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Journal articles on the topic 'Quantum Bayesian Networks'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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 pred

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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 th

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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 a

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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 mim

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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 domin

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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

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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 over

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Gentili, Pier Luigi. "Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing." Molecules 26, no. 19 (2021): 5987. http://dx.doi.org/10.3390/molecules26195987.

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Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex ar

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Kuravsky, L. S., G. A. Yuryev, N. E. Yuryeva, et al. "Development of Psychological Diagnostics Systems Basing on New Mathematical Representations." Experimental Psychology (Russia) 16, no. 2 (2023): 178–202. http://dx.doi.org/10.17759/exppsy.2023160211.

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<p style="text-align: justify;">Suggested is a new approach to development of the adaptive systems for psychological diagnostics, which can be considered as artificial intelligence tools for assessing the subject activities. It is based on the convolution of the applied Markovian process representing a diagnostic procedure under study into the quantum representation, which makes it possible to reveal the structure of this procedure with the aid of the quantum spectral analysis. When small samples of empirical data are in use to set up diagnostic tools, the considered quantum estimates ha

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Dai, Jiongyu, and Yong Deng. "A new method to predict the interference effect in quantum-like Bayesian networks." Soft Computing 24, no. 14 (2020): 10287–94. http://dx.doi.org/10.1007/s00500-020-04693-2.

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14

Luan, Tian, Zetong Li, Congcong Zheng, Xueheng Kuang, Xutao Yu, and Zaichen Zhang. "Quantum Tomography: From Markovianity to Non-Markovianity." Symmetry 16, no. 2 (2024): 180. http://dx.doi.org/10.3390/sym16020180.

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The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of

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A. Sankaran and K. Sathiyamurthy. "Quantum LLM Model for Entity and Semantic Relation Extraction in Drug Interactions." Advances in Artificial Intelligence and Machine Learning 05, no. 01 (2025): 3495–518. https://doi.org/10.54364/aaiml.2025.51200.

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In modern natural language processing, it is still difficult to extract entity and semantic links from biomedical literature, such as drug-drug, drug-gene, drug-test, drug-disease, drug-herb, drug-food and drug-lab range interactions. In this work XLNet, a large language model based on transformers, is finetuned with Bayesian network that have been improved by the Quantum Approximate Optimization Algorithm (QAOA) by using directed acyclic graphs (DAGs) and Conditional Probability Tables (CPTs) to model complicated biomedical interactions. This work combines the ability of XLNet to capture two-

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Hatefi, Armin, Ehsan Hatefi, and Roberto J. Lopez-Sastre. "Neural networks assisted Metropolis-Hastings for Bayesian estimation of critical exponent on elliptic black hole solution in 4D using quantum perturbation theory." Journal of Cosmology and Astroparticle Physics 2024, no. 09 (2024): 015. http://dx.doi.org/10.1088/1475-7516/2024/09/015.

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Abstract It is well-known that the critical gravitational collapse produces continuous self-similar solutions characterized by the Choptuik critical exponent, γ. We examine the solutions in the domains of the linear perturbation equations, considering the numerical measurement errors. Specifically, we study quantum perturbation theory for the four-dimensional Einstein-axion-dilaton system of the elliptic class of SL(2,ℝ) transformations. We develop a novel artificial neural network-assisted Metropolis-Hastings algorithm based on quantum perturbation theory to find the distribution of the criti

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Villalba-Diez, Javier, Miguel Gutierrez, Mercedes Grijalvo Martín, Tomas Sterkenburgh, Juan Carlos Losada, and Rosa María Benito. "Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization." Sensors 21, no. 15 (2021): 5031. http://dx.doi.org/10.3390/s21155031.

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With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional

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18

Huang, Zhiming, Lin Yang, and Wen Jiang. "Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Networks." Applied Mathematics and Computation 347 (April 2019): 417–28. http://dx.doi.org/10.1016/j.amc.2018.11.036.

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Chinnaraju, Arunraju. "Quantum Computing in Consumer Behavior: A Theoretical Framework for Market Prediction and Decision Analytics." International Journal of Advanced Research in Science, Communication and Technology 5, no. 2 (2025): 339–71. https://doi.org/10.48175/ijarsct-23352.

