The DOI matches its Crossref record; 83 of the 84 references it cites were found in a registry or an index.
The quest for a Quantum Neural Network
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“We make the individual neurons reversible and then naturally generalise them to being quantum reversible (unitary).This resolves the classical-quantum clash mentioned above from ref. 24.”
From Quantum generalisation of feedforward neural networks (2017) · cites this paper as [24]
The cited source says what the claim attributes to it.
In the citing paper
“has produced noteworthy advances in quantum random walks, 38,39 quantum control, 40 open quantum systems, 41 and pioneering work on quantum neural networks, 42 quantum machine learning, 43 and quantum processes in the brain.44”
From Toward a quantum future for South Africa (2021) · cites this paper as [42]
In the citing paper
“A key challenge to overcome is the clash between the nonlinear, dissipative dynamics of neural network computing and the linear, reversible dynamics of quantum computing.24”
From Quantum generalisation of feedforward neural networks (2017) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Theory of open quantum systems
Xu, R., & Yan, Y. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1474579 · Cited by 6,622
Introduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410
The Theory of Open Quantum Systems
Breuer, H.-P., & Petruccione, F. (2007) · doi:10.1093/acprof:oso/9780199213900.001.0001 · Cited by 5,938
Self-citation
Research works printed but not linked: 4
- Tóth, G., Lent, C.S., Tougaw, P.D., Brazhnik, Y., Weng, W., Porod, W., Liu, R.-W., Huang, Y.-F.: Quantum cellular neural networks (preprint). cond-mat/0005038 (2000) · arXiv
- Peruš, M.: Neural networks as a basis for quantum associative networks. Neural Netw. World 10(6), 1001–1013 (2000)
- Faber, J., Giraldi, G.A.: Quantum models for artificial neural networks. Electronically available: http://arquivosweb.lncc.br/pdfs/QNN-Review.pdf (2002)
- Siomau, M.: Quantum perceptron (preprint). arXiv:1210.6626 (2012) · arXiv
Cited by
Sorted by citations
Items 301–400 of 683
Quantum Attention for Vision Transformers in High Energy Physics
Tesi, A., Gleyzer, S., Matchev, K. T., Matcheva, K., Kong, K., Dahale, G. R., & Magorsch, T. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15931-1_2 · Cited by 8
Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning
Li, C., Yang, Y., Liang, H. M., & Wu, B. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3158987 · Cited by 8
Chaos in Quantum Weightless Neuron Node Dynamics
Neto, F. M. D. P., de Oliveira, W. R., da Silva, A. J., & Ludermir, T. B. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.103 · Cited by 8
Neural Networks Architecture Evaluation in a Quantum Computer
da Silva, A. J., & de Oliveira, R. L. F. (2017) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2017.33 · Cited by 8
CNOT-Measure Quantum Neural Networks
Lukáč, M., Abdiyeva, K., & Kameyama, M. (2018) · IEEE International Symposium on Multiple-Valued Logic (ISMVL) · doi:10.1109/ismvl.2018.00040 · Cited by 8
Lepcha, D. C., Goyal, B., Dogra, A., & Wang, S. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6644 · Cited by 8
RetractedQML Based Community Detection in the realm of Social Network Analysis
Akbar, S., & Saritha, S. K. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225271 · Cited by 8
Classicalization Clearly: Quantum Transition into States of Maximal Memory Storage CapacityPreprint
Dvali, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06154 · Cited by 17
Learning Simon's quantum algorithmPreprint
Wan, K. H., Liu, F., Dahlsten, O., & Kim, M. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10448 · Cited by 8
Learning to Utilize Correlated Auxiliary Classical or Quantum Noise.Preprint
Ahmadzadegan, A., Simidzija, P., Li, M., & Kempf, A. (2020) · arXiv (Cornell University) · Cited by 8
Quantum Computing in Civil Engineering: Potentials and Limitations
Ploennigs, J., Berger, M., Mevissen, M., & Smarsly, K. (2025) · Lecture Notes in Civil Engineering · doi:10.1007/978-3-031-87364-5_2 · Cited by 7
