Quantum-Enhanced Machine Learning
Open accessAbstract
The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.
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
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Quantum computation and quantum information
Law, J. (2001) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/505482.505499 · Cited by 20,449 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,726 · 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
Giovannetti, V., Lloyd, S., & Maccone, L. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.160501 · Cited by 935 · see citing works
Quantum Measurement and Control
Kiefer, C. (2010) · Classical and Quantum Gravity · doi:10.1088/0264-9381/27/24/249002 · Cited by 878 · see citing works
(1996) · Cited by 824 · see citing works
The quest for a Quantum Neural Network
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0809-8 · Cited by 671 · see citing works
Quantum Reinforcement Learning
Dong, D., Chen, C., Li, H., & Tarn, T.-J. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.925743 · Cited by 410 · see citing works
Quantum Machine Learning: What Quantum Computing Means to Data Mining
Wittek, P. (2014) · Cited by 319 · see citing works
Automated 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 · see citing works
Fixed-Point Quantum Search with an Optimal Number of Queries
Yoder, T. J., Low, G. H., & Chuang, I. L. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.210501 · Cited by 223 · see citing works
Quantum speed-up for unsupervised learning
Aı̈meur, E., Brassard, G., & Gambs, S. (2012) · Machine Learning · doi:10.1007/s10994-012-5316-5 · Cited by 212 · see citing works
Quantum Speedup for Active Learning Agents
Paparo, G. D., Dunjko, V., Makmal, A., Martin-Delgado, M. A., & Briegel, H. J. (2014) · Physical Review X · doi:10.1103/physrevx.4.031002 · Cited by 192 · see citing works
Self-citationProjective simulation for artificial intelligence
Briegel, H. J., & Cuevas, G. D. L. (2012) · Scientific Reports · doi:10.1038/srep00400 · Cited by 148 · see citing works
Self-citationGrover's Quantum Algorithm Applied to Global Optimization
Baritompa, W. P., Bulger, D. W., & Wood, G. R. (2005) · SIAM Journal on Optimization · doi:10.1137/040605072 · Cited by 108 · see citing works
Evolutionary algorithms for hard quantum control
Zahedinejad, E., Schirmer, S. G., & Sanders, B. C. (2014) · Physical Review A · doi:10.1103/physreva.90.032310 · Cited by 85 · see citing works
Differential Evolution for Many-Particle Adaptive Quantum Metrology
Lovett, N. B., de Bellaistre, C. C., Perarnau-Llobet, M., & Sanders, B. C. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.220501 · Cited by 71 · see citing works
Adaptive quantum computation in changing environments using projective simulation
Tiersch, M., Ganahl, E. J., & Briegel, H. J. (2015) · Scientific Reports · doi:10.1038/srep12874 · Cited by 56 · see citing works
Self-citationMeta-learning within Projective Simulation
Makmal, A., Melnikov, A. A., Dunjko, V., & Briegel, H. J. (2016) · IEEE Access · doi:10.1109/access.2016.2556579 · Cited by 37 · see citing works
Self-citationChouard, T., & Venema, L. C. (2015) · Nature · doi:10.1038/521435a · Cited by 31 · see citing works
Stajic, J., Stone, R., Chin, G., & Wible, B. (2015) · Science · doi:10.1126/science.349.6245.248 · Cited by 30 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- [2]Science, Special issue on Artificial Intelligence , Vol. 349 (AAAS, 2015)
- [6]Lloyd, S., Mohseni, M. and Rebentrost, P. Quantum algorithms for supervised and unsupervised machine learning. ArXiv:1307.0411 (2013). · arXiv
- [11]Arunachalam, S., de Wolf, R., arXiv:1607.00932 (2016). · arXiv
- [12]Servedio, R. A. Gortler, S. J. Quantum versus classical learnability. In: 16th Annual IEEE Conference on Computational Complexity, 138-148 (2001).
- [13]Atıcı, A. Advances in Quantum Computational Learning Theory (Columbia University, PhD Thesis, 2006).
- [22]Van den Nest, M., D¨ ur, W., Miyake, A. & Briegel, H. J. Fundamentals of universality in one-way quantum computation. New Journal of Physics 9 , 204 (2007). URL http://stacks.iop.org/1367-2630/9/i=6/a=204 .
