Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works

Quantum machine learning

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works

Nuclear Induction

Bloch, F. (1946) · Physical Review · doi:10.1103/physrev.70.460 · Cited by 3,681 · see citing works

A learning algorithm for Boltzmann machines

Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402 · see citing works

Quantum Support Vector Machine for Big Data Classification

Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951 · see citing works

Quantum circuit learning

Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018) · Physical Review A · doi:10.1103/physreva.98.032309 · Cited by 1,639 · see citing works

Quantum principal component analysis

Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385 · see citing works

Characterizing quantum supremacy in near-term devices

Boixo, S., Isakov, S. V., Smelyanskiy, V. N., Babbush, R., Ding, N., Jiang, Z., Bremner, M. J., Martinis, J. M., & Neven, H. (2018) · Nature Physics · doi:10.1038/s41567-018-0124-x · Cited by 1,175 · see citing works

Evaluating analytic gradients on quantum hardware

Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., & Killoran, N. (2019) · Physical Review A · doi:10.1103/physreva.99.032331 · Cited by 1,072 · see citing works

Quantum computational supremacy

Harrow, A. W., & Montanaro, A. (2017) · Nature · doi:10.1038/nature23458 · Cited by 823 · see citing works

Effective data generation for imbalanced learning using conditional generative adversarial networks

Douzas, G., & Bação, F. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.030 · Cited by 603 · see citing works

Quantum Generative Adversarial Learning

Lloyd, S., & Weedbrook, C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.040502 · Cited by 576 · see citing works

Read the fine print

Aaronson, S. (2015) · Nature Physics · doi:10.1038/nphys3272 · Cited by 567 · see citing works

Quantum generative adversarial networks

Dallaire-Demers, P.-L., & Killoran, N. (2018) · Physical Review A · doi:10.1103/physreva.98.012324 · Cited by 506 · see citing works

Generative Adversarial Network for Medical Images (MI-GAN)

Iqbal, T., & Ali, H. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1072-9 · Cited by 255 · see citing works

Predicting weather forecast uncertainty with machine learning

Scher, S., & Messori, G. (2018) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3410 · Cited by 225 · see citing works

On the Effectiveness of Least Squares Generative Adversarial Networks

Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2872043 · Cited by 225 · see citing works

How entangled can two couples get?

Higuchi, A., & Sudbery, A. (2000) · Physics Letters A · doi:10.1016/s0375-9601(00)00480-1 · Cited by 189 · see citing works

Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

Romero, J., & Aspuru‐Guzik, A. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202000003 · Cited by 186 · see citing works

Quantum sampling problems, BosonSampling and quantum supremacy

Lund, A. P., Bremner, M. J., & Ralph, T. C. (2017) · npj Quantum Information · doi:10.1038/s41534-017-0018-2 · Cited by 184 · see citing works

Emerging Applications of Machine Learning in Food Safety

Deng, X., Cao, S., & Horn, A. L. (2021) · Annual Review of Food Science and Technology · doi:10.1146/annurev-food-071720-024112 · Cited by 183 · see citing works

Quantum generative adversarial network for generating discrete distribution

Situ, H., He, Z., Wang, Y., Li, L., & Zheng, S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.127 · Cited by 175 · see citing works

Quantum generative adversarial learning in a superconducting quantum circuit

Hu, L., Wu, S.-H., Cai, W., Ma, Y., Mu, X., Xu, Y., Wang, H., Song, Y., Deng, D.-L., Zou, C.-L., et al. (2019) · Science Advances · doi:10.1126/sciadv.aav2761 · Cited by 167 · see citing works

A quantum-inspired genetic algorithm for k-means clustering

Xiao, J., Yan, Y., Zhang, J., et al. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.12.017 · Cited by 122 · see citing works

Eight ways machine learning is assisting medicine

May, M. J. (2021) · Nature Medicine · doi:10.1038/s41591-020-01197-2 · Cited by 110 · see citing works

