The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Quantum autoencoders for efficient compression of quantum data
Open accessAbstract
Abstract Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x , to map x to a lower dimensional point y such that x can likely be recovered from y . The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This approach has been used in generalization and in classification tasks [80, 156], with Refs. [76, 80, 160, 161] discussing different ways of embedding classical data into quantum states (such as data re-uploading), and with Ref. [158] showing an experimental demonstration of variational classification.”
From Variational quantum algorithms (2021) · cites this paper as [161]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In QML, state preparation is critical for pre-processing and encoding the classical data into a Quantum state.”
From Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset (2022) · cites this paper as [16]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The quantum autoencoder (Romero et al. , 2017) (QAE) is a VQA for the compression of data on a quantum computer.”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Romero et al. , 2017
In the citing paper
“Other techniques that could be implemented using this strategy are canonical transformation theory 119 and perturbative explicit correlated corrections 363,364 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [364]
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
A Limited Memory Algorithm for Bound Constrained Optimization
Byrd, R. H., Lu, P., Nocedal, J. M., & Zhu, C. (1995) · SIAM Journal on Scientific Computing · doi:10.1137/0916069 · Cited by 6,267
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552
Self-citationWales, D. J., & Doye, J. P. K. (1997) · The Journal of Physical Chemistry A · doi:10.1021/jp970984n · Cited by 3,030
Georgescu, I. M., Ashhab, S., & Nori, F. (2014) · Reviews of Modern Physics · doi:10.1103/revmodphys.86.153 · Cited by 2,932
QuTiP: An open-source Python framework for the dynamics of open quantum systems
Johansson, J. R., Nation, P. D., & Nori, F. (2012) · Computer Physics Communications · doi:10.1016/j.cpc.2012.02.021 · Cited by 2,279
QuTiP 2: A Python framework for the dynamics of open quantum systems
Johansson, J. R., Nation, P. D., & Nori, F. (2012) · Computer Physics Communications · doi:10.1016/j.cpc.2012.11.019 · Cited by 2,251
The theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227
Self-citationSuperconducting quantum circuits at the surface code threshold for fault tolerance
Barends, R., Kelly, J., Megrant, A., Veitia, A., Sank, D., Jeffrey, E., White, T. C., Mutus, J., Fowler, A. G., Campbell, B., et al. (2014) · Nature · doi:10.1038/nature13171 · Cited by 1,733
Simulated Quantum Computation of Molecular Energies
Aspuru-Guzik, A., Dutoi, A. D., Love, P. J., & Head-Gordon, M. (2005) · Science · doi:10.1126/science.1113479 · Cited by 1,418
Self-citationBuhrman, H., Cleve, R., Watrous, J., & de Wolf, R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.167902 · Cited by 1,190
Research works printed but not linked: 1
- Sousa, 2007, Quantum Info. Comput., 7, 228
Cited by
Sorted by citations
Items 401–500 of 560
Density matrix representation of hybrid tensor networks for noisy quantum devices
Harada, H., Suzuki, Y., Yang, B., Tokunaga, Y., & Endo, S. (2025) · Quantum · doi:10.22331/q-2025-08-07-1823 · Cited by 6
Efficient Quantum Emulation of Unknown Unitaries
Marvian, I., & Lloyd, S. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.030346 · Cited by 2
Parameter-efficient quantum anomaly detection method on a superconducting quantum processor
Wang, M., Jiang, J., & Coveney, P. V. (2025) · Physical Review Research · doi:10.1103/cv9y-2cnj · Cited by 5
QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
Nguyen, H. Q., Nguyen, X.-B., Pandey, S., Khan, S. U., Safro, I., & Luu, K. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.10323 · Cited by 3
Superior resilience to poisoning and amenability to unlearning in quantum machine learning
Chen, Y., & Zhang, S. (2026) · Nature Communications · doi:10.1038/s41467-026-70420-4 · Cited by 6
QFI‐Opt: Communication‐Efficient Quantum Federated Learning via Quantum Fisher Information
Zhang, R., Wang, Y., He, X. G., Cai, Z., Di, Y., Bao, J., Fan, J., & Qu, Z. (2025) · Software Practice and Experience · doi:10.1002/spe.70048 · Cited by 2
Quantum hybrid feature selector
Lopatkin, V., Sagingalieva, A., Lusnig, L., Protasevich, T., Behnke, B., & Melnikov, A. A. (2026) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-026-00491-1 · Cited by 2
