The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Quantum Support Vector Machine for Big Data Classification
Open accessAbstract
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.
Overview
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
“Exponential speedup has, for instance, shown to be possible for quantum support vector machines [110]”
From Neutral atom quantum computing hardware: performance and end-user perspective (2023) · cites this paper as [110]
In the citing paper
“First, by more sophisticated Trotter-Suzuki decompositions 261 , the approximation error can be made to depend on progressively-higher orders in τ 196,260,261 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [261]
In the citing paper
“If |χ is some other vector, then the method provides a method for supervised learning and cluster assignment 9,10,21,28 :”
From Quantum principal component analysis (2014) · cites this paper as [28]
In the citing paper
“Note that this expressivity boost is not limited to neural networks and has been observed, for instance, for support vector machines.[222,228,229]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [222]
In the citing paper
“The first advantage has been leveraged in deriving the quantum realization of the principal components analysis algorithm [221] and of a support vector machine.[222]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [222]
In the citing paper
“Rebentrost et al. [75] presented a quantum algorithm that can train a SVM with a polynomial kernel in O log N ð Þ, and this was later extended to the radial basis function (RBF) kernel [76].”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [75]
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Least Squares Support Vector Machine Classifiers
Suykens, J., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,481
(2012) · Cited by 9,062
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
ASH, R. B. (1966) · Cited by 3,990
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,521
Flagged first, then by citations
Research works printed but not linked: 3
- S. Aaronson, 2010, Proceedings of the 42nd ACM Symposium on Theory of Computing, Cambridge, MA, 2010
- C. Ding, 2004, Proceedings of the 21st International Conference on Machine Learning, Banff, AB, Canada, 2004
- Z. Bar-Yossef, 2003, Proceedings of the 35th Annual ACM Symposium on Theory of Computing, San Diego, 2003
Cited by
Sorted by citations
Items 701–800 of 1,001
Quantum algorithm for MMNG-based DBSCAN
Xie, X.-M., Duan, L., Qiu, T., & Li, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-95156-7 · Cited by 15
QDNN: DNN with Quantum Neural Network LayersPreprint
Zhao, C., & Gao, X.-S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12660 · Cited by 15
QuEst: Graph Transformer for Quantum Circuit Reliability EstimationPreprint
Wang, H., Liu, P., Cheng, J., Liang, Z., Gu, J., Li, Z., Ding, Y., Jiang, W., Shi, Y., Qian, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.16724 · Cited by 36
Support Vector Machines on Noisy Intermediate Scale Quantum ComputersPreprint
Yang, J., Awan, A. J., & Vall-llosera, G. (2019) · arXiv (Cornell University) · doi:10.13140/rg.2.2.17956.63360 · Cited by 15
Image Denoising Using Quantum Deep Convolutional Generative Adversarial Network for Medical Images
Nandal, P., Pahal, S., & Upadhyay, G. M. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00920-6 · Cited by 14
Qu, Z., Liu, X., & Sun, L. (2022) · Physical Review A · doi:10.1103/physreva.105.052427 · Cited by 14
Quantum-Enhanced Support Vector Classifier for Image Classification
Golchha, R., & Verma, G. K. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126421 · Cited by 14
EEG-Based Emotion Recognition Using Quantum Machine Learning
Garg, D., Verma, G. K., & Singh, A. K. (2023) · SN Computer Science · doi:10.1007/s42979-023-01943-6 · Cited by 16
A variational quantum perceptron with Grover’s algorithm for efficient classification
Innan, N., & Bennai, M. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad3e38 · Cited by 20
Pimentel, J. S., Ospina, R., & Ara, A. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100457 · Cited by 14
Quantum Fisher kernel for mitigating the vanishing similarity issue