A Comparative Analysis of Hybrid Quantum Neural Networks in Binary Credit Defaulting Tasks
Sengar, A., Vijayaraghavan, A., & Hwang, J. (2023) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc60662.2023.10535032 · Cited by 7
Explainable Quantum Neural Networks: Example-Based and Feature-Based Methods
Tian, J., & Yang, W. (2024) · Electronics · doi:10.3390/electronics13204136 · Cited by 8
Quantum-Enhanced AI and Machine Learning: Transforming Predictive Analytics
AL-Shammri, F. K., Al-Shareeda, . A., Abbood, A. N. A. J., Almaiah, M. A., & AlAli, R. M. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965377470250225062938 · Cited by 7
Supply Chain Malware Detection via Classical and Quantum Kernel Methods in Embedded Systems
Passo, S. J. G., Kothavade, V. H., & Prevost, J. J. (2025) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w65791.2025.00063 · Cited by 7
Lalawat, R. S., Bajaj, V., Padhy, P. K., & Lin, C.-Y. (2025) · Applied Acoustics · doi:10.1016/j.apacoust.2025.110976 · Cited by 7
Joshi, N., Katyayan, P., & Ahmed, S. A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1854/1/012032 · Cited by 7
Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks
Vallero, M., Dri, E., Giusto, E., Montrucchio, B., & Rech, P. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3372880 · Cited by 8
Coskuner‐Weber, O., Alpsoy, S., Yolcu, O., Teber, E., de Marco, A., & Shumka, S. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108444 · Cited by 7
STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud
Kundu, S., & Ghosh, S. (2025) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/host64725.2025.11050015 · Cited by 7
Quantum steering in tripartite quantum systems
Xiao, S., ZhiHua, G., & Huai-Xin, C. (2018) · Zhongguo kexue. Wulixue Lixue Tianwenxue · doi:10.1360/sspma2018-00277 · Cited by 7
Design of a novel quantum neural network
Niu, X.-F., & Ma, W. (2020) · Laser Physics Letters · doi:10.1088/1612-202x/abaf58 · Cited by 7
Quantum neural networks with multi-qubit potentials
Ban, Y., Torrontegui, E., & Casanova, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-35867-1 · Cited by 11
Storage properties of a quantum perceptron
Gratsea, A., Kasper, V., & Lewenstein, M. (2024) · Physical review. E · doi:10.1103/physreve.110.024127 · Cited by 11
Quantum Neural Network model for Token allocation for Course Bidding
Laokondee, S., & Chongstitvatana, P. (2021) · International Computer Science and Engineering Conference · doi:10.1109/icsec53205.2021.9684650 · Cited by 7
An Efficient and Novel Semi-Quantum Deterministic Secure Quantum Communication Protocol
Zhang, X., & Zhou, R. (2022) · International Journal of Theoretical Physics · doi:10.1007/s10773-022-05092-z · Cited by 7
Toward Physically Realizable Quantum Neural Networks
Heidari, M., Grama, A. Y., & Wojciech Szpankowski, A. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20647 · Cited by 7
Implementing perceptron models with qubits
Wiersema, R., & Kappen, H. J. (2019) · Physical Review A · doi:10.1103/physreva.100.020301 · Cited by 7
Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks
Khalid, M. F., Wu, J., Ali, T. M., Ameen, T., Altaher, A. S., Moustafa, A. A., Zhu, Q., & Xiong, R. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00080 · Cited by 7
Controlled Deterministic Secure Semi-Quantum Communication
Zhou, R., & Zhang, X. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04797-x · Cited by 7
A Quantum Implementation Model for Artificial Neural Networks
Daskin, A. (2018) · Quanta · doi:10.12743/quanta.v7i1.65 · Cited by 7
Quantum speed-up in global optimization of binary neural nets
Liao, Y., Ebler, D., Liu, F., & Dahlsten, O. (2020) · New Journal of Physics · doi:10.1088/1367-2630/abc9ef · Cited by 9
Quantum machine learning and its supremacy in high energy physics
Sharma, K. K. (2020) · Modern Physics Letters A · doi:10.1142/s0217732320300244 · Cited by 7
Defining Quantum Neural Networks via Quantum Time EvolutionPreprint
Dendukuri, A., Keeling, B., Fereidouni, A., Burbridge, J., Luu, K., & Churchill, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10912 · Cited by 16
