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
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
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Self-citationSupervised learning with quantum-enhanced feature spaces
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019) · Nature · doi:10.1038/s41586-019-0980-2 · Cited by 2,572
A quantum engineer's guide to superconducting qubits
Krantz, P., Kjærgaard, M., Yan, F., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2019) · Applied Physics Reviews · doi:10.1063/1.5089550 · Cited by 2,321
Quantum Machine Learning in Feature Hilbert Spaces
Schuld, M., & Killoran, N. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.040504 · Cited by 1,840
Big data in healthcare: management, analysis and future prospects
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0217-0 · Cited by 1,747
Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018) · Physical Review A · doi:10.1103/physreva.98.032309 · Cited by 1,639
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560
Albash, T., & Lidar, D. A. (2018) · Reviews of Modern Physics · doi:10.1103/revmodphys.90.015002 · Cited by 1,546
Quantum principal component analysis
Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385
Self-citationAn introduction to quantum machine learning
Schuld, M., Sinayskiy, I., & Petruccione, F. (2015) · Contemporary Physics · doi:10.1080/00107514.2014.964942 · Cited by 1,381
Parameterized quantum circuits as machine learning models
Benedetti, M., Lloyd, E., Sack, S., & Fiorentini, M. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/ab4eb5 · Cited by 1,494
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Dunjko, V., & Briegel, H. J. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aab406 · Cited by 1,214
Circuit-centric quantum classifiers
Schuld, M., Bocharov, A., Svore, K. M., & Wiebe, N. (2020) · Physical Review A · doi:10.1103/physreva.101.032308 · Cited by 1,057
Quantum information processing with superconducting circuits: a review
Wendin, G. P. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa7e1a · Cited by 1,021
Power of data in quantum machine learning
Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., & McClean, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-22539-9 · Cited by 896
Self-citationTsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Schuld, M., Sweke, R., & Meyer, J. J. (2021) · Physical Review A · doi:10.1103/physreva.103.032430 · Cited by 739
Data re-uploading for a universal quantum classifier
Pérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., & Latorre, J. I. (2020) · Quantum · doi:10.22331/q-2020-02-06-226 · Cited by 713
Challenges and opportunities in quantum machine learning
Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, Ł., & Coles, P. J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00311-3 · Cited by 862
Quantum computing for finance: Overview and prospects
Orús, R., Mugel, S., & Lizaso, E. (2019) · Reviews in Physics · doi:10.1016/j.revip.2019.100028 · Cited by 783
Classification with Quantum Neural Networks on Near Term Processors
Farhi, E., & Neven, H. (2020) · doi:10.37686/qrl.v1i2.80 · Cited by 1,176
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658
A Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
A rigorous and robust quantum speed-up in supervised machine learning
Liu, Y., Arunachalam, S., & Temme, K. (2021) · Nature Physics · doi:10.1038/s41567-021-01287-z · Cited by 649
Aaronson, S. (2015) · Nature Physics · doi:10.1038/nphys3272 · Cited by 567
Hybrid Quantum-Classical Algorithms and Quantum Error Mitigation
Endo, S., Cai, Z., Benjamin, S. C., & Yuan, X. (2021) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.90.032001 · Cited by 566
Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B., & Melko, R. (2018) · Physical Review X · doi:10.1103/physrevx.8.021050 · Cited by 520
Quantum machine learning: a classical perspective
Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., & Wossnig, L. (2018) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2017.0551 · Cited by 518
Quantum-Enhanced Machine Learning
Dunjko, V., Taylor, J. M., & Briegel, H. J. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.130501 · Cited by 460
An introduction to quantum machine learningPreprint
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Cited by 435
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033063 · Cited by 418
Self-citationSuperconducting quantum computing: a review
Huang, H.-L., Wu, D., Fan, D., & Zhu, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2881-9 · Cited by 412
Quantum convolutional neural network for classical data classification
Hur, T., Kim, L., & Park, D. K. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00061-x · Cited by 448
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 358
Self-citationPotential of quantum computing for drug discovery
Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353
Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning
Fujii, K., & Nakajima, K. (2017) · Physical Review Applied · doi:10.1103/physrevapplied.8.024030 · Cited by 337
Hierarchical quantum classifiers