- [23]Wolpert, D. H. The lack of a priori distinctions between learning algorithms. Neural Comput. 8 , 1341-1390 (1996). URL http://dx.doi.org/10.1162/neco.1996. 8.7.1341 . · DOI
- [27]Durr, C., Hoyer, P. A Quantum Algorithm for Finding the Minimum. arXiv:quant-ph/9607014 (1996). · arXiv
- [30]Brassard, G., Hoyer, P., Mosca, M. and Tapp, A. Quantum Amplitude Amplification and Estimation arXiv:quant-ph/0005055 (2000). · arXiv
- [31]Dunjko, V., Taylor, J. M. & Briegel, H. J. Framework for learning agents in quantum environments. arXiv:1507.08482 (2015). · arXiv
- [32]Grover, L. K. Proceedings, 28th Annual ACM Symposium on the Theory of Computing (STOC) 212 (1996).
- [34]Sutton, R. S. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. Advances in Natural Computation In Proceedings of the Seventh International Conference on Machine Learninge 216-224 (1990).
- [36]Makmal, A , Melnikov, A. A., and Briegel, H. J. Metalearning within Projective Simulation. IEEE Access 4, 2110 (2016).
Cited by
Sorted by citations
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
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
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517 · see citing works
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 1,132 · see citing works
Self-citationTraining deep quantum neural networks
Beer, K., Bondarenko, D., Farrelly, T., Osborne, T. J., Salzmann, R., Scheiermann, D., & Wolf, R. (2020) · Nature Communications · doi:10.1038/s41467-020-14454-2 · Cited by 726 · see citing works
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 714 · see citing works
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 · see citing works
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 · see citing works
Quanvolutional neural networks: powering image recognition with quantum circuits
Henderson, M., Shakya, S., Pradhan, S. M., & Cook, T. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00012-y · Cited by 534 · see citing works
Transfer learning in hybrid classical-quantum neural networks
Mari, A., Bromley, T. R., Izaac, J., Schuld, M., & Killoran, N. (2020) · Quantum · doi:10.22331/q-2020-10-09-340 · Cited by 435 · see citing works
Koch, C. P., Boscain, U., Calarco, T., Dirr, G., Filipp, S., Glaser, S. J., Kosloff, R., Montangero, S., Schulte-Herbrüggen, T., Sugny, D., et al. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00138-x · Cited by 404 · see citing works
Reinforcement Learning in Different Phases of Quantum Control
Bukov, M., Day, A. G. R., Sels, D., Weinberg, P., Polkovnikov, A., & Mehta, P. (2018) · Physical Review X · doi:10.1103/physrevx.8.031086 · Cited by 348 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Zhang, Z., & Zhuang, Q. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abd4c3 · Cited by 246 · see citing works
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 · see citing works
Machine Learning: Quantum vs Classical
Khan, T. M., & Robles‐Kelly, A. (2020) · IEEE Access · doi:10.1109/access.2020.3041719 · Cited by 217 · see citing works
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 227 · see citing works
Ullah, U., & García-Zapirain, B. (2024) · IEEE Access · doi:10.1109/access.2024.3353461 · Cited by 211 · see citing works
A quantum convolutional neural network on NISQ devices
Wei, S., Chen, Y., Zhou, Z., & Long, G. (2022) · AAPPS Bulletin · doi:10.1007/s43673-021-00030-3 · Cited by 192 · see citing works
Challenges and opportunities in quantum optimization
Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., et al. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00770-9 · Cited by 201 · see citing works
“…oracular access to the task environment have been explored: for example, where coherent access to a unitary encoding the transition function is possible 149 , or where the environment closely mimics classical reinforcement-learning settings, allows only actions as inputs and has memory 150 .”