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<strong>This is the author-archived version of the article titled</strong><em>“Quantum Computing in Consumer Behavior: A Theoretical Framework for Market Prediction and Decision Analytics”</em>,published in <em>International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)</em>, Vol. 5, Issue 2, February 2025. The official published version is available at: https://doi.org/10.48175/IJARSCT-23352 URL: https://ijarsct.co.in/Paper23352.pdf This version has been deposited on Zenodo to enhance academic visibility and ensure indexing through scholarly datab

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Chaves, Rafael, Daniel Cavalcanti, and Leandro Aolita. "Causal hierarchy of multipartite Bell nonlocality." Quantum 1 (August 4, 2017): 23. http://dx.doi.org/10.22331/q-2017-08-04-23.

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As with entanglement, different forms of Bell nonlocality arise in the multipartite scenario. These can be defined in terms of relaxations of the causal assumptions in local hidden-variable theories. However, a characterisation of all the forms of multipartite nonlocality has until now been out of reach, mainly due to the complexity of generic multipartite causal models. Here, we employ the formalism of Bayesian networks to reveal connections among different causal structures that make a both practical and physically meaningful classification possible. Our framework holds for arbitrarily many

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Belliardo, Federico, Fabio Zoratti, Florian Marquardt, and Vittorio Giovannetti. "Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology." Quantum 8 (December 10, 2024): 1555. https://doi.org/10.22331/q-2024-12-10-1555.

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Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters. For each sensing platform, pinpointing the optimal controls to enhance the sensor&apos;s precision remains a challenging task. While an analytical solution might be out of reach, machine learning offers a promising avenue for many systems of interest, especially given the capabilities of contemporary hardware. We have introduced a versatile procedure capable of optimizing a wide range of problems in quantum metrology, estimation, and hypothesis testing by comb

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Moreira, Catarina, and Andreas Wichert. "Exploring the relations between Quantum-Like Bayesian Networks and decision-making tasks with regard to face stimuli." Journal of Mathematical Psychology 78 (June 2017): 86–95. http://dx.doi.org/10.1016/j.jmp.2016.10.004.

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23

Zolotin, A. A., and A. L. Tulupyev. "Sensitivity Statistical Estimates for Local A Posteriori Inference Matrix-Vector Equations in Algebraic Bayesian Networks over Quantum Propositions." Vestnik St. Petersburg University, Mathematics 51, no. 1 (2018): 42–48. http://dx.doi.org/10.3103/s1063454118010168.

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24

Sameer Joshi. "Technical innovations transforming the property and casualty insurance landscape." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 757–64. https://doi.org/10.30574/wjaets.2025.15.2.0530.

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The property and casualty insurance sector is experiencing profound technological transformation driven by digital innovations that fundamentally restructure traditional insurance models. This technical review explores how cloud-native architectures, artificial intelligence, telematics, and regulatory technologies are reshaping core insurance functions. Cloud platforms with microservices architectures enable superior scalability while significantly reducing operational costs across the insurance value chain. AI-driven algorithmic underwriting systems transcend traditional actuarial models thro

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Payandeh, Shahram. "Applications of Quantum Probability Amplitude in Decision Support Systems." Applied Computational Intelligence and Soft Computing 2023 (December 7, 2023): 1–14. http://dx.doi.org/10.1155/2023/5532174.

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Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the

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Ren, Zhiyuan, Shijie Zhou, Dong Liu, and Qihe Liu. "Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing." Applied Sciences 15, no. 14 (2025): 8092. https://doi.org/10.3390/app15148092.

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Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid

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Nasser, Maged, Naomie Salim, Hentabli Hamza, Faisal Saeed, and Idris Rabiu. "Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks." Molecules 26, no. 1 (2020): 128. http://dx.doi.org/10.3390/molecules26010128.

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Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficien

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Sadugol, Shreyas, and Lev Kaplan. "Quantum metrology in a lossless Mach–Zehnder interferometer using entangled photon inputs for a sequence of non-adaptive and adaptive measurements." AVS Quantum Science 5, no. 1 (2023): 014407. http://dx.doi.org/10.1116/5.0137125.

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Using multi-photon entangled input states, we estimate the phase uncertainty in a noiseless Mach–Zehnder interferometer using photon-counting detection. We assume a flat prior uncertainty and use Bayesian inference to construct a posterior uncertainty. By minimizing the posterior variance to get the optimal input states, we first devise an estimation and measurement strategy that yields the lowest phase uncertainty for a single measurement. N00N and Gaussian states are determined to be optimal in certain regimes. We then generalize to a sequence of repeated measurements, using non-adaptive and

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O’Gorman, B., R. Babbush, A. Perdomo-Ortiz, A. Aspuru-Guzik, and V. Smelyanskiy. "Bayesian network structure learning using quantum annealing." European Physical Journal Special Topics 224, no. 1 (2015): 163–88. http://dx.doi.org/10.1140/epjst/e2015-02349-9.

