. The performance of the model is evaluated using metrics such as accuracy and F1 score. The current system employs Bayesian optimization techniques to refine LGBM and XGBoost models. The proposed model aims to identify consumer fraud by analysing purchasing patterns and historical data using machine learning methodologies, specifically adopting a classification approach. Tree-based methods, including tree-based bagging and boosting techniques such as LGBM, XGBoost, CatBoost, and deep learning, are utilized. The synthetic minority over-sampling technique (SMOTE) is used to balance the imbalanced data. The primary aim is to create a reliable fraud detection system that is suited to the e-commerce environment.
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Klepac, G. "The Schrödinger equation as inspiration for a client portfolio simulation hybrid system based on dynamic Bayesian networks and the REFII model." In Quantum Inspired Computational Intelligence. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-804409-4.00012-7.

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Jothishwar, S., Visnu Dharsini S., S. Dinesh, and K. Jayasurya. "Short-Term Traffic Prediction." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-9336-9.ch024.

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Traffic prediction accuracy is pivotal for fruitful urban transportation management. Existing methodologies often struggle to record complex spatio-temporal features of traffic patterns, which results in sub-optimal congestion management. This approach integrates Bayesian inference with a graph attention network (GAN) and a SARIMA model to comprehensively record complex traffic dynamics. The GAN focuses on spatial-dependencies in data, whereas SARIMA considers temporal patterns. Bayesian inference facilitates seamless integration of predictions from both models, enhancing overall forecasting accuracy. To further enhance the accuracy and efficiency of traffic prediction and management, quantum networking is integrated with the Bayesian fusion of GAN and SARIMA. To evaluate, metrics like mean absolute error, root mean squared error, and mean absolute percentage error are calculated. The research contributes to advancing traffic forecasting methodologies and holds promise for enhancing short-term traffic management strategies.
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Conference papers on the topic "Quantum Bayesian Networks"

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Krebs, Florian, Hermann Fürntratt, Roland Unterberger, and Franz Graf. "Modeling Musical Knowledge With Quantum Bayesian Networks." In 2024 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2024. https://doi.org/10.1109/cbmi62980.2024.10859245.

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Mayorga Redondo, Alejandro, and Vicente Moret Bonillo. "Study and Implementation of Quantum Bayesian Networks." In VII Congreso XoveTIC: impulsando el talento científico. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.16.

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This project deals with the implementation of Bayesian networks, a type of artificial intelligence based on graph theory and conditional probability, in the quantum computing paradigm. Examples related to the world of sports will be used as input in order to test the developed model. Besides, a comparison between implementations in classical and quantum paradigms will be carried out, so their differences in performance and attributes can be appreciated. The work will conclude with a debate about quantum computing and its future as a basis for executing AI models.
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Regmi, Sangita, Ashley N. Blackwell, Amirali Khannejad, et al. "Bayesian quantum state reconstruction with a learning-based tuned prior." In Quantum 2.0. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/quantum.2023.qm4b.3.

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We demonstrate machine-learning-enhanced Bayesian quantum state tomography on near-term intermediate-scale quantum hardware. Our approach to selecting prior distributions leverages pre-trained neural networks incorporating measurement data and en-ables improved inference times over standard prior distributions.
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Borujeni, Sima E., Nam H. Nguyen, Saideep Nannapaneni, Elizabeth C. Behrman, and James E. Steck. "Experimental evaluation of quantum Bayesian networks on IBM QX hardware." In 2020 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2020. http://dx.doi.org/10.1109/qce49297.2020.00053.

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Kishore, Lal, and M. J. S. Rangachar. "Reliability Analysis of Quantum Cellular Automata Circuits Using Bayesian Networks." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.218.

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Jiang, Zhenhua, and Linh Nguyen. "Learning Quantum System Disturbance Models with Probabilistic Bayesian Neural Networks." In NAECON 2023 - IEEE National Aerospace and Electronics Conference. IEEE, 2023. http://dx.doi.org/10.1109/naecon58068.2023.10365822.

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Harikrishnakumar, Ramkumar, Sima E. Borujeni, Syed Farhan Ahmad, and Saideep Nannapaneni. "Rebalancing Bike Sharing Systems under Uncertainty using Quantum Bayesian Networks." In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2021. http://dx.doi.org/10.1109/qce52317.2021.00078.

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Sakhnenko, Alona, Julian Sikora, and Jeanette Lorenz. "Buildung Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset." In ReAQCT '24: Recent Advances in Quantum Computing and Technology. ACM, 2024. http://dx.doi.org/10.1145/3665870.3665872.

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Nikoloska, Ivana, and Osvaldo Simeone. "Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines." In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2022. http://dx.doi.org/10.1109/mlsp55214.2022.9943342.

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Wang, Yingying, Rundan Zhang, and Fei Li. "Applying Bayesian information criterion score in binary code quantum-behaved particle swarm optimization algorithm for learning Bayesian networks." In International Conference on Electrical and Electronics Engineering. WIT Press, 2014. http://dx.doi.org/10.2495/iceee140101.

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