Supervised Learning with Quantum Computers

Stochastic Gradient Descent

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Machine learning and the physical sciences

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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

The power of quantum neural networks

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Circuit-centric quantum classifiers

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Quantum computing with neutral atoms

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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 · see citing works

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 · see citing works

Robust data encodings for quantum classifiers

LaRose, R., & Coyle, B. (2020) · Physical Review A · doi:10.1103/physreva.102.032420 · Cited by 332 · see citing works

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 314 · see citing works

Barren plateaus in variational quantum computing

Larocca, M., Thanasilp, S., Wang, S., Sharma, K., Biamonte, J., Coles, P. J., Cincio, Ł., McClean, J. R., Holmes, Z., & Cerezo, M. (2025) · Nature Reviews Physics · doi:10.1038/s42254-025-00813-9 · Cited by 318 · see citing works

Quantum Long Short-Term Memory

Chen, S. Y.-C., Yoo, S., & Fang, Y.-L. L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747369 · Cited by 260 · see citing works

The bitter truth about gate-based quantum algorithms in the NISQ era

Leymann, F., & Barzen, J. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abae7d · Cited by 228 · see citing works

Stochastic gradient descent for hybrid quantum-classical optimization

Sweke, R., Wilde, F., Meyer, J., Schuld, M., Faehrmann, P. K., Meynard-Piganeau, B., & Eisert, J. (2020) · Quantum · doi:10.22331/q-2020-08-31-314 · Cited by 219 · 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

QuantumNAS: 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 215 · see citing works

Quantum Machine Learning: A Review and Case Studies

Zeguendry, A., Jarir, Z., & Quafafou, M. (2023) · Entropy · doi:10.3390/e25020287 · Cited by 221 · see citing works

Stochastic gradient descent for hybrid quantum-classical optimization

Sweke, R., Sweke, R., Meyer, J. J., Meyer, J. J., Faehrmann, P. K., Meynard-Piganeau, B., & Eisert, J. (2020) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-28427 · Cited by 203 · see citing works

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 191 · 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

Quantum convolutional neural network based on variational quantum circuits

Gong, L., Pei, J.-J., Zhang, T., & Zhou, N. (2023) · Optics Communications · doi:10.1016/j.optcom.2023.129993 · Cited by 168 · see citing works

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 · see citing works

Generalization in Quantum Machine Learning: A Quantum Information Standpoint

Banchi, L., Pereira, J. L., & Pirandola, S. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.040321 · Cited by 154 · 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

Quantum machine learning for chemistry and physics

Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., Singh, V., & Kais, S. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00203e · Cited by 157 · see citing works

Training quantum embedding kernels on near-term quantum computers

Hubregtsen, T., Wierichs, D., Gil-Fuster, E., Derks, P.-J. H. S., Faehrmann, P. K., & Meyer, J. J. (2022) · Physical Review A · doi:10.1103/physreva.106.042431 · Cited by 144 · see citing works

Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

Rath, M., & Date, H. (2024) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-024-00285-3 · Cited by 142 · see citing works

Quantum transfer learning for breast cancer detection

Azevedo, V., Silva, C., & Dutra, I. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00062-4 · Cited by 121 · see citing works

Mixed Quantum–Classical Method for Fraud Detection With Quantum Feature Selection

Grossi, M., Ibrahim, N., Radescu, V. A. M., Loredo, R., Voigt, K., von Altrock, C., & Rudnik, A. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3213474 · Cited by 125 · see citing works

Quantum Federated Learning with Quantum Data

Chehimi, M. M., & Saad, W. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746622 · Cited by 115 · see citing works

Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

Banchi, L., & Crooks, G. E. (2021) · Quantum · doi:10.22331/q-2021-01-25-386 · Cited by 111 · see citing works

Encoding patterns for quantum algorithms

Weigold, M., Barzen, J., Leymann, F., & Salm, M. (2021) · IET Quantum Communication · doi:10.1049/qtc2.12032 · Cited by 102 · see citing works

Quantum machine learning: from physics to software engineering

Melnikov, A. A., Kordzanganeh, M., Alodjants, A. P., & Lee, R. (2023) · Advances in Physics X · doi:10.1080/23746149.2023.2165452 · Cited by 115 · see citing works

Quantum algorithm for the advection–diffusion equation simulated with the lattice Boltzmann method

Budinski, L. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-02996-3 · Cited by 101 · see citing works

Hybrid classical–quantum Convolutional Neural Network for stenosis detection in X-ray coronary angiography