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Harikrishnakumar, Ramkumar, and Saideep Nannapaneni. "Forecasting Bike Sharing Demand Using Quantum Bayesian Network." Expert Systems with Applications 221 (July 2023): 119749. http://dx.doi.org/10.1016/j.eswa.2023.119749.

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Bilal, Muhammad, Eunkeu Oh, Rong Liu, Joyce C. Breger, Igor L. Medintz, and Yoram Cohen. "Bayesian Network Resource for Meta‐Analysis: Cellular Toxicity of Quantum Dots." Small 15, no. 34 (2019): 1900510. http://dx.doi.org/10.1002/smll.201900510.

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ZHAO, Xuewu, Guangliang LIU, Xindang CHENG, and Junzhong JI. "Bayesian network structure learning algorithm based on topological order and quantum genetic algorithm." Journal of Computer Applications 33, no. 6 (2013): 1595–99. http://dx.doi.org/10.3724/sp.j.1087.2013.01595.

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Bilal, Muhammad, Eunkeu Oh, Rong Liu, Joyce C. Breger, Igor L. Medintz, and Yoram Cohen. "Toxicity Models: Bayesian Network Resource for Meta‐Analysis: Cellular Toxicity of Quantum Dots (Small 34/2019)." Small 15, no. 34 (2019): 1970181. http://dx.doi.org/10.1002/smll.201970181.

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34

She, Lulu, Shilian Han, and Xinwang Liu. "Application of quantum-like Bayesian network and belief entropy for interference effect in multi-attribute decision making problem." Computers & Industrial Engineering 157 (July 2021): 107307. http://dx.doi.org/10.1016/j.cie.2021.107307.

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Pengyuan Zhu. "Virtual Reality Animation Interaction Design using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based Scene Modelling." Journal of Electrical Systems 20, no. 3s (2024): 2819–32. http://dx.doi.org/10.52783/jes.3182.

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Virtual Reality Animation Interaction Design is a dynamic field at the intersection of technology and creativity, where immersive experiences come to life through the fusion of animation and interactive design. There are some challenges involves in design of virtual Reality animation. The main challenge is animation image error involves in the design. To overcome this issue, present a Virtual Reality Animation Interaction Design Using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based scene modelling (VRAID-BPINN-AOA) is proposed. Initially, the animation ima

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Nguyen, Nam, and Kwang-Cheng Chen. "Bayesian Quantum Neural Networks." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3168675.

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Low, Guang Hao, Theodore J. Yoder, and Isaac L. Chuang. "Quantum inference on Bayesian networks." Physical Review A 89, no. 6 (2014). http://dx.doi.org/10.1103/physreva.89.062315.

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Shi, Hao, Chenghao Han, Peng Wang, and Ming Zhang. "Hybrid Quantum-Classical Multi-Agent Decision-Making Framework based on Hierarchical Bayesian Networks in the noisy intermediate-scale quantum era." Chinese Physics B, July 15, 2025. https://doi.org/10.1088/1674-1056/adefd7.

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Abstract Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making, their practical application faces two challenges in the noisy intermediate-scale quantum (NISQ) era. Limited qubit resources restrict direct application to large-scale inference tasks. Additionally, no quantum methods are currently available for multi-agent collaborative decision-making. To address these, we propose a hybrid quantum-classical multi-agent decision-making framework based on hierarchical Bayesian networks, comprising two novel methods. The first one is a hybrid quantum-classi

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Micadei, Kaonan, Gabriel T. Landi, and Eric Lutz. "Extracting Bayesian networks from multiple copies of a quantum system." Europhysics Letters, December 20, 2023. http://dx.doi.org/10.1209/0295-5075/ad177d.

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Abstract Despite their theoretical importance, dynamic Bayesian networks associated with quantum processes are currently not accessible experimentally. We here describe a general scheme to determine the multi-time path probability of a Bayesian network based on local measurements on independent copies of a composite quantum system combined with postselection. We further show that this protocol corresponds to a non-projective measurement. It thus allows the investigation of the multi-time properties of a given local observable while fully preserving all its quantum features.&#xD;

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Rinaldi, Enrico, Manuel González Lastre, Sergio García Herreros, et al. "Parameter estimation from quantum-jump data using neural networks." Quantum Science and Technology, April 9, 2024. http://dx.doi.org/10.1088/2058-9565/ad3c68.