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V., Green, A. G., & Simone Severini, B. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0116-9 · Cited by 387
Expressive power of parametrized quantum circuits
Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033125 · Cited by 318
Experimental Quantum Generative Adversarial Networks for Image Generation
Huang, H.-L., Du, Y., Gong, M., Zhao, Y., Wu, Y., Wang, C., Li, S., Liang, F., Lin, J., Xu, Y., et al. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.024051 · Cited by 336
Hierarchical quantum classifiers
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V, Green, A. G., & Simone Severini, B. (2018) · UCL Discovery (University College London) · Cited by 296
Herman, D., Googin, C., Liu, X., Sun, Y., Galda, A., Safro, I., Pistoia, M., & Alexeev, Y. (2023) · Nature Reviews Physics · doi:10.1038/s42254-023-00603-1 · Cited by 361
Hybrid quantum-classical convolutional neural networks
Liu, J., Lim, K. H., Wood, K. L., Huang, W., Guo, C., & Huang, H.-L. (2021) · Science China Physics, Mechanics & Astronomy · doi:10.1007/s11433-021-1734-3 · Cited by 289
Implementing a distance-based classifier with a quantum interference circuit
Schuld, M., Fingerhuth, M., & Petruccione, F. (2017) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/119/60002 · Cited by 285
Learning the quantum algorithm for state overlap
Cincio, L., Subaşı, Y., Sornborger, A. T., & Coles, P. J. (2018) · New Journal of Physics · doi:10.1088/1367-2630/aae94a · Cited by 282
Prediction by linear regression on a quantum computer
Schuld, M., Sinayskiy, I., & Petruccione, F. (2016) · Physical Review A · doi:10.1103/physreva.94.022342 · Cited by 254
Quantum generalisation of feedforward neural networks
Wan, K. H., Dahlsten, O., Kristjánsson, H., Gardner, R., & Kim, M. S. (2017) · npj Quantum Information · doi:10.1038/s41534-017-0032-4 · Cited by 276
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274
Simulating Large Quantum Circuits on a Small Quantum Computer
Peng, T., Harrow, A. W., Ozols, M., & Wu, X. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.150504 · Cited by 317
Experimental fault-tolerant universal quantum gates with solid-state spins under ambient conditions
Rong, X., Geng, J., Shi, F., Liu, Y., Xu, K., Ma, W., Kong, F., Jiang, Z. L., Wu, Y., & Du, J. (2015) · Nature Communications · doi:10.1038/ncomms9748 · Cited by 263
Experimental Realization of Quantum Artificial Intelligence
Zhaokai, L., Xiaomei, L., Nanyang, X., & jiangfeng, D. (2014) · arXiv · doi:10.48550/arxiv.1410.1054 · Cited by 261
Experimental Realization of a Quantum Support Vector Machine
Li, Z., Liu, X., Xu, N., & Du, J. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.140504 · Cited by 269
A Survey of Important Issues in Quantum Computing and Communications
Yang, Z., Zolanvari, M., & Jain, R. K. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3254481 · Cited by 273
Information-Theoretic Bounds on Quantum Advantage in Machine Learning
Huang, H.-Y., Kueng, R., & Preskill, J. P. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.190505 · Cited by 307
Quantum Algorithm Implementations for Beginners
Jayakumar, A., Adedoyin, A. A., Ambrosiano, J. J., Anisimov, P. M., Casper, W., Chennupati, G., Coffrin, C., Djidjev, H. N., Gunter, D., Karra, S., et al. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3517340 · Cited by 299
Distributed secure quantum machine learning
Sheng, Y.-B., & Zhou, L. (2017) · Science Bulletin · doi:10.1016/j.scib.2017.06.007 · Cited by 245
Zhang, Z., & Zhuang, Q. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abd4c3 · Cited by 277
Quantum Models as Kernel Methods
Schuld, M., & Petruccione, F. (2021) · Quantum science and technology · doi:10.1007/978-3-030-83098-4_6 · Cited by 260
Quantum algorithms for topological and geometric analysis of data
Lloyd, S., Garnerone, S., & Zanardi, P. (2016) · Nature Communications · doi:10.1038/ncomms10138 · Cited by 241
Self-citationAutomated Search for new Quantum Experiments
Krenn, M., Malik, M., Fickler, R., Łapkiewicz, R., & Zeilinger, A. (2016) · Physical Review Letters · doi:10.1103/physrevlett.116.090405 · Cited by 234
Variational Quantum Simulation of General Processes
Endo, S., Sun, J., Li, Y., Benjamin, S. C., & Yuan, X. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.010501 · Cited by 237
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232
Cichocki, A. S., Phan, A.-H., Zhao, Q., Lee, N., Oseledets, I. V., Sugiyama, M., & Mandic, D. P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000067 · Cited by 243
Quantum Linear System Algorithm for Dense Matrices
Wossnig, L., Zhao, Z., & Prakash, A. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.050502 · Cited by 224
A quantum deep convolutional neural network for image recognition
Li, Y., Zhou, R.-G., Xu, R., Luo, J., & Hu, W. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab9f93 · Cited by 225
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A. (2016) · Physical Review A · doi:10.1103/physreva.94.022308 · Cited by 215
Is Quantum Advantage the Right Goal for Quantum Machine Learning?