Suzuki, Y., Kawaguchi, H., & Yamamoto, N. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad4b97 · Cited by 17
Muser, T., Zapusek, E., Belis, V., & Reiter, F. (2024) · Physical Review A · doi:10.1103/physreva.110.032434 · Cited by 18
Liao, Q., Fei, Z., Liu, J., Huang, A., Huang, L., & Wang, Y. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116331 · Cited by 14
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition
Chen, K., Ma, W., & Xu, X. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw65056.2025.11011036 · Cited by 14
A quantum-enhanced ensemble model to forecast battery state of health under varying discharging load
Soon, K. L., Lai, N. S., Chow, C., Kanesan, J., Yen, K. S., & Soon, L. T. (2025) · Measurement · doi:10.1016/j.measurement.2025.118318 · Cited by 16
Quantum diffusion map for nonlinear dimensionality reduction
Sornsaeng, A., Dangniam, N., Palittapongarnpim, P., & Chotibut, T. (2021) · Physical Review A · doi:10.1103/physreva.104.052410 · Cited by 13
Learning bounds for quantum circuits in the agnostic setting
Popescu, C. M. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03225-7 · Cited by 13
Experimental Implementation of Quantum Algorithm for Association Rules Mining
Yu, C. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3201097 · Cited by 15
Quantum Computing and Quantum Machine Learning Classification – A Survey
Kuppusamy, P., Kumar, N. Y., Dontireddy, J., & Iwendi, C. (2022) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla56841.2022.9989137 · Cited by 14
Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training
Phalak, K., & Ghosh, S. (2023) · IEEE Access · doi:10.1109/access.2023.3270419 · Cited by 14
Osaba, E., Onieva, E., Moreno, A., López-García, P., Perallos, A., & Bringas, P. G. (2016) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2016.0047 · Cited by 14
Quantum expectation-maximization algorithm
Miyahara, H., Aihara, K., & Lechner, W. (2020) · Physical Review A · doi:10.1103/physreva.101.012326 · Cited by 16
Quantum correlation alignment for unsupervised domain adaptation
He, X. (2020) · Physical Review A · doi:10.1103/physreva.102.032410 · Cited by 15
Quantum DeepONet: Neural operators accelerated by quantum computing
Xiao, P., Zheng, M., Jiao, A., Xiu, Y., & Lu, L. (2025) · Quantum · doi:10.22331/q-2025-06-04-1761 · Cited by 14
Sawaika, A., Krishna, S., Tomar, T., Suggisetti, D. P., Lal, A., Shrivastav, T., Innan, N., & Shafique, M. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00175 · Cited by 17
Sukulthanasorn, N., & Terada, K. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.118411 · Cited by 13
State stabilization for gate-model quantum computers
Gyöngyösi, L., & Imre, S. (2019) · Quantum Information Processing · doi:10.1007/s11128-019-2397-0 · Cited by 13
Yang, Z., He, Y., Xiang, Q., Zio, E., He, L., Luo, X., Su, H., Wang, J., & Zhang, J. (2022) · Petroleum Science · doi:10.1016/j.petsci.2022.09.005 · Cited by 14
Harrow-Hassidim-Lloyd algorithm without ancilla postselection
Валерьевич, Б. Д. (2023) · Physical Review A · doi:10.1103/physreva.107.042408 · Cited by 13
Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets
John, M., Schuhmacher, J., Barkoutsos, P. K., Tavernelli, I., & Tacchino, F. (2023) · Entropy · doi:10.3390/e25060860 · Cited by 15
A survey on quantum data mining algorithms: challenges, advances and future directions
Qi, H., Wang, L., Gong, C., & Gani, A. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04279-z · Cited by 14
Solving the vehicle routing problem via quantum support vector machines
Mohanty, N., Behera, B. K., & Ferrie, C. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00161-4 · Cited by 13
Blind Quantum Machine Learning with Quantum Bipartite Correlator
Li, C., Li, B., Amer, O., Shaydulin, R., Chakrabarti, S., Wang, G., Xu, H., Tang, H., Schoch, I., Kumar, N., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.120602 · Cited by 13
Federated quantum machine learning for drug discovery and healthcare
Saggi, M. K., Bhatia, A. S., & Kais, S. (2024) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2024.10.007 · Cited by 13
The role of data embedding in equivariant quantum convolutional neural networks