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Abstract We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps.&#xD;We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that o

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Carrascal, Gines, Guillermo Botella, Alberto del Barrio, and David Kremer. "A Bayesian-network-based quantum procedure for failure risk analysis." EPJ Quantum Technology 10, no. 1 (2023). http://dx.doi.org/10.1140/epjqt/s40507-023-00171-4.

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AbstractStudying the propagation of failure probabilities in interconnected systems such as electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure for creating a model of the system on a quantum computer using a restricted representation of Bayesian networks. We present examples of this implementation on sample models using Qiskit and test them using both quantum simulators and IBM Quantum hardware. The results show a correlation in the precision of the results when considering the number of Monte Carlo iterations

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Moreira, Catarina, and Andreas Wichert. "Quantum-Like Bayesian Networks for Modeling Decision Making." Frontiers in Psychology 7 (January 26, 2016). http://dx.doi.org/10.3389/fpsyg.2016.00011.

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Ohno, Hiroshi. "Quantum inference for Bayesian networks: an empirical study." Quantum Machine Intelligence 7, no. 1 (2025). https://doi.org/10.1007/s42484-025-00251-x.

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Nayak, Padmil, and Karthick Seshadri. "Towards quantum amenable Bayesian networks: classical transformation to facilitate quantum inference." Quantum Machine Intelligence 7, no. 1 (2024). https://doi.org/10.1007/s42484-024-00227-3.

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Zio, Enrico. "Quantum reliability analysis of a wireless telecommunication network." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, June 30, 2023. http://dx.doi.org/10.1177/1748006x231182455.

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This work is positioned within the new field of quantum probability theory and its application to the reliability analysis of wireless telecommunication networks. Specifically, we present the development of a Quantum Bayesian Network (QBN) for calculating the reliability of a 5G wireless telecommunication network. The qualitative comparison with a classical Bayesian Network model allows highlighting the role of interferences in the calculation of the reliability of a complex system such as a wireless telecommunication network.

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Prielinger, Luise, Álvaro G. Iñesta, and Gayane Vardoyan. "Surrogate-guided optimization in quantum networks." npj Quantum Information 11, no. 1 (2025). https://doi.org/10.1038/s41534-025-01048-3.

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Abstract When physical architectures become too complex for analytical study, numerical simulation proves essential to investigate quantum network behavior. Although highly informative, these simulations involve intricate numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. We introduce a more efficient computational framework that employs machine learning models as surrogates for the objective function. We demonstrate the effectiveness of our approach by applying it to three well-kn

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Zhang, Na, Haiyan Wang, and Zaiwu Gong. "Dynamic multi-attribute grey target group decision model based on quantum-like Bayesian networks." Grey Systems: Theory and Application, November 29, 2023. http://dx.doi.org/10.1108/gs-08-2023-0072.

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PurposeGrey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.Design/methodology/approachFirstly

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Nolan, Samuel, Augusto Smerzi, and Luca Pezzè. "A machine learning approach to Bayesian parameter estimation." npj Quantum Information 7, no. 1 (2021). http://dx.doi.org/10.1038/s41534-021-00497-w.

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AbstractBayesian estimation is a powerful theoretical paradigm for the operation of the approach to parameter estimation. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its use to systems that can be explicitly modeled. In this theoretical study, we formulate parameter estimation as a classification task and use artificial neural networks to efficiently perform Bayesian estimation. We show that the network’s posterior distribution is centered at the true (unknown) value of the parameter within an unce

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Leopoldo, Sarra, and Marquardt Florian. "Deep Bayesian Experimental Design for Quantum Many-body Systems." June 26, 2023. https://doi.org/10.5281/zenodo.8084079.

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Abstract:

Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both

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Hong, Seokmin. "Solving inference problems of Bayesian networks by probabilistic computing." AIP Advances 13, no. 7 (2023). http://dx.doi.org/10.1063/5.0157394.

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Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block. This new type of computing scheme tackles domain-specific and computationally hard problems that can be efficiently solved using probabilistic algorithms compared to classical deterministic counterparts. Here, we apply the probabilistic computing scheme to various inference problems of Bayesian networks with non-linear synaptic connections witho

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