Hybrid Quantum Algorithms and Quantum Software Development FrameworksPreprint
Ramouthar, R., & Şeker, H. (2023) · doi:10.14293/pr2199.000298.v1 · Cited by 7
Quantum Algorithm for Estimating Betti Numbers Using Cohomology Approach
Nghiem, N. A., Gu, X. D., & Wei, T.-C. (2025) · Quantum · doi:10.22331/q-2025-12-23-1955 · Cited by 6
Satellite image classification using quantum machine learning
Ayoade, O., Rivas, P., Orduz, J., & Rafi, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91737-7.00013-x · Cited by 6
Boneberg, M., Carollo, F., & Lesanovsky, I. (2023) · New Journal of Physics · doi:10.1088/1367-2630/aceff4 · Cited by 7
Quantum-Enhanced Convolutional Neural Networks for Image Classification
Qayyum, T., Tariq, A., Haseeb, M. W., Lakas, A., Serhani, M. A., & Trabelsi, Z. (2023) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf58287.2023.10520509 · Cited by 6
Quantum Graph Neural Network for Resource Management in Wireless Communication
Giang, L. T., Tung, N. X., & Hwang, W. (2025) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic64266.2025.10920694 · Cited by 6
Interactive Quantum Classifier Inspired by Quantum Open System Theory
Zhang, J., Li, Z., He, R., Zhang, J., Wang, B., Li, Z., & Niu, T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533917 · Cited by 7
Quantum density peak clustering
Magano, D., Buffoni, L., & Omar, Y. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00090-0 · Cited by 6
Experimental Demonstration of Quantum Overlapping Tomography
Yang, Z., Ru, S., Cao, L., Zheludev, N. I., & Gao, W. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.050804 · Cited by 8
Noisy Quantum Channel Characterization Using Quantum Neural Networks
Song, J.-Y., Lü, B., Liu, L., & Wang, C. (2023) · Electronics · doi:10.3390/electronics12112430 · Cited by 6
Cui, Y., Shi, J., & Wang, Z. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2614840 · Cited by 6
Quantum arithmetics via computation with minimized external control: The half-adder
Eloie, L., Banchi, L., & Bose, S. (2018) · Physical Review A · doi:10.1103/physreva.97.062321 · Cited by 6
Quantum Hopfield Neural Networks: A New Approach and Its Storage Capacity
Meinhardt, N., Neumann, N. M. P., & Phillipson, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50433-5_44 · Cited by 6
Classifying quantum data by dissipation
Marshall, J., Venuti, L. C., & Paolo Zanardi, A. (2019) · Physical Review A · doi:10.1103/physreva.99.032330 · Cited by 6
Quantum enhanced cross-validation for near-optimal neural networks architecture selection
dos Santos, P. G. M., Sousa, R. S., Araujo, I. C. S., & da Silva, A. J. (2018) · International Journal of Quantum Information · doi:10.1142/s0219749918400051 · Cited by 6
Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms
Ramezanpour, A., Beam, A. L., Chen, J. H., & Mashaghi, A. (2020) · Diagnostics · doi:10.3390/diagnostics10110972 · Cited by 7
Tian, J., & Yang, W. (2024) · Entropy · doi:10.3390/e26110902 · Cited by 6
Tong, Z.-L., Jiang, Y., Min-Huang, Tang, X., Tang, Z.-H., Guo, X.-B., Li, W.-H., & Zhou, Y.-C. (2025) · Physica. B, Condensed matter · doi:10.1016/j.physb.2025.417412 · Cited by 5
Quantum Cardiovascular Medicine: From Hype to Hope—A Critical Review of Real-World Applications
Tomala, M. T., & Kłaczyński, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14176029 · Cited by 5
Chowdhury, C. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500015 · Cited by 5
Detection of brain cancer using quantum-classical CNN based method
Bada, S., Warrier, S. R., & Dontabhaktuni, J. (2023) · Quantum Computing, Communication, and Simulation · doi:10.1117/12.2650132 · Cited by 5
AlMasri, M. W. (2024) · International Journal of Theoretical Physics · doi:10.1007/s10773-024-05569-z · Cited by 5
SoK Paper: Security Concerns in Quantum Machine Learning as a Service
Kundu, S., & Ghosh, S. (2024) · Hardware and Architectural Support for Security and Privacy · doi:10.1145/3696843.3696846 · Cited by 5
Liouvillian skin effect in quantum neural networks
Sannia, A., Giorgi, G. L., Longhi, S., & Zambrini, R. (2025) · Optica Quantum · doi:10.1364/opticaq.541744 · Cited by 5
Exploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization
Hernández-Romero, I. M., Gibford, S. M., Clements, W. R., Savoie, C. J., Flores‐Tlacuahuac, A., & Mansouri, S. S. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c04624 · Cited by 5
Devendrababu, S., Ganguly, S., & Hemachandran, K. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00406-6 · Cited by 7
Solving NP-complete Problems Using Quantum Weightless Neuron Nodes
Neto, F. M. D. P., Ludermir, T. B., de Oliveira, W. R., & da Silva, A. J. (2015) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2015.22 · Cited by 5
Asghar, S. A., Naz, S., & Raja, M. A. Z. (2023) · International Journal of Modern Physics B · doi:10.1142/s021797922350217x · Cited by 5
Exploring Quantum Computing: Potential Applications and Current Challenges in Algorithm Design
Ahmed, A. A., & Hasan, M. K. (2024) · CyberSystem journal. · doi:10.57238/4srf6b63 · Cited by 5
Current Advances in Information Quantum Technologies – Critical Issues
Nałęcz-Charkiewicz, K., Meles, J., Rzesa, W., Wojciechowski, A. A., Warchulski, E., Kania, K., Stypułkowska, J. S., Fluder, G., & Romaniuk, R. S. (2021) · International Journal of Electronics and Telecommunications · doi:10.24425/ijet.2021.137839 · Cited by 5
Single qubit neural quantum circuit for solving Exclusive-OR
Grossu, I. V. (2021) · MethodsX · doi:10.1016/j.mex.2021.101573 · Cited by 5
Ghasemian, E. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00096-2 · Cited by 7
Implementing arbitrary quantum operations via quantum walks on a cycle graph
Lin, J.-Y., Li, X., Shao, Y.-H., Wang, W., & Wu, S. (2023) · Physical Review A · doi:10.1103/physreva.107.042405 · Cited by 5
Ferreira–Martins, A. J., da Silva, L. H. G., de Palhares, A. B., Pereira, R. G., Soares-Pinto, D. O., Chaves, R., & Canabarro, A. (2024) · Physical Review A · doi:10.1103/physreva.109.052623 · Cited by 8
Reservoir induced activation of a quantum neuron
Türkpençe, D. (2020) · Physics Letters A · doi:10.1016/j.physleta.2020.126442 · Cited by 5
Khalid, M. F., Wu, J., Ali, T. M., Ameen, T., Moustafa, A. A., Zhu, Q., & Xiong, R. (2020) · Brain Sciences · doi:10.3390/brainsci10070431 · Cited by 5
Automatic post-selection by ancillae thermalization
Wright, L., Barratt, F., Dborin, J., Booth, G. H., & Green, A. G. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033151 · Cited by 5
Schuld, M., & Petruccione, F. (2021) · Quantum science and technology · doi:10.1007/978-3-030-83098-4_1 · Cited by 5
Self-citationDirect-dynamical Entanglement–Discord relations
Feldman, V., Maziero, J., & Auyuanet, A. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1580-4 · Cited by 5
Quantum Neural Network and Soft Quantum ComputingPreprint
Chen, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05025 · Cited by 5
Quantum information outside quantum information
Pozas-Kerstjens, A. (2019) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-170923 · Cited by 5
Tunable Quantum Neural Networks for Boolean FunctionsPreprint
Ngoc, V. P., & Wiklicky, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14122 · Cited by 5
Experimental quantum memristorPreprint
Spagnolo, M., Morris, J., Piacentini, S., Antesberger, M., Massa, F., Ceccarelli, F., Crespi, A., Osellame, R., & Walther, P. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04867 · Cited by 5
Trainable Discrete Feature Embeddings for Variational Quantum ClassifierPreprint
Thumwanit, N., Lortaraprasert, C., Yano, H., & Raymond, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09415 · Cited by 7
QLSTM4FM: Quantum Assisted Long Short-Term Memory Framework for Financial Market Trend Forecasting
Kundu, S., Tarasia, N., & Barik, R. K. (2024) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit63503.2024.10862667 · Cited by 4
Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction
Brahimi, N., Zhang, H., & Razzaq, Z. (2025) · Sustainability · doi:10.3390/su17114987 · Cited by 4
Frasca, M., Cutica, I., Pravettoni, G., & Torre, D. L. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100264 · Cited by 4