Self-citationSolving 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 · see citing works
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 176 · see citing works
Quantum technology for military applications
Krelina, M. (2021) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-021-00113-y · Cited by 208 · see citing works
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 176 · see citing works
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. H. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0149-8 · Cited by 171 · see citing works
Experimental quantum speed-up in reinforcement learning agents
Saggio, V., Asenbeck, B. E., Hamann, A., Strömberg, T., Schiansky, P., Dunjko, V., Friis, N., Harris, N. C., Hochberg, M. J., Englund, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03242-7 · Cited by 170 · see citing works
Self-citationQuantum 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
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 157 · see citing works
Silq: a high-level quantum language with safe uncomputation and intuitive semantics
Bichsel, B., Baader, M., Gehr, T., & Vechev, M. (2020) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3385412.3386007 · Cited by 154 · see citing works
Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac003 · Cited by 154 · see citing works
Zhang, X., Wei, Z., Asad, R., Yang, X.-C., & Wang, X. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0201-8 · Cited by 147 · see citing works
Path Planning for Cellular-Connected UAV: A DRL Solution With Quantum-Inspired Experience Replay
Li, Y., Aghvami, A. H., & Dong, D. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3162749 · Cited by 150 · see citing works
Quantum machine learning for quantum anomaly detection
Liu, N., & Rebentrost, P. (2018) · Physical Review A · doi:10.1103/physreva.97.042315 · Cited by 149 · see citing works
Quantum algorithm for linear regression
Wang, G. (2017) · Physical Review A · doi:10.1103/physreva.96.012335 · Cited by 145 · see citing works
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
Maheshwari, D., García-Zapirain, B., & Sierra-Sosa, D. (2022) · IEEE Access · doi:10.1109/access.2022.3195044 · Cited by 147 · see citing works
Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing
Hegade, N. N., Paul, K., Ding, Y., Sanz, M., Albarrán-Arriagada, F., Solano, E., & Chen, X. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.15.024038 · Cited by 133 · see citing works
Amin, J., Sharif, M. I., Gul, N., Kadry, S., & Chakraborty, C. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09926-6 · Cited by 130 · see citing works
Quantum reinforcement learning during human decision-making
Li, J., Dong, D., Wei, Z., Liu, Y., Pan, Y., Nori, F., & Zhang, X. (2020) · Nature Human Behaviour · doi:10.1038/s41562-019-0804-2 · Cited by 122 · see citing works
Machine Learning Algorithms in Quantum Computing: A Survey
Ramezani, S. B., Sommers, A., Manchukonda, H. K., Rahimi, S., & Amirlatifi, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207714 · Cited by 123 · see citing works
Review of Quantum Image Processing
Wang, Z., Xu, M., & Zhang, Y. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09599-2 · Cited by 127 · see citing works
Quantum Neural Network States: A Brief Review of Methods and Applications
Jia, Z., Yi, B., Zhai, R., Wu, Y.-C., Guo, G., & Guo, G. (2019) · Advanced Quantum Technologies · doi:10.1002/qute.201800077 · Cited by 123 · see citing works
Quantum Software Engineering: Landscapes and HorizonsPreprint
Zhao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07047 · Cited by 200 · see citing works
Deep Reinforcement Learning With Quantum-Inspired Experience Replay
Wei, Q., Ma, H., Chen, C., & Dong, D. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3053414 · Cited by 116 · see citing works
Quantum Computing Review: A Decade of Research
Sood, S. K., & Pooja (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3284689 · Cited by 121 · see citing works
Quantum Machine Learning: A tutorial
Martín-Guerrero, J. D., & Lamata, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.02.102 · Cited by 106 · see citing works
Recent advances for quantum classifiers
Li, W., & Deng, D.-L. (2021) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-021-1793-6 · Cited by 106 · see citing works
Unitary quantum perceptron as efficient universal approximator
Torrontegui, E., & García-Ripoll, J. J. (2019) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/125/30004 · Cited by 102 · see citing works
Quantum computing for smart grid applications
Ullah, M. H., Eskandarpour, R., Zheng, H., & Khodaei, A. (2022) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12602 · Cited by 107 · see citing works
Quantum estimation, control and learning: Opportunities and challenges
Dong, D., & Petersen, I. R. (2022) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2022.04.011 · Cited by 96 · see citing works
Jawad, A. T., Mâaloul, R., & Chaari, L. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110085 · Cited by 99 · see citing works
Basic protocols in quantum reinforcement learning with superconducting circuits
Lamata, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-01711-6 · Cited by 89 · see citing works