Das, S., Martina, S., & Caruso, F. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00215-7 · Cited by 15
Choi, J., Hur, T., Park, D. K., Shin, N., Lee, S., Lee, H., & Han, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112922 · Cited by 14
A survey on quantum deep learning
Wu, H., Zhang, J., Wang, L., Li, D., Kong, D., & Han, Y. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07083-3 · Cited by 14
Quantum computing for space applications: a selective review and perspectives
Torta, P., Casati, R., Bruni, S., Mandarino, A., & Prati, E. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00369-8 · Cited by 16
A quantum active learning algorithm for sampling against adversarial attacks
Casares, P. A. M., & Martin-Delgado, M. A. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ab976f · Cited by 13
Computing Eigenvalues of Diagonalizable Matrices on a Quantum Computer
Shao, C. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3527845 · Cited by 16
A quantum active learning algorithm for sampling against adversarial attacks
Casares, P. A. M., & Martin-Delgado, M. A. (2020) · Docta Complutense (Universidad Complutense de Madrid) · Cited by 13
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
Phillipson, F., Wezeman, R. S., & Chiscop, I. (2021) · Computers · doi:10.3390/computers10060071 · Cited by 13
Blind quantum machine learning based on quantum circuit model
Zhou, X., & Qiu, D. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03301-y · Cited by 13
Discriminating Quantum States with Quantum Machine Learning
Quiroga, D., Date, P., & Pooser, R. C. (2021) · IEEE International Conference on Rebooting Computing (ICRC) · doi:10.1109/icrc53822.2021.00018 · Cited by 13
An efficient quantum algorithm for spectral estimation
Steffens, A., Rebentrost, P., Marvian, I., Eisert, J., & Lloyd, S. (2017) · New Journal of Physics · doi:10.1088/1367-2630/aa5e48 · Cited by 14
Self-citationRecognizing the pattern of binary Hermitian matrices by quantum kNN and SVM methods
Wiśniewska, J., & Sawerwain, M. (2018) · Vietnam Journal of Computer Science · doi:10.1007/s40595-018-0115-y · Cited by 14
Quantum mean embedding of probability distributions
Kübler, J. M., Muandet, K., & Schölkopf, B. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033159 · Cited by 18
Quantum Boltzmann machine algorithm with dimension-expanded equivalent Hamiltonian
Xiao, T., Bai, D., Jianping Fan, B., & Zeng, G. (2020) · Physical Review A · doi:10.1103/physreva.101.032304 · Cited by 15
Benlamine, K., Bennani, Y., Grozavu, N., & Mateï, B. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207334 · Cited by 13
An efficient quantum algorithm for spectral estimation
Eisert, J., Steffens, A., Rebentrost, F., Marvian, I., & Lloyd, S. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 13
Self-citationRevisiting dequantization and quantum advantage in learning tasksPreprint
Cotler, J. S., Huang, H.-Y., & McClean, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00811 · Cited by 54
Quantum Support Vector Machine for Prostate Cancer Detection: A Performance AnalysisPreprint
Maouaki, W. E., Said, T., & Bennai, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3867096/v1 · Cited by 17
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 27
Deterministic and Entanglement-Efficient Preparation of Amplitude-Encoded Quantum Registers
Gundlapalli, P., & Lee, J. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.024013 · Cited by 15
quEEGNet: Quantum AI for Biosignal Processing
Koike–Akino, T., & Wang, Y. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926814 · Cited by 12
A multi-classification classifier based on variational quantum computation
Zhou, J., Li, D., Tan, Y., Yang, X., Zheng, Y., & Liu, X. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-04151-6 · Cited by 14
Shallow quantum neural networks (SQNNs) with application to crack identification
Das, M., Naskar, A., Mitra, P., & Basu, B. (2024) · Applied Intelligence · doi:10.1007/s10489-023-05192-1 · Cited by 12
Conglomeration of deep neural network and quantum learning for object detection: Status quo review
Sinha, P. K., & Marimuthu, R. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111480 · Cited by 14