Quantum-enhanced cortical deep echo state network for fast and accurate speech emotion recognition
Soltani, R., Benmohamed, E., & Ltifi, H. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00304-1 · Cited by 4
A repetitive amplitude encoding method for enhancing the mapping ability of quantum neural networks
Li, Z., Fu, X., Meng, L., & Du, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-17651-5 · Cited by 4
Wiest, M. C., & Puniani, A. S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.09.016 · Cited by 7
VQ‐Rice: Integrating Variational Quantum Models for Intelligent Rice Disease Classification
Verma, D. S., Mishra, J. K., Kumar, A., Saudagar, A. K. J., & Mahato, S. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/9911441 · Cited by 4
Akanbi, O. S., Shannon, J. P., Delhommelle, J., & Desgranges, C. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c02390 · Cited by 5
Zúñiga-Galindo, W. A., & Chacón-Cortés, L. F. (2026) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ae9fd4 · Cited by 7
Neurocomputing approach to matrix product state using quantum dynamics
Bhatia, A. S., & Kumar, A. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-2053-0 · Cited by 4
A representação de Kraus para a dinâmica de sistemas quânticos abertos
Maziero, J. (2016) · Revista Brasileira de Ensino de Física · doi:10.1590/1806-9126-rbef-2015-0005 · Cited by 4
Wrobel, N., Baul, A., Tam, K.-M., & Moreno, J. (2022) · Quantum Reports · doi:10.3390/quantum4040042 · Cited by 6
Razzak, S. A., Alkhalaf, O. H., Rahman, S. M., & Zhu, J. (2023) · Chemical Engineering Science · doi:10.1016/j.ces.2023.119310 · Cited by 4
Equilibrium propagation: the quantum and the thermal cases
Massar, S., & Mognetti, B. M. (2024) · Quantum Studies Mathematics and Foundations · doi:10.1007/s40509-024-00351-6 · Cited by 4
Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication
Tabi, Z., Bakó, B., Nagy, D., Vaderna, P., Kallus, Z., Hága, P., & Zimborás, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3566207 · Cited by 4
qlbm – A quantum lattice Boltzmann software framework
Calin, G., Schalkers, M. A., & Möller, M. (2025) · Computer Physics Communications · doi:10.1016/j.cpc.2025.109699 · Cited by 8
Tanbhir, G., Shahriyar, M. F., & Chy, A. M. R. (2025) · International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM) · doi:10.1109/ncim65934.2025.11160275 · Cited by 4
Single-qudit quantum neural networks for multiclass classification
de Souza, L. C., & Portugal, R. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04998-x · Cited by 5
Batched quantum state exponentiation and quantum Hebbian learning
Bromley, T. R., & Rebentrost, P. (2019) · Quantum Machine Intelligence · doi:10.1007/s42484-019-00002-9 · Cited by 5
Qubit Neural Tree Network With Applications in Nonlinear System Modeling
Feng, Q., & Chen, C. (2018) · IEEE Access · doi:10.1109/access.2018.2869894 · Cited by 4
Xue, Y., Wang, X., Cao, J., & Liao, X. (2021) · Artificial Intelligence in Geosciences · doi:10.1016/j.aiig.2022.02.004 · Cited by 4
The role of coherence theory in attractor quantum neural networks
Marconi, C., Saus, P. C., Díaz, M. G., & Sanpera, A. (2022) · Quantum · doi:10.22331/q-2022-09-08-794 · Cited by 4
Metadata
- Authors
- Schuld, M., Sinayskiy, I., & Petruccione, F.
- Affiliations
- University of KwaZulu-Natal (ZA)
- National Institute for Theoretical Physics (ZA)
- Corresponding author
- Schuld, Maria
- Venue
- Quantum Information Processing
- Published
- 2014-08-26
- Volume
- 13
- Issue
- 11
- Pages
- 2567-2586
- ISSN
- 1570-0755 (ISSN-L)
- 1573-1332
- OpenAlex
- W1990514347
- Cited by
- 671
- References
- 84
- Citations by year
- 2026: 83 · 2025: 131 · 2024: 72 · 2023: 80 · 2022: 51 · 2021: 83 · 2020: 49 · 2019: 41 · 2018: 38 · 2017: 17 · 2016: 15 · 2015: 6 · 2014: 5
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Quantum Information Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- quantum neural networks, Hopfield neural network, associative memory, quantum computing, artificial neural networks, nonlinear dynamics, unitary dynamics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