Ahmad, J. E. (2026) · Telecommunication Systems · doi:10.1007/s11235-026-01436-w · Cited by 2
A review of quantum machine learning algorithms, applications, and emerging advantages
Gokhale, S. S., Dhote, R. R., & Delhibabu, R. (2026) · Discover Computing · doi:10.1007/s10791-026-10085-1 · Cited by 7
Qi, H., Xu, Y., Wang, H., Gani, A., & Por, L. Y. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05186-1 · Cited by 2
Parameter-Efficient Quantum and Hybrid Autoencoders for One-Class Anomaly Detection
Salem, M., Pontes, D., Carrett, J., Böhm, L., dos Reis, H., & Ferrugem, A. P. (2026) · Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ) · doi:10.5753/sbccq.2026.23343 · Cited by 2
Accordion AutoEncoders (A2E) for Generative Classification with Low Complexity Network
Kelso, K., & Lee, B. K. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00152 · Cited by 2
Quantum Neural Networks: Bridging Quantum Computing and Machine Learning
Thomas, R. K. (2023) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2023/v16i3340 · Cited by 3
Quantum Autoencoders for Learning Quantum Channel Codes
Rathi, L., DiAdamo, S., & Shabani, A. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427450 · Cited by 2
Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data
Liang, Z., Chen, L. A., & Xiao, W. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2718/1/012067 · Cited by 2
LEXIQL: Quantum Natural Language Processing on NISQ-era Machines
Silver, D., Ranjan, A., Achutha, R., Patel, T., & Tiwari, D. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00073 · Cited by 4
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Aktar, S., Bärtschi, A., Oyen, D., Eidenbenz, S., & Badawy, A. A. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00181 · Cited by 9
Error mitigation in brainbox quantum autoencoders
Pazem, J., & Ansari, M. H. (2025) · Scientific Reports · doi:10.1038/s41598-024-84171-z · Cited by 9
Hao, C., Ma, Q., Si, N., Liu, B., & Qu, D. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00391-w · Cited by 2
QEA-FAD: Quantum Enhanced Autoencoders for Anomaly Detection in Finance and Healthcare
Kundu, S., Mishra, D., Tarasia, N., Lenka, R. K., & Barik, R. K. (2025) · International Conference on Networks and Cryptology (NETCRYPT) · doi:10.1109/netcrypt65877.2025.11102801 · Cited by 2
Huang, Y., Yang, M., & Zhou, D. L. (2026) · Chinese Physics Letters · doi:10.1088/0256-307x/43/2/020601 · Cited by 2
A quantum autoencoder: using machine learning to compress qutrits
Pepper, A., Tischler, N., & Pryde, G. J. (2020) · Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) · doi:10.1364/cleopr.2020.c12c_5 · Cited by 2
Variational Circuits as Machine Learning Models
Schuld, M., & Petruccione, F. (2021) · Quantum science and technology · doi:10.1007/978-3-030-83098-4_5 · Cited by 3
Efficient quantum algorithms for set operations
Elgendy, R., Younes, A., Abu-Donia, H. M., & Farouk, R. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-56860-2 · Cited by 2
Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A PerspectivePreprint
Joshi, R. P., & Kumar, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06045 · Cited by 2
Chip-to-Chip High-Dimensional Teleportation via A Quantum AutoencoderPreprint
Zhang, H., Wan, L., Haug, T., Mok, W., Cai, H. N., Shi, Y., Chin, L. K., Karim, M. F., Xiao, L., Lo, G.-Q., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-809022/v1 · Cited by 2
Quantum computing enhanced machine learning for physico-chemical applicationsPreprint
Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., & Kais, S. (2021) · arXiv (Cornell University) · Cited by 3
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation (Rev.1)
Aktar, S., (NNSA), U. N. N. S. A., Bärtschi, A., Badawy, A. A., Oyen, D., & Eidenbenz, S. (2024) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/2350603 · Cited by 2
Revolutionizing Learning with Qubits
Bhusari, S., Badakh, A., Daine, K., Gagare, N., & Mutkule, P. R. (2025) · Wiley eBooks · doi:10.1002/9781394271429.ch9 · Cited by 2
Ghosh, K., & Ghosh, S. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.108.165408 · Cited by 1
Shin, M., Lee, S., Lee, J., Lee, M., Ji, D., Yeo, H., & Jeong, K. (2024) · Physical Review A · doi:10.1103/physreva.110.062418 · Cited by 1
Quantum Patch-Based Autoencoder for Anomaly Segmentation
Madeira, M. F., Poggiali, A., & Lorenz, J. M. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00039 · Cited by 2
Hybrid Quantum Machine Learning Model for Power Allocation in NOMA