Chen, Q., Du, Y., Jiao, Y., Lu, X., Wu, X., & Zhao, Q. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad420a · Cited by 19
Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats
Rivas, P., Orduz, J., Jui, T. D., DeCusatis, C. M., & Khanal, B. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020044 · Cited by 12
Quantum support vector data description for anomaly detection
Oh, H., & Park, D. K. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad6be8 · Cited by 19
Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
Jobst, B., Shen, K., Riofrío, C. A., Shishenina, E., & Pollmann, F. (2024) · Quantum · doi:10.22331/q-2024-12-03-1544 · Cited by 21
Reinforcement learning-based architecture search for quantum machine learning
Rapp, F., Kreplin, D. A., Huber, M. F., & Roth, M. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adaf75 · Cited by 12
Enhancing quantum support vector machine for healthcare applications using custom feature maps
Bansal, R., Rajput, N. K., & Khanna, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113669 · Cited by 12
Experimental demonstration of quantum learning speedup with classical input data
Lee, J.-S., Bang, J., Hong, S., Lee, C., Seol, K. H., Lee, J., & Lee, K.-G. (2019) · Physical Review A · doi:10.1103/physreva.99.012313 · Cited by 12
Qiao, M., Li, C., Suo, S., Cheng, F., Hua, J., Xue, D., Guo, Y., Xu, J., & Wang, Y. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02177-0 · Cited by 12
Jove, E., Casteleiro‐Roca, J., Vara, R. C., Quintián, H., Pérez, J. A. M., Mohamad, M. S., & ROLLE, J. L. C. (2020) · Cybernetics & Systems · doi:10.1080/01969722.2020.1798641 · Cited by 12
Leira, A., Jove, E., González-Cava, J. M., Casteleiro‐Roca, J., Quintián, H., Gato, F. Z., Torres, S., Simić, S., Pérez, J. A. M., & ROLLE, J. L. C. (2020) · Logic Journal of IGPL · doi:10.1093/jigpal/jzaa065 · Cited by 12
An intelligent system for harmonic distortions detection in wind generator power electronic devices
Jove, E., González-Cava, J. M., Casteleiro‐Roca, J., Aláiz‐Moretón, H., Baruque, B., Leitão, P., Pérez, J. A. M., & ROLLE, J. L. C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.07.155 · Cited by 12
Fake News Detection: An Application of Quantum K-Nearest Neighbors
Tian, Z., & Baskiyar, S. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659944 · Cited by 12
Quantum Gaussian process model of potential energy surface for a polyatomic molecule
Dai, J. F., & Krems, R. V. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0088821 · Cited by 16
Kernel-based quantum regressor models learning non-Markovianity
Tancara, D., Dinani, H. T., Norambuena, A., Fanchini, F. F., & Coto, R. (2023) · Physical Review A · doi:10.1103/physreva.107.022402 · Cited by 13
Communication-Efficient Quantum Algorithm for Distributed Machine Learning
Tang, H., Li, B., Wang, G., Xu, H., Li, C., Barr, A., Cappellaro, P., & Li, J. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.150602 · Cited by 16
P, S. S., & Divya, S. (2023) · International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS) · doi:10.1109/iq-cchess56596.2023.10391714 · Cited by 12
Quantum simulation of a quantum stochastic walk
Govia, L. C. G., Taketani, B. G., Schuhmacher, P. K., & Wilhelm, F. K. (2016) · Quantum Science and Technology · doi:10.1088/2058-9565/aa540b · Cited by 14
A Survey of Machine Learning Methods for Big Data
Ruiz–Chavez, Z., Salvador–Meneses, J., & García‐Rodríguez, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59773-7_27 · Cited by 12
Quantum Implementation of Fuzzy Systems through Grover’s Algorithm
Acampora, G., Luongo, F., & Vitiello, A. (2018) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2018.8491579 · Cited by 12
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
Near-term quantum-classical associative adversarial networks
Anschuetz, E. R., & Zanoci, C. (2019) · Physical Review A · doi:10.1103/physreva.100.052327 · Cited by 13
Quantum-enhanced learning of rotations about an unknown direction
Mo, Y., & Chiribella, G. (2019) · New Journal of Physics · doi:10.1088/1367-2630/ab4d9a · Cited by 12
Discriminative Fast Hierarchical Learning for Multiclass Image Classification