A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification
Li, Y., Zhou, R., Xu, R., Luo, J., & Jiang, S.-X. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3000734 · Cited by 89 · see citing works
Biology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87 · see citing works
Yan, R., Wang, Y., Xu, Y. H., & Dai, J. (2022) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2022.3140702 · Cited by 85 · see citing works
Pal, S., Bhattacharya, M., Lee, S.-S., & Chakraborty, C. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-00765-4 · Cited by 84 · see citing works
Bokhan, D., Mastiukova, A. S., Boev, A. S., Trubnikov, D. N., & Fedorov, A. K. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.1069985 · Cited by 83 · see citing works
Quantum Machine Learning: A Review and Current Status
Mishra, N., Kapil, M., Rakesh, H. R., Anand, A. K., Mishra, N., Warke, A., Sarkar, S., Dutta, S., Gupta, S., Dash, A. P., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5619-7_8 · Cited by 85 · see citing works
Sengupta, K., & Srivastava, P. R. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01588-6 · Cited by 84 · see citing works
Survey of Promising Technologies for Quantum Drones and Networks
Kumar, A., Bhatia, S., Kaushik, K., Gandhi, S. M., Devi, S. G., Pacheco, D. A. D. J., & Mashat, A. (2021) · IEEE Access · doi:10.1109/access.2021.3109816 · Cited by 84 · see citing works
Identification of light sources using machine learning
You, C., Quiroz-Juarez, M. A., Lambert, A., Bhusal, N., Dong, C., Pérez-Leija, A., Javaid, A., León‐Montiel, R. D. J., & Magaña‐Loaiza, O. S. (2020) · Applied Physics Reviews · doi:10.1063/1.5133846 · Cited by 78 · see citing works
Advances in quantum reinforcement learning
Dunjko, V., Taylor, J. M., & Briegel, H. J. (2017) · 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2017.8122616 · Cited by 81 · see citing works
Self-citationExpressive power of parametrized quantum circuits
Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2018) · UTS ePRESS (University of Technology Sydney) · Cited by 99 · see citing works
NISQ Computers: A Path to Quantum Supremacy
AbuGhanem, M., & Eleuch, H. (2024) · IEEE Access · doi:10.1109/access.2024.3432330 · Cited by 80 · see citing works
Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks
Mashhadimoslem, H., Abdol, M. A., Karimi, P., Zanganeh, K. E., Shafeen, A., Elkamel, A., & Kamkar, M. (2024) · ACS Nano · doi:10.1021/acsnano.3c13001 · Cited by 77 · see citing works
Analytic continuation via domain knowledge free machine learning
Yoon, H., Sim, J.-H., & Han, M. J. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.98.245101 · Cited by 74 · see citing works
A hybrid quantum–classical neural network with deep residual learning
Liang, Y., Peng, W., Zheng, Z., Sílven, O., & Zhao, G. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.05.028 · Cited by 74 · see citing works
Nuclear magnetic resonance for quantum computing: Techniques and recent achievements
Xin, T., Wang, B.-X., Li, K., Kong, X., Wei, S.-J., Wang, T., Ruan, D., & Long, G. (2018) · Chinese Physics B · doi:10.1088/1674-1056/27/2/020308 · Cited by 73 · see citing works
Reinforcement learning using quantum Boltzmann machines
Crawford, D., Levit, A., Ghadermarzy, N., Oberoi, J. S., & Ronagh, P. (2018) · Quantum Information and Computation · doi:10.26421/qic18.1-2-3 · Cited by 100 · see citing works
Circuit Compilation Methodologies for Quantum Approximate Optimization Algorithm
Alam, M., Saki, A. A., & Ghosh, S. (2020) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro50266.2020.00029 · Cited by 76 · see citing works
A hybrid classical-quantum approach for multi-class classification
Chalumuri, A., Kune, R., & Manoj, B. S. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03029-9 · Cited by 70 · see citing works
Quantum Enhancements for Deep Reinforcement Learning in Large Spaces
Jerbi, S., Trenkwalder, L. M., Nautrup, H. P., Briegel, H. J., & Dunjko, V. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.010328 · Cited by 71 · see citing works
Self-citationQuantum Neuromorphic Computing with Reservoir Computing Networks
Ghosh, S., Nakajima, K., Krisnanda, T., Fujii, K., & Liew, T. C. H. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202100053 · Cited by 69 · see citing works
Classification with Quantum Machine Learning: A Survey
Abohashima, Z., Elhosen, M., Houssein, E. H., & Mohamed, W. M. (2020) · arXiv · doi:10.48550/arxiv.2006.12270 · Cited by 68 · see citing works
Chen, L., Tao, S., Huang, X., Wang, Y., Xia, B., & Zhang, X. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.02.030 · Cited by 66 · see citing works
Reconstruction of a Photonic Qubit State with Reinforcement Learning
Yu, S., Albarrán-Arriagada, F., Retamal, J. C., Wang, Y., Liu, W., Ke, Z., Meng, Y., Li, Z., Tang, J., Solano, E., et al. (2019) · Advanced Quantum Technologies · doi:10.1002/qute.201800074 · Cited by 66 · see citing works
Kernel methods in Quantum Machine Learning
Mengoni, R., & Pierro, A. D. (2019) · Quantum Machine Intelligence · doi:10.1007/s42484-019-00007-4 · Cited by 67 · see citing works
Reconstructing Quantum States With Quantum Reservoir Networks
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3009716 · Cited by 63 · see citing works
Reformulation of the No-Free-Lunch Theorem for Entangled Datasets