Ovalle-Magallanes, E., Avina‐Cervantes, J. G., Cruz-Aceves, I., & Ruiz-Pinales, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116112 · Cited by 96 · see citing works

Theoretical guarantees for permutation-equivariant quantum neural networks

Schatzki, L., Larocca, M., Nguyen, Q. T., Sauvage, F., & Cerezo, M. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00804-1 · Cited by 107 · see citing works

An introduction to quantum machine learning: from quantum logic to quantum deep learning

Alchieri, L., Badalotti, D., Bonardi, P., & Bianco, S. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00056-8 · Cited by 92 · see citing works

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Kao, P.-Y., Yang, Y.-C., Chiang, W.-Y., Hsiao, J.-Y., Cao, Y., Aliper, A., Ren, F., Aspuru‐Guzik, A., Zhavoronkov, A. A., Hsieh, M.-H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00562 · Cited by 101 · see citing works

Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions

Ranga, D., Rana, A., Prajapat, S., Kumar, P., Kumar, K., & Vasilakos, A. V. (2024) · Mathematics · doi:10.3390/math12213318 · Cited by 101 · see citing works

Quantum computing in power systems

Zhou, Y., Tang, Z., Nikmehr, N., Babahajiani, P., Feng, F., Wei, T.-C., Zheng, H., & Zhang, P. (2022) · iEnergy · doi:10.23919/ien.2022.0021 · Cited by 98 · see citing works

On the Quantum versus Classical Learnability of Discrete Distributions

Sweke, R., Seifert, J., Hangleiter, D., & Eisert, J. (2021) · Quantum · doi:10.22331/q-2021-03-23-417 · Cited by 85 · see citing works

Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces

Goto, T., Tran, Q. H., & Nakajima, K. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.090506 · Cited by 97 · see citing works

A non-review of Quantum Machine Learning: trends and explorations

Dunjko, V., & Wittek, P. (2020) · doi:10.22331/qv-2020-03-17-32 · Cited by 78 · see citing works

An end-to-end trainable hybrid classical-quantum classifier

Chen, S. Y.-C., Huang, C.-M., Hsing, C.-W., & Kao, Y.-J. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac104d · Cited by 115 · see citing works

Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications

Gujju, Y., Matsuo, A., & Raymond, R. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.067001 · Cited by 93 · 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

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Zhou, Y., & Zhang, P. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3160384 · Cited by 85 · see citing works

Quantum Algorithm Design: Techniques and Applications

Shao, C., Yang, L., & Li, H. (2019) · Journal of Systems Science and Complexity · doi:10.1007/s11424-019-9008-0 · Cited by 73 · see citing works

Variational Quantum Singular Value Decomposition

Wang, X., Song, Z., & Wang, Y. (2021) · Quantum · doi:10.22331/q-2021-06-29-483 · Cited by 69 · 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

Machine learning for anomaly detection in particle physics

Belis, V., Odagiu, P., & Aarrestad, T. K. (2024) · Reviews in Physics · doi:10.1016/j.revip.2024.100091 · Cited by 78 · see citing works

Automatic design of quantum feature maps

Altares-López, S., Ribeiro, Á., & García‐Ripoll, J. J. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac1ab1 · Cited by 68 · see citing works

A systematic review of quantum machine learning for digital health

Gupta, R. S., Wood, C. E., Engstrom, T., Pole, J. D., & Shrapnel, S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01597-z · Cited by 74 · 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

Clinical data classification with noisy intermediate scale quantum computers

Moradi, S., Brandner, C., Spielvogel, C. P., Krajnc, D., Hillmich, S., Wille, R., Drexler, W., & Papp, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-05971-9 · Cited by 69 · 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

Input Redundancy for Parameterized Quantum Circuits

Vidal, F. J. G., & Theis, D. O. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.00297 · Cited by 71 · see citing works

Quantum-classical convolutional neural networks in radiological image classification

Matic, A., Monnet, M., Lorenz, J. M., Schachtner, B. M., & Messerer, T. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00024 · Cited by 70 · see citing works

Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements

Devadas, R. M., & Sowmya, T. (2025) · MethodsX · doi:10.1016/j.mex.2025.103318 · Cited by 66 · see citing works

Quantum algorithm for the Navier–Stokes equations by using the streamfunction-vorticity formulation and the lattice Boltzmann method

Budinski, L. (2022) · International Journal of Quantum Information · doi:10.1142/s0219749921500398 · Cited by 74 · see citing works

