{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:10:43Z","timestamp":1766139043951,"version":"build-2065373602"},"reference-count":117,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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 or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum tunnelling neural networks (QT-NNs) inspired by human brain processes alongside quantum cognition theory to classify image datasets while emulating human perception and judgment. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making. We also reveal that the QT-NN model can be trained up to 50 times faster than its classical counterpart.<\/jats:p>","DOI":"10.3390\/bdcc9010012","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:28:31Z","timestamp":1736854111000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4979-7737","authenticated-orcid":false,"given":"Milan","family":"Maksimovic","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1428-1216","authenticated-orcid":false,"given":"Ivan S.","family":"Maksymov","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Busemeyer, J.R., and Bruza, P.D. (2012). Quantum Models of Cognition and Decision, Oxford University Press.","DOI":"10.1017\/CBO9780511997716"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1650692","DOI":"10.1080\/23311975.2019.1650692","article-title":"Uncertainty in decision-making: A review of the international business literature","volume":"6","author":"Sniazhko","year":"2019","journal-title":"Cogent Bus. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8866386","DOI":"10.1155\/2020\/8866386","article-title":"From uncertainty to anxiety: How uncertainty fuels anxiety in a process mediated by intolerance of uncertainty","volume":"2020","author":"Gu","year":"2020","journal-title":"Neural Plast."},{"key":"ref_4","unstructured":"Strogatz, S.H. (2015). Nonlinear Dynamics and Chaos. With Applications to Physics, Biology, Chemistry, and Engineering, CRC Press. [2nd ed.]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1834759","DOI":"10.1080\/21614083.2020.1834759","article-title":"What do reinforcement and confidence have to do with it? A systematic pathway analysis of knowledge, competence, confidence, and intention to change","volume":"9","author":"Lucero","year":"2020","journal-title":"J. Eur. CME."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods","volume":"110","author":"Waegeman","year":"2021","journal-title":"Mach. Learn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","article-title":"A survey of uncertainty in deep neural networks","volume":"56","author":"Gawlikowski","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13321-023-00712-0","article-title":"Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties","volume":"15","author":"Guha","year":"2023","journal-title":"J. Cheminform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"455305","DOI":"10.1088\/1751-8121\/acfd6b","article-title":"Uncertainty of feed forward neural networks recognizing quantum contextuality","volume":"56","author":"Wasilewski","year":"2024","journal-title":"J. Phys. A Math. Theor."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Karaca, Y., Baleanu, D., Zhang, Y.D., Gervasi, O., and Moonis, M. (2022). Multi-chaos, fractal and multi-fractional AI in different complex systems. Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems, Academic Press.","DOI":"10.1016\/B978-0-323-90032-4.00016-X"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bobadilla-Suarez, S., Guest, O., and Love, B.C. (2020). Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain. Commun. Biol., 3.","DOI":"10.1038\/s42003-020-01315-3"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"036107","DOI":"10.1063\/5.0225771","article-title":"Quantum-tunneling deep neural network for optical illusion recognition","volume":"2","author":"Maksymov","year":"2024","journal-title":"APL Mach. Learn."},{"key":"ref_13","unstructured":"Haykin, S. (1998). Neural Networks: A Comprehensive Foundation, Pearson-Prentice Hall."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, P. (2017). MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence, Apress.","DOI":"10.1007\/978-1-4842-2845-6_1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., and Bloch, I. (2020, January 23\u201328). TRADI: Tracking Deep Neural Network Weight Distributions. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58520-4_7"},{"key":"ref_16","first-page":"625","article-title":"Why does unsupervised pre-training help deep learning?","volume":"11","author":"Erhan","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MCI.2022.3155327","article-title":"Hands-on Bayesian neural networks\u2013A tutorial for deep learning users","volume":"17","author":"Jospin","year":"2022","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_18","unstructured":"Wang, D.B., Feng, L., and Zhang, M.L. (2021, January 6\u201314). Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence. Proceedings of the Advances in Neural Information Processing Systems, Virtual."},{"key":"ref_19","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., and Li, Y. (2022, January 17\u201323). Mitigating Neural Network Overconfidence with Logit Normalization. Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"54805","DOI":"10.1109\/ACCESS.2022.3175195","article-title":"Reducing overconfidence predictions in autonomous driving perception","volume":"10","author":"Melotti","year":"2022","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102028","DOI":"10.1016\/j.ribaf.2023.102028","article-title":"Revisiting overconfidence in investment decision-making: Further evidence from the U. S. market","volume":"66","author":"Bouteska","year":"2023","journal-title":"Res. Int. Bus. Financ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P.H., and Gal, Y. (2023, January 17\u201324). Deep Deterministic Uncertainty: A New Simple Baseline. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02336"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Weiss, M., G\u00f3mez, A.G., and Tonella, P. (2023). Generating and detecting true ambiguity: A forgotten danger in DNN supervision testing. Empir. Softw. Eng., 146.","DOI":"10.1007\/s10664-023-10393-w"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., and Clune, J. (2015, January 7\u201312). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref_25","unstructured":"Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.Q. (2017, January 6\u201311). On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning\u2013Volume 70. JMLR.org, Sydney, NSW, Australia. ICML\u201917."},{"key":"ref_26","unstructured":"Moon, J., Kim, J., Shin, Y., and Hwang, S. (2020, January 13\u201318). Confidence-aware learning for deep neural networks. Proceedings of the 37th International Conference on Machine Learning, Virtual Event. ICML\u201920."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107214","DOI":"10.1016\/j.infsof.2023.107214","article-title":"Predicting neural network confidence using high-level feature distance","volume":"159","author":"Wang","year":"2023","journal-title":"Inf. Softw. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1038\/s41562-024-01914-8","article-title":"The neural network RTNet exhibits the signatures of human perceptual decision-making","volume":"8","author":"Rafiei","year":"2024","journal-title":"Nat. Hum. Behav."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, S., Xiao, T.P., Kwon, J., Debusschere, B.J., Agarwal, S., Incorvia, J.A.C., and Bennett, C.H. (2022). Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing. Front. Nanotechnol., 4.","DOI":"10.3389\/fnano.2022.1021943"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/s41534-017-0032-4","article-title":"Quantum generalisation of feedforward neural networks","volume":"3","author":"Wan","year":"2017","journal-title":"Npj Quantum Inf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1038\/s41467-020-14454-2","article-title":"Training deep quantum neural networks","volume":"11","author":"Beer","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.neucom.2021.02.060","article-title":"Quantum probability-inspired graph neural network for document representation and classification","volume":"445","author":"Yan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s42484-021-00046-w","article-title":"QDNN: Deep neural networks with quantum layers","volume":"3","author":"Zhao","year":"2021","journal-title":"Quantum Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7760","DOI":"10.1038\/s41467-024-51509-0","article-title":"Photonic probabilistic machine learning using quantum vacuum noise","volume":"15","author":"Choi","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"020001","DOI":"10.1063\/5.0203600","article-title":"A quantum information theoretic view on a deep quantum neural network","volume":"3061","author":"Hiesmayr","year":"2024","journal-title":"AIP Conf. Proc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s42484-024-00191-y","article-title":"On the interpretability of quantum neural networks","volume":"6","author":"Pira","year":"2024","journal-title":"Quantum Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100619","DOI":"10.1016\/j.cosrev.2024.100619","article-title":"Systematic literature review: Quantum machine learning and its applications","volume":"51","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43586-020-00001-2","article-title":"Bayesian statistics and modelling","volume":"1","author":"Depaoli","year":"2021","journal-title":"Nat. Rev. Methods Prim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.neunet.2021.07.034","article-title":"Quantum neuron with real weights","volume":"143","author":"Monteiro","year":"2021","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/s41467-023-39785-8","article-title":"Deep quantum neural networks on a superconducting processor","volume":"14","author":"Pan","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"94310","DOI":"10.1109\/ACCESS.2019.2929084","article-title":"Detecting entanglement with deep quantum neural networks","volume":"7","author":"Qiu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.cjph.2024.03.026","article-title":"Superposition-enhanced quantum neural network for multi-class image classification","volume":"89","author":"Bai","year":"2024","journal-title":"Chin. J. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nielsen, M., and Chuang, I. (2002). Quantum Computation and Quantum Information, Oxford University Press.","DOI":"10.1119\/1.1463744"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s11128-022-03466-0","article-title":"Quantum activation functions for quantum neural networks","volume":"21","author":"Maronese","year":"2023","journal-title":"Quantum Inf. Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115892","DOI":"10.1016\/j.eswa.2021.115892","article-title":"Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson\u2019s disease and COVID-19","volume":"187","author":"Parisi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.neunet.2019.03.005","article-title":"Recent advances in physical reservoir computing: A review","volume":"115","author":"Tanaka","year":"2019","journal-title":"Neural Newt."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"093901","DOI":"10.1103\/PhysRevLett.125.093901","article-title":"Theory of neuromorphic computing by waves: Machine learning by rogue waves, dispersive shocks, and solitons","volume":"125","author":"Marcucci","year":"2020","journal-title":"Phys. Rev. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Maksymov, I.S. (2023). Analogue and physical reservoir computing using water waves: Applications in power engineering and beyond. Energies, 16.","DOI":"10.3390\/en16145366"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1126\/science.abp8064","article-title":"Nanosecond protonic programmable resistors for analog deep learning","volume":"377","author":"Onen","year":"2022","journal-title":"Science"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s44172-023-00074-3","article-title":"Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach","volume":"2","author":"Ye","year":"2023","journal-title":"Commun. Eng."},{"key":"ref_51","unstructured":"McQuarrie, D.A., and Simon, J.D. (1997). Physical Chemistry\u2014A Molecular Approach, Prentice Hall."},{"key":"ref_52","unstructured":"Griffiths, D.J. (2004). Introduction to Quantum Mechanics, Prentice Hall."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Maksymov, I.S. (2024). Quantum-inspired neural network model of optical illusions. Algorithms, 17.","DOI":"10.3390\/a17010030"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s00422-003-0436-4","article-title":"Quantum Zeno features of bistable perception","volume":"90","author":"Atmanspacher","year":"2004","journal-title":"Biol. Cybern."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.biosystems.2005.11.005","article-title":"Quantum-like brain: \u201cInterference of minds\u201d","volume":"84","author":"Khrennikov","year":"2006","journal-title":"Biosystems"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1146\/annurev-psych-033020-123501","article-title":"Quantum Cognition","volume":"73","author":"Pothos","year":"2022","journal-title":"Annu. Rev. Psychol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Galam, S. (2012). Sociophysics: A Physicist\u2019s Modeling of Psycho-Political Phenomena, Springer.","DOI":"10.1007\/978-1-4614-2032-3"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Maksymov, I.S., and Pogrebna, G. (2024). Quantum-mechanical modelling of asymmetric opinion polarisation in social networks. Information, 15.","DOI":"10.3390\/info15030170"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Maksymov, I.S., and Pogrebna, G. (2024). The physics of preference: Unravelling imprecision of human preferences through magnetisation dynamics. Information, 15.","DOI":"10.3390\/info15070413"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Benedek, G., and Caglioti, G. (2018, January 3\u20137). Graphics and Quantum Mechanics\u2013The Necker Cube as a Quantum-like Two-Level System. Proceedings of the 18th International Conference on Geometry and Graphics, Milan, Italy.","DOI":"10.1007\/978-3-319-95588-9_12"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.pbiomolbio.2018.01.006","article-title":"The quantum physics of synaptic communication via the SNARE protein complex","volume":"135","author":"Georgiev","year":"2018","journal-title":"Prog. Biophys. Mol."},{"key":"ref_62","unstructured":"Georgiev, D.D. (2019). Quantum Information and Consciousness, CRC Press."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2450256","DOI":"10.1142\/S0217979224502564","article-title":"Causal potency of consciousness in the physical world","volume":"38","author":"Georgiev","year":"2024","journal-title":"Int. J. Mod. Phys. B"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"025001","DOI":"10.1103\/RevModPhys.91.025001","article-title":"Quantum resource theories","volume":"91","author":"Chitambar","year":"2019","journal-title":"Rev. Mod. Phys."},{"key":"ref_65","unstructured":"Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv."},{"key":"ref_66","unstructured":"Pope, P.E., Zhu, C., Abdelfattah, M., Goldblum, M., and Goldstein, T. (2021, January 3\u20137). The Intrinsic Dimension of Images and Its Impact on Learning. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event, Austria."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_68","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 3\u20136). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the Seventh International Conference on Document Analysis and Recognition, Edinburgh, Scotland, UK."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","article-title":"The MNIST database of handwritten digit images for machine learning research","volume":"29","author":"Deng","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kayed, M., Anter, A., and Mohamed, H. (2020, January 8\u20139). Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture. Proceedings of the 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), Aswan, Egypt.","DOI":"10.1109\/ITCE48509.2020.9047776"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41534-024-00902-0","article-title":"Trainability barriers and opportunities in quantum generative modeling","volume":"10","author":"Rudolph","year":"2024","journal-title":"Npj Quantum Inf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1214\/aop\/1176996454","article-title":"I-divergence geometry of probability distributions and minimization problems","volume":"3","author":"Csiszar","year":"1975","journal-title":"Ann. Probab."},{"key":"ref_73","unstructured":"Burnham, K.P., and Anderson, D.R. (2002). Model Selection and Multi-Model Inference, Spriger."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e12595","DOI":"10.1002\/eng2.12595","article-title":"Dynamic fine-tuning layer selection using Kullback\u2013Leibler divergence","volume":"5","author":"Wanjiku","year":"2023","journal-title":"Eng. Rep."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TIT.2003.813506","article-title":"A new metric for probability distributions","volume":"49","author":"Endres","year":"2003","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Nielsen, F. (2019). On the Jensen\u2013Shannon symmetrization of distances relying on abstract means. Entropy, 21.","DOI":"10.3390\/e21050485"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Go, J., Baek, B., and Lee, C. (2004, January 18\u201320). Analyzing Weight Distribution of Feedforward Neural Networks and Efficient Weight Initialization. Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, Lisbon, Portugal.","DOI":"10.1007\/978-3-540-27868-9_92"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., and Yosinski, J. (2017, January 21\u201326). Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.374"},{"key":"ref_80","unstructured":"Yosinski, J. (2024, December 04). How AI Detectives Are Cracking Open the Black Box of Deep Learning, Science, Available online: https:\/\/www.science.org\/content\/article\/how-ai-detectives-are-cracking-open-black-box-deep-learning."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ohzeki, M., Okada, S., Terabe, M., and Taguchi, S. (2018). Optimization of neural networks via finite-value quantum fluctuations. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-28212-4"},{"key":"ref_82","unstructured":"Eilertsen, G., J\u00f6nsson, D., Ropinski, T., Unger, J., and Ynnerman, A. (September, January 29). Classifying the classifier: Dissecting the weight space of neural networks. Proceedings of the European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain."},{"key":"ref_83","unstructured":"Billingsley, P. (1995). Probability and Measure, Wiley. [3rd ed.]."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1146\/annurev-neuro-060909-152832","article-title":"Brain plasticity through the life span: Learning to learn and action video games","volume":"35","author":"Bavelier","year":"2012","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.visres.2004.10.006","article-title":"The Necker cube\u2013an ambiguous figure disambiguated in early visual processing","volume":"45","author":"Kornmeier","year":"2005","journal-title":"Vis. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1143\/PTP.92.501","article-title":"Dynamics of cognitive interpretations of a Necker cube in a chaos neural network","volume":"92","author":"Inoue","year":"1994","journal-title":"Prog. Theor. Phys."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0926-6410(97)00038-4","article-title":"Neural network classifications and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube","volume":"6","author":"Gaetz","year":"1998","journal-title":"Cogn. Brain Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11571-019-09565-6","article-title":"A neural network model for exogenous perceptual alternations of the Necker cube","volume":"14","author":"Araki","year":"2020","journal-title":"Cogn. Neurodyn."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Joos, E., Giersch, A., Hecker, L., Schipp, J., Heinrich, S.P., van Elst, L.T., and Kornmeier, J. (2020). Large EEG amplitude effects are highly similar across Necker cube, smiley, and abstract stimuli. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0232928"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.jmp.2009.12.001","article-title":"A proposed test of temporal nonlocality in bistable perception","volume":"54","author":"Atmanspacher","year":"2010","journal-title":"J. Math. Psychol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"6991067","DOI":"10.1155\/2022\/6991067","article-title":"Numerical solution of Schr\u00f6dinger equation by Crank\u2013Nicolson method","volume":"2022","author":"Khan","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_93","unstructured":"Maksymov, I.S. (2024). Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neunet.2021.05.028","article-title":"A hybrid quantum\u2013classical neural network with deep residual learning","volume":"143","author":"Liang","year":"2021","journal-title":"Neural Netw."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Domingo, L., Djukic, M., Johnson, C., and Borondo, F. (2023). Binding affinity predictions with hybrid quantum-classical convolutional neural networks. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45269-y"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Gircha, A.I., Boev, A.S., Avchaciov, K., Fedichev, P.O., and Fedorov, A.K. (2023). Hybrid quantum-classical machine learning for generative chemistry and drug design. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-32703-4"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"54110","DOI":"10.1109\/ACCESS.2022.3168675","article-title":"Bayesian quantum neural networks","volume":"10","author":"Nguyen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Sakhnenko, A., Sikora, J., and Lorenz, J. (2024, January 19\u201320). Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset. Proceedings of the Recent Advances in Quantum Computing and Technology, Budapest, Hungary. ReAQCT \u201924.","DOI":"10.1145\/3665870.3665872"},{"key":"ref_99","unstructured":"LeCun, Y. (2025, January 04). Security Council Debates Use of Artificial Intelligence in Conflicts, Hears Calls for UN Framework to Avoid Fragmented Governance, Video Presentation, Available online: https:\/\/press.un.org\/en\/2024\/sc15946.doc.htm."},{"key":"ref_100","unstructured":"Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (2007). Numerical Recipes: The Art of Scientific Computing, Cambridge University Press. [3rd ed.]."},{"key":"ref_101","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T. (2018, January 3\u20138). Visualizing the loss landscape of neural nets. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada. NIPS\u201918."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"884033","DOI":"10.3389\/fninf.2022.884033","article-title":"A system-on-chip based hybrid neuromorphic compute node architecture for reproducible hyper-real-time simulations of spiking neural networks","volume":"16","author":"Trensch","year":"2022","journal-title":"Front. Neuroinform."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3390\/dynamics4030033","article-title":"Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective","volume":"4","author":"Abbas","year":"2024","journal-title":"Dynamics"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1103\/PhysRev.109.603","article-title":"New phenomenon in narrow germanium p \u2212 n junctions","volume":"109","author":"Esaki","year":"1958","journal-title":"Phys. Rev."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1002\/j.1538-7305.1967.tb01738.x","article-title":"A floating gate and its application to memory devices","volume":"46","author":"Kahng","year":"1967","journal-title":"Bell Syst. Tech. J."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1063\/1.1655067","article-title":"Resonant tunneling in semiconductor double barriers","volume":"12","author":"Chang","year":"1974","journal-title":"Appl. Phys. Lett."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1038\/nature10679","article-title":"Tunnel field-effect transistors as energy-efficient electronic switches","volume":"479","author":"Ionescu","year":"2011","journal-title":"Nature"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0079-6816(93)90058-4","article-title":"Field emission spectroscopy","volume":"42","author":"Modinos","year":"1993","journal-title":"Prog. Surf. Sci."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"041501","DOI":"10.1063\/5.0160568","article-title":"Scanning probe microscopy in the age of machine learning","volume":"1","author":"Celano","year":"2023","journal-title":"APL Mach. Learn."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1103\/RevModPhys.59.615","article-title":"Scanning tunneling microscopy\u2014From birth to adolescence","volume":"59","author":"Binnig","year":"1987","journal-title":"Rev. Mod. Phys."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"4972","DOI":"10.1109\/TED.2023.3295791","article-title":"Fully flash-based reservoir computing network with low power and rich states","volume":"70","author":"Feng","year":"2023","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"eadg9123","DOI":"10.1126\/sciadv.adg9123","article-title":"Reconfigurable neuromorphic computing block through integration of flash synapse arrays and super-steep neurons","volume":"9","author":"Kwon","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNANO.2013.2263153","article-title":"Image processing by a programmable grid comprising quantum dots and memristors","volume":"12","author":"Yilmaz","year":"2013","journal-title":"IEEE Trans. Nanotechnol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3886","DOI":"10.1038\/s41467-024-48133-3","article-title":"Controlling chaos using edge computing hardware","volume":"15","author":"Kent","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"093709","DOI":"10.1063\/1.5043267","article-title":"Amplifier for scanning tunneling microscopy at MHz frequencies","volume":"89","author":"Bastiaans","year":"2018","journal-title":"Rev. Sci. Instrum."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"150501","DOI":"10.1063\/5.0020014","article-title":"Quantum neuromorphic computing","volume":"117","author":"Grollier","year":"2020","journal-title":"Appl. Phys. Lett."},{"key":"ref_117","unstructured":"van der Made, P. (2022). Learning How to Learn: Neuromorphic AI Inference at the Edge. BrainChip White Pap., 12. Available online: https:\/\/brainchip.com\/learning-how-to-learn-neuromorphic-ai-inference-at-the-edge\/."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/12\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:28:45Z","timestamp":1759919325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,14]]},"references-count":117,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["bdcc9010012"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9010012","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,1,14]]}}}