Dirichlet Variational Autoencoder

Joo, W., Lee, W., Park, S., et al. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107514 · Cited by 102 · see citing works

Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

Rudolph, M. S., Toussaint, N. B., Katabarwa, A., Johri, S., Peropadre, B., & Perdomo-Ortiz, A. (2020) · arXiv · doi:10.48550/arxiv.2012.03924 · Cited by 97 · see citing works

Adversarial quantum circuit learning for pure state approximation

Benedetti, M., Grant, E., Wossnig, L., & Simone Severini, B. (2019) · New Journal of Physics · doi:10.1088/1367-2630/ab14b5 · Cited by 80 · see citing works

MFIF-GAN: A new generative adversarial network for multi-focus image fusion

Wang, Y., Xu, S., Liu, J., Zhao, Z., Zhang, C., & Zhang, J. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116295 · Cited by 73 · see citing works

SmartPaint: a co-creative drawing system based on generative adversarial networks

Sun, L., Chen, P., Xiang, W., Chen, P., Gao, W.-Y., & Zhang, K.-J. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900386 · Cited by 57 · see citing works

Quantum machine learning

Allcock, J., & Zhang, S. (2018) · National Science Review · doi:10.1093/nsr/nwy149 · Cited by 50 · see citing works

Compressive Privacy Generative Adversarial Network

Tseng, B.-W., & Wu, P.-Y. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2968188 · Cited by 48 · see citing works

Improved Boundary Equilibrium Generative Adversarial Networks

Li, Y., Xiao, N., & Ouyang, W. (2018) · IEEE Access · doi:10.1109/access.2018.2804278 · Cited by 43 · see citing works

Universal quantum circuit for n-qubit quantum gate: a programmable quantum gate

de Sousa, P. B. M., & Ramos, R. V. (2007) · Quantum Information and Computation · doi:10.26421/qic7.3-4 · Cited by 34 · see citing works

Optimal Tuning of Quantum Generative Adversarial Networks for Multivariate Distribution Loading

Agliardi, G., & Prati, E. (2022) · Quantum Reports · doi:10.3390/quantum4010006 · Cited by 34 · see citing works

Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

Anand, A., Romero, J., Degroote, M., & Aspuru‐Guzik, A. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202000069 · Cited by 31 · see citing works

A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

Liu, W., Zhang, Y., Deng, Z., Zhao, J., & Tong, L. (2021) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-021-01898-3 · Cited by 24 · see citing works

Supervised deep convolutional generative adversarial networks

Öcal, A., & Özbakır, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.125 · Cited by 22 · see citing works

A Conditional Generative Model Based on Quantum Circuit and Classical Optimization

He, Z., Li, L., Zheng, S., Huang, Z., & Situ, H. (2019) · International Journal of Theoretical Physics · doi:10.1007/s10773-019-04005-x · Cited by 12 · see citing works

Deep Learning for Visual Content Analysis

Iosifidis, A., & Tefas, A. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115806 · Cited by 2 · see citing works

From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works

Machine Learning Could be Easier if All Data Were MNIST

Hayashi, T., Cimr, D., & Cimler, R. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230234

Training on test data: Removing near duplicates in Fashion-MNISTPreprint

Geier, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08255 · Cited by 3 · see citing works

Symmetric Rectified Linear Units for Fully Connected Deep Models

Hu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99247-1_26 · Cited by 4 · see citing works

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108 · see citing works

РЕЗУЛЬТАТИ НАЛАШТУВАННЯ ПАРАМЕТРІВ НЕЙРОННИХ ГЛИБОКИХ МЕРЕЖ ЩОДО РОЗПІЗНАВАННЯ FASHION MNIST DATASET

Hnatushenko, V., Fenenko, T., & ДОРОШ, О. Л. (2023) · Applied Questions of Mathematical Modeling · doi:10.32782/mathematical-modelling/2022-5-2-2