Quantum Kernels for Real-World Predictions Based on Electronic Health Records

Krunic, Z., Flöther, F. F., Seegan, G., Earnest-Noble, N., & Shehab, O. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3176806 · Cited by 65 · see citing works

Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets

Schetakis, N. A., Aghamalyan, D., Griffin, P., & Boguslavsky, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-14876-6 · Cited by 68 · see citing works

Quantum Optimal Transport with Quantum Channels

Palma, G. D., & Trevisan, D. (2021) · Annales Henri Poincaré · doi:10.1007/s00023-021-01042-3 · Cited by 59 · see citing works

Expanding Data Encoding Patterns For Quantum Algorithms

Weigold, M., Barzen, J., Leymann, F., & Salm, M. (2021) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c52384.2021.00025 · Cited by 60 · see citing works

Data compression for quantum machine learning

Dilip, R., Liu, Y.-J., Smith, A., & Pollmann, F. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043007 · Cited by 60 · see citing works

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Rajawat, A. S., Goyal, S. B., Bedi, P., Jan, T., Whaiduzzaman, M., & Prasad, M. (2023) · Future Internet · doi:10.3390/fi15080271 · Cited by 56 · see citing works

The theory of the quantum kernel-based binary classifier

Park, D. K., Blank, C., & Petruccione, F. (2020) · Physics Letters A · doi:10.1016/j.physleta.2020.126422 · Cited by 57 · see citing works

A Leap among Quantum Computing and Quantum Neural Networks: A Survey

Massoli, F. V., Vadicamo, L., Amato, G. M., & Falchi, F. (2022) · ACM Computing Surveys · doi:10.1145/3529756 · Cited by 64 · see citing works

Insights from incorporating quantum computing into drug design workflows

Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac789 · Cited by 54 · see citing works

Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network

Sannidhan, M. S., Martis, J. E., Nayak, R. S., Sunil Kumar Aithal, S., & Sudeepa, K. B. (2023) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.321150 · Cited by 54 · see citing works

Quantum angle encoding with learnable rotation applied to quantum–classical convolutional neural networks

Ovalle-Magallanes, E., Alvarado-Carrillo, D. E., Avina‐Cervantes, J. G., Cruz-Aceves, I., & Ruiz-Pinales, J. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110307 · Cited by 55 · see citing works

Quantum Computing: An Overview Across the System StackPreprint

Resch, S., & Karpuzcu, U. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07240 · Cited by 80 · see citing works

Experimental quantum kernel trick with nuclear spins in a solid

Kusumoto, T., Mitarai, K., Fujii, K., Kitagawa, M., & Negoro, M. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00423-0 · Cited by 52 · see citing works

Towards Quantum-Enhanced Machine Learning for Network Intrusion Detection

Gouveia, A., & Correia, M. (2020) · 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca51143.2020.9306691 · Cited by 52 · see citing works

Quantum computing with and for many-body physics

Ayral, T., Besserve, P., Denis Lacroix, A., & Guzman, E. A. R. (2023) · The European Physical Journal A · doi:10.1140/epja/s10050-023-01141-1 · Cited by 54 · see citing works

Quantum clustering with k-Means: A hybrid approach

Poggiali, A., Berti, A., Bernasconi, A., Corso, G. M. D., & Guidotti, R. (2024) · Theoretical Computer Science · doi:10.1016/j.tcs.2024.114466 · Cited by 62 · see citing works

Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network

Zhuang, Q., & Zhang, Z. (2019) · Physical Review X · doi:10.1103/physrevx.9.041023 · Cited by 51 · see citing works

Linear-depth quantum circuits for multiqubit controlled gates

da Silva, A. J., & Park, D. K. (2022) · Physical Review A · doi:10.1103/physreva.106.042602 · Cited by 50 · see citing works

Quantum anomaly detection in the latent space of proton collision events at the LHC

Belis, V., Wozniak, K. A., Puljak, E., Barkoutsos, P. K., Dissertori, G., Grossi, M., Maurizio Pierini, B., Reiter, F., Tavernelli, I., & Vallecorsa, S. (2024) · Communications Physics · doi:10.1038/s42005-024-01811-6 · Cited by 53 · 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

Hybrid classical-quantum autoencoder for anomaly detection

Sakhnenko, A., O’Meara, C., Ghosh, K., Mendl, C. B., Cortiana, G., & Bernabé-Moreno, J. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00075-z · Cited by 49 · see citing works