Zheng, Y., Jianping Fan, B., Zhang, J., & Gao, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2948881 · Cited by 12
Quantum semi-supervised kernel learning
Saeedi, S., Panahi, A., & Arodz, T. J. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00053-x · Cited by 12
Literature review and analysis on big data stream classification techniques
Srivani, B., Sandhya, N., & Rani, B. P. (2020) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-200042 · Cited by 12
Zhang, K., Hsieh, M.-H., Liu, L., & Tao, D. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043095 · Cited by 14
Bhattacharyya, S., Pan, I., Mani, A., De, S., Behrman, E., Chakrabarti, S., & Behrman, E. (2020) · De Gruyter eBooks · doi:10.1515/9783110670707 · Cited by 12
Efficient implementation of unitary transformationsPreprint
Lloyd, S., & Maity, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03431 · Cited by 12
Self-citationSharma, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10453 · Cited by 12
Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital TwinPreprint
Otgonbaatar, S., Nurmi, O., Johansson, M. P., Mäkelä, J., kocman, T., Gawron, P., Puchała, Z., Mielzcarek, J., Miroszewski, A., & Dumitru, C. O. (2023) · doi:10.36227/techrxiv.24478663.v1 · Cited by 17
Quantum computing and supervised machine learning
Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11
Mixed-state quantum denoising diffusion probabilistic model
Kwun, G., Zhang, B., & Zhuang, Q. (2025) · Physical Review A · doi:10.1103/physreva.111.032610 · Cited by 11
Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms
Li, T.-Y., Mekala, V. R., Ng, K., & Su, C.-F. (2022) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe55377.2022.00078 · Cited by 11
Padha, A., & Sahoo, A. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00108-1 · Cited by 12
A Multi‐Class Quantum Kernel‐Based Classifier
Pillay, S. M., Sinayskiy, I., Jembere, E., & Petruccione, F. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300249 · Cited by 11
Evaluating the Impact of Noise on Variational Quantum Circuits in NISQ Era Devices
Khanal, B., & Rivas, P. (2023) · Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE) · doi:10.1109/csce60160.2023.00272 · Cited by 11
Quantum computing challenges and solutions in software industry—A multivocal literature review
Salam, M., & Ilyas, M. (2024) · IET Quantum Communication · doi:10.1049/qtc2.12096 · Cited by 11
Hybrid genetic optimization for quantum feature map design
Pellow-Jarman, R., Pillay, A. W., Sinayskiy, I., & Petruccione, F. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00177-w · Cited by 11
Toward optimal circuit size for sparse quantum state preparation
Mao, R., Tian, G., & Sun, X. (2024) · Physical Review A · doi:10.1103/physreva.110.032439 · Cited by 12
PQKELP: Projected Quantum Kernel Embedding based Link Prediction in dynamic networks
Kumar, M., Singh, N., & Biswas, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125944 · Cited by 12
Kwon, S., Huh, J., Kwon, S. J., Choi, S.-H., & Kwon, O. (2025) · Symmetry · doi:10.3390/sym17020186 · Cited by 12
BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting
Su, L., Li, D., & Qiu, D. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05348-z · Cited by 15
Metadata
- Authors
- Rebentrost, P., Mohseni, M., & Lloyd, S.
- Affiliations
- Massachusetts Institute of Technology (US)
- Google (United States) (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2014-09-25
- Volume
- 113
- Issue
- 13
- Pages
- 130503-130503
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2103956991
- Cited by
- 1,951
- References
- 30
- Citations by year
- 2026: 229 · 2025: 306 · 2024: 274 · 2023: 277 · 2022: 205 · 2021: 208 · 2020: 151 · 2019: 105 · 2018: 77 · 2017: 54 · 2016: 45 · 2015: 13 · 2014: 7
- FWCI
- 39.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (DARPA)
- National Science Foundation (NSF)
- Eni
- Air Force Office of Scientific Research (AFOSR)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum support vector machine, quantum machine learning, big data classification, binary classification, quantum advantage, matrix inversion, kernel matrix, quantum computing
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