Schuld, M., & Killoran, N. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.030101 · Cited by 267
Machine Learning: Quantum vs Classical
Khan, T. M., & Robles‐Kelly, A. (2020) · IEEE Access · doi:10.1109/access.2020.3041719 · Cited by 238
Solving a Higgs optimization problem with quantum annealing for machine learning
Mott, A., Job, J., Vlimant, J.-R., Lidar, D. A., & Spiropulu, M. (2017) · Nature · doi:10.1038/nature24047 · Cited by 210
Varatharajan, R., Manogaran, G., & Kumar, P. M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5318-1 · Cited by 208
RetractedQuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
Wang, H., Ding, Y., Gu, J., Lin, Y., Pan, D. Z., Chong, F. T., & Han, S. (2022) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca53966.2022.00057 · Cited by 235
Quantum Machine Learning: A Review and Case Studies
Zeguendry, A., Jarir, Z., & Quafafou, M. (2023) · Entropy · doi:10.3390/e25020287 · Cited by 246
Quantum arithmetic with the quantum Fourier transform
Ruiz, L., & García-Escartín, J. C. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1603-1 · Cited by 206
Li, R. Y., Di Felice, R., Rohs, R., & Lidar, D. A. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0060-8 · Cited by 202
Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
Yao, X., Wang, H., Liao, Z., Chen, M.-C., Pan, J., Li, J., Zhang, K., Lin, X., Wang, ., Luo, Z., et al. (2017) · Physical Review X · doi:10.1103/physrevx.7.031041 · Cited by 200
Quantum classifier with tailored quantum kernel
Blank, C., Park, D. K., Rhee, J. K., & Petruccione, F. (2020) · npj Quantum Information · doi:10.1038/s41534-020-0272-6 · Cited by 203
0.5 petabyte simulation of a 45-qubit quantum circuit
Häner, T., & Steiger, D. S. (2017) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3126908.3126947 · Cited by 213
Quantum-inspired support vector machine
Ding, C., Bao, T.-Y., & Huang, H.-L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084467 · Cited by 186
Solving nonlinear differential equations with differentiable quantum circuits
Kyriienko, O., Paine, A. E., & Elfving, V. E. (2021) · Physical Review A · doi:10.1103/physreva.103.052416 · Cited by 185
Quantum Hopfield neural network
Rebentrost, P., Bromley, T. R., Weedbrook, C., & Lloyd, S. (2018) · Physical Review A · doi:10.1103/physreva.98.042308 · Cited by 187
Self-citationQuantum gradient descent for linear systems and least squares
Kerenidis, I., & Prakash, A. (2020) · Physical Review A · doi:10.1103/physreva.101.022316 · Cited by 177
Support vector machines on the D-Wave quantum annealer
Willsch, D., Willsch, M., Raedt, H. A. D., & Michielsen, K. F. L. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.107006 · Cited by 195
Quantum Machine Learning Algorithms for Drug Discovery Applications
Batra, K., Zorn, K. M., Foil, D. H., Minerali, E., Gawriljuk, V. O., Lane, T. R., & Ekins, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00166 · Cited by 184
NISQ computing: where are we and where do we go?
Lau, J. W. Z., Lim, K. H., Shrotriya, H., & Kwek, L. C. (2022) · AAPPS bulletin · doi:10.1007/s43673-022-00058-z · Cited by 198
Supervised quantum machine learning models are kernel methods
Schuld, M. (2021) · arXiv · doi:10.48550/arxiv.2101.11020 · Cited by 173
A software methodology for compiling quantum programs
Häner, T., Steiger, D. S., Svore, K. M., & Troyer, M. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aaa5cc · Cited by 171
Variational algorithms for linear algebra
Xu, X., Sun, J., Endo, S., Li, Y., Benjamin, S. C., & Yuan, X. (2021) · 中国科学通报：英文版 · doi:10.1016/j.scib.2021.06.023 · Cited by 195
Tomography and generative training with quantum Boltzmann machines
Kieferová, M., & Wiebe, N. (2017) · Physical Review A · doi:10.1103/physreva.96.062327 · Cited by 170
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 190
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162
Quantum machine learning for electronic structure calculations
Xia, R., & Kais, S. (2018) · Nature Communications · doi:10.1038/s41467-018-06598-z · Cited by 161
Zhou, N.-R., Zhang, T.-F., Xie, X.-W., & Wu, J.-Y. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116891 · Cited by 161
Experimental Machine Learning of Quantum States
Gao, J., Qiao, L., Jiao, Z., Ma, Y., Hu, C.-Q., Ren, R.-J., Yang, A.-L., Tang, H. L., Yung, M., & Jin, X. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.240501 · Cited by 153
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision
Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116512 · Cited by 165
The Born supremacy: quantum advantage and training of an Ising Born machine
Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020) · npj Quantum Information · doi:10.1038/s41534-020-00288-9 · Cited by 155
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo‐Ortiz, A. (2017) · Physical Review X · doi:10.1103/physrevx.7.041052 · Cited by 172
Image classification based on quantum K-Nearest-Neighbor algorithm
Dang, Y., Jiang, N., Hu, H., Ji, Z., & Zhang, W. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-2004-9 · Cited by 166
The strong gravitational lens finding challenge
Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., et al. (2019) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201832797 · Cited by 152
Guest Column: A Survey of Quantum Learning Theory
Arunachalam, S., & de Wolf, R. (2017) · ACM SIGACT News · doi:10.1145/3106700.3106710 · Cited by 152
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