Quantum-inspired machine learning on high-energy physics data

Felser, T., Trenti, M., Sestini, L., Gianelle, A., Zuliani, D., Lucchesi, D., & Montangero, S. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00443-w · Cited by 48 · see citing works

Nonlinear quantum neuron: A fundamental building block for quantum neural networks

Yan, S., Qi, H., & Cui, W. (2020) · Physical Review A · doi:10.1103/physreva.102.052421 · Cited by 45 · see citing works

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

Sierra-Sosa, D., Telahun, M., & Elmaghraby, A. (2020) · IEEE Access · doi:10.1109/access.2020.3040798 · Cited by 46 · see citing works

Artificial Intelligence Computing at the Quantum Level

Ayoade, O., Rivas, P., & Orduz, J. (2022) · Data · doi:10.3390/data7030028 · Cited by 47 · see citing works

Quantum algorithms for approximate function loading

Marin-Sanchez, G., Gonzalez-Conde, J., & Sanz, M. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033114 · Cited by 46 · see citing works

Quantum Random Access Memory for Dummies

Phalak, K., Chatterjee, A., & Ghosh, S. (2023) · Sensors · doi:10.3390/s23177462 · Cited by 45 · see citing works

A Variational Algorithm for Quantum Neural Networks

Macaluso, A., Clissa, L., Lodi, S., & Sartori, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50433-5_45 · Cited by 44 · see citing works

Style-based quantum generative adversarial networks for Monte Carlo events

Bravo-Prieto, C., Baglio, J., Cè, M., Francis, A. S., Grabowska, D. M., & Carrazza, S. (2022) · Quantum · doi:10.22331/q-2022-08-17-777 · Cited by 48 · see citing works

Quantum machine intelligence

Acampora, G. (2019) · Quantum Machine Intelligence · doi:10.1007/s42484-019-00006-5 · Cited by 47 · see citing works

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

DiAdamo, S., O’Meara, C., Cortiana, G., & Bernabé-Moreno, J. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3185505 · Cited by 41 · see citing works

On the interpretability of quantum neural networks

Pira, L., & Ferrie, C. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00191-y · Cited by 42 · see citing works

QDataSet, quantum datasets for machine learning

Perrier, E., Youssry, A., & Ferrie, C. (2022) · Scientific Data · doi:10.1038/s41597-022-01639-1 · Cited by 41 · see citing works

Quantum Dilated Convolutional Neural Networks

Chen, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3152213 · Cited by 51 · see citing works

An enhanced approach for predicting air pollution using quantum support vector machine

Farooq, O., Shahid, M., Arshad, S., Altaf, A., Iqbal, F., Vera, Y. A. M., Flores, M. Á. L., & Ashraf, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-69663-2 · Cited by 38 · see citing works

Quantum computing methods for supervised learning

Kulkarni, V., Kulkarni, M., & Pant, A. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00050-0 · Cited by 38 · see citing works

Variational quantum approximate support vector machine with inference transfer

Park, S., Park, D. K., & Rhee, J. K. (2023) · Scientific Reports · doi:10.1038/s41598-023-29495-y · Cited by 38 · see citing works

Paving the way to hybrid quantum–classical scientific workflows

Cranganore, S. S., Maio, V. D., Brandić, I., & Deelman, E. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.030 · Cited by 41 · see citing works

Parallelizing the queries in a bucket-brigade quantum random access memory

Paler, A., Oumarou, O., & Basmadjian, R. (2020) · Physical Review A · doi:10.1103/physreva.102.032608 · Cited by 36 · see citing works

A quantum-inspired classifier for clonogenic assay evaluations

Sergioli, G., Militello, C., Rundo, L., Minafra, L., Torrisi, F., Russo, G. I., Chow, K. L., & Giuntini, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-82085-8 · Cited by 38 · see citing works

Concise Guide to Quantum Machine Learning

Pastorello, D. (2022) · Machine learning · doi:10.1007/978-981-19-6897-6 · Cited by 17 · see citing works

Quantum Machine Learning: An Applied Approach

Ganguly, S. (2021) · Apress eBooks · doi:10.1007/978-1-4842-7098-1 · Cited by 30 · see citing works

Quantum Machine Learning

Conti, C. (2023) · Quantum science and technology · doi:10.1007/978-3-031-44226-1 · Cited by 10 · see citing works

Quantum computing methods for supervised learning

Kulkarni, V., Kulkarni, M., & Pant, A. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00050-0 · Cited by 38 · see citing works

Experimental Evaluation Of Quantum Machine Learning

Venkatakrishnasameer, B. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.12505696