Sharma, K., Cerezo, M., Holmes, Z., Cincio, Ł., Sornborger, A. T., & Coles, P. J. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.070501 · Cited by 66 · see citing works
Measurement-based adaptation protocol with quantum reinforcement learning
Albarrán-Arriagada, F., Retamal, J. C., Solano, E., & Lamata, L. (2018) · Physical Review A · doi:10.1103/physreva.98.042315 · Cited by 59 · see citing works
Quantum machine learning algorithms for anomaly detection: A review
Corli, S., Moro, L., Dragoni, D., Dispenza, M., & Prati, E. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107632 · Cited by 73 · see citing works
Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits
Xin, T., Che, L., Cheng, X., Singh, A., Nie, X., Li, J., Dong, Y., & Lu, D. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.110502 · Cited by 60 · see citing works
Machine learning meets quantum foundations: A brief survey
Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C. (2020) · AVS Quantum Science · doi:10.1116/5.0007529 · Cited by 59 · see citing works
Quantum Optimization: Potential, Challenges, and the Path Forward
Abbas, A., (NNSA), U. N. N. S. A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D., et al. (2023) · doi:10.2172/2229681 · Cited by 63 · see citing works
Self-citationQuantum circuit cutting with maximum-likelihood tomography
Perlin, M. A., Saleem, Z. H., Suchara, M., & Osborn, J. C. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00390-6 · Cited by 57 · see citing works
Quantum machine learning and quantum biomimetics: A perspective
Lamata, L. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab9803 · Cited by 58 · see citing works
Quantum learning with noise and decoherence: a robust quantum neural network
Nguyen, N. H., Behrman, E., & Steck, J. E. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00013-x · Cited by 57 · see citing works
Machine learning \& artificial intelligence in the quantum domainPreprint
Dunjko, V., & Briegel, H. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02779 · Cited by 54 · see citing works
Self-citationNeural Networks-Based Cryptography: A Survey
Meraouche, I., Dutta, S., Tan, H., & Sakurai, K. (2021) · IEEE Access · doi:10.1109/access.2021.3109635 · Cited by 52 · see citing works
“Another interesting research area focuses on quantum-enhancing the neural networks which requires writing a quantum circuit of the neural network and running it on a quantum machine simulator or on an online available quantum machine [43], [44].”
BackgroundQuantum Language Model With Entanglement Embedding for Question Answering
Chen, Y., Pan, Y., & Dong, D. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3131252 · Cited by 53 · see citing works
Supervised Quantum Learning without Measurements
Álvarez Rodríguez, U., Lamata Manuel, L., Escandell Montero, P., Martín‐Guerrero, J. D., & Solano Villanueva, E. L. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 54 · see citing works
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Miroszewski, A., B, J. M. A., Czelusta, G., Szczepanek, F., Grabowski, B., Saux, B. L., & Nalepa, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3304122 · Cited by 51 · see citing works
A Stochastic Process Model for Free Agency under Indeterminism
Müller, T. (2018) · dialectica · doi:10.1111/1746-8361.12222 · Cited by 49 · see citing works
Presence and Absence of Barren Plateaus in Tensor-Network Based Machine Learning
Liu, Z., Yu, L.-W., Duan, L., & Deng, D.-L. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.270501 · Cited by 49 · see citing works
Markovian Quantum Neuroevolution for Machine Learning
Lu, Z., Shen, P.-X., & Deng, D.-L. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.044039 · Cited by 48 · see citing works
Supervised Quantum Learning without Measurements
Alvarez-Rodriguez, U., Lamata, L., Escandell-Montero, P., Martín‐Guerrero, J. D., & Solano, E. (2017) · Scientific Reports · doi:10.1038/s41598-017-13378-0 · Cited by 59 · see citing works
Quantum Embedding Search for Quantum Machine Learning
Nguyen, N., & Chen, K. (2022) · IEEE Access · doi:10.1109/access.2022.3167398 · Cited by 46 · see citing works
Reinforcement Learning and Physics
Martín‐Guerrero, J. D., & Lamata, L. (2021) · Applied Sciences · doi:10.3390/app11188589 · Cited by 46 · see citing works
- Authors
- Dunjko, V., Taylor, J. M., & Briegel, H. J.
- Affiliations
- Universität Innsbruck (AT)
- National Institute of Standards and Technology (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2016-09-20
- Volume
- 117
- Issue
- 13
- Pages
- 130501-130501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2521267242
- Cited by
- 460
- References
- 23
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 60 · 2023: 54 · 2022: 50 · 2021: 63 · 2020: 56 · 2019: 31 · 2018: 23 · 2017: 17
- FWCI
- 19.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review Letters (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (SFB FoQuS F 4012)
- Templeton World Charity Foundation (TWCF0078/AB46)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum machine learning, supervised learning, unsupervised learning, quantum information, learning efficiency, quantum advantage
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