Nandi, T., Baghel, A. K., Pathak, A. K., & Bhatia, V. (2024) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict64037.2024.10899724 · Cited by 1
Variational quantum Hamiltonian engineering
Zhao, B., & Fujii, K. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023123 · Cited by 1
Abiwardani, M. R., Ruskanda, F. Z., Syafalni, I., Mulyawan, R., & Higo, A. (2025) · IEEE Access · doi:10.1109/access.2025.3596031 · Cited by 1
Quantum Error Mitigation via Autoencoder Neural Networks
Lin, X.-D., Chang, H.-M., You, J.-S., & Hsu, H.-C. (2025) · IEEE International Conference on Quantum Control, Computing and Learning (qCCL) · doi:10.1109/qccl65142.2025.11158291 · Cited by 1
A black-box attack method of machine learning algorithms based on quantum autoencoders
Tan, D., Yan, L., Zhao, J., Chang, Y., & Zhang, S. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.131033 · Cited by 1
Hao, C., Ma, Q., Chen, Y., Zhang, H., & Qu, D. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00274-1 · Cited by 2
Power and Limitations of Distributed Quantum State Purification
Zhao, B., Chen, Y.-A., Zhao, X., Zhu, C., Chiribella, G., & Wang, X. (2026) · Physical Review Letters · doi:10.1103/3bb1-pmtp · Cited by 3
Manna, A. L. C., & Goswami, R. S. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae282a · Cited by 1
An Efficient Quantum Classifier Based on Hamiltonian Representations
Tiblias, F., Schroeder, A. K., Zhang, Y., Gachechiladze, M., & Gurevych, I. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13883-5_5 · Cited by 1
Symmetry-constrained QCNN for few-shot learning with polylogarithmic generalization bounds
Guo, Z., Huang, C., Ding, W., & Ma, H. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05071-x · Cited by 1
Kaur, M., Kumar, V., & Upadhyay, R. (2026) · Soft Computing · doi:10.1007/s00500-025-11045-5 · Cited by 1
Hybrid Quantum–Classical Models for Forecasting and Anomaly Detection on Edge IoT Devices
Hajjem, R., Azim, A., & Triki, M. (2025) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta66620.2025.11364711 · Cited by 1
Rizzo, A., Parmiggiani, N., Bulgarelli, A. A., Macaluso, A., Burigana, C., Burns, E., Cappelli, L., Cardone, V. F., Farsian, F., Gallego, S., et al. (2026) · Astronomy and Computing · doi:10.1016/j.ascom.2026.101090 · Cited by 1
Quantum computing for astrophysics and cosmology: Results from the INAF activities
Bulgarelli, A. A., Burigana, C., Cappelli, L., Cardone, V. F., Cavuoti, S., Farsian, F., Graziotti, I., Macaluso, A., Meneghetti, M., Murante, G., et al. (2026) · Astronomy and Computing · doi:10.1016/j.ascom.2026.101108 · Cited by 1
Ankalaki, S., Hukkeri, G. S., & Bhatnagar, S. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10553-3 · Cited by 3
Wang, C. W., Sam, M. I., Kuo, T.-L., Chen, N.-Y., & Li, T.-Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464301 · Cited by 1
Deep Variational Quantum Circuits with Barren-Plateau-Free Architectures
Zhang, K., Hsieh, M.-H., & Tao, D. (2026) · Artificial Intelligence Science and Engineering · doi:10.23919/aise.2026.000005 · Cited by 1
Hamiltonian expressibility for ansatz selection in variational quantum algorithms
Brozzi, F., Turati, G., Dacrema, M. F., Caruso, F., & Cremonesi, P. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00407-3 · Cited by 4
Unpredictability and entanglement in open quantum systems
Kazemi, J., & Weimer, H. (2023) · New Journal of Physics · doi:10.1088/1367-2630/acf151 · Cited by 2
Lai, H. (2023) · Acta Physica Sinica · doi:10.7498/aps.72.20230589 · Cited by 1
Ahmadkhaniha, A., & Bathaee, M. (2024) · Physical Review A · doi:10.1103/physreva.109.032620 · Cited by 1
Experimental quantum state compression from two identical qubits to a qutrit
Xu, Q., Zhou, L., Feng, T., Qiu, S.-F., Li, S.-W., Zhang, W.-J., Luo, H., & Zhou, X. (2024) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-023-2361-6 · Cited by 1
Towards an efficient variational quantum algorithm for solving linear equations
Xu, W.-S., Zhou, R., Li, Y., & Zhang, X.-X. (2024) · Communications in Theoretical Physics · doi:10.1088/1572-9494/ad597d · Cited by 1
Quantum Technology Improves Small-World Network Collective Dynamics for Secure AI Transactions
Jagan, S., Shrivas, S. B., Thomas, S., & Jha, S. S. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5832-0.ch008 · Cited by 1
Selective Qubit Utilization for Optimizing Quantum Data Compression based on Quantum State Error
Prasetiadi, A., & Mambo, M. (2024) · International Conference on Quantum Communications, Networking, and Computing (QCNC) · doi:10.1109/qcnc62729.2024.00023 · Cited by 1
Ding, X., Liu, F., Wang, W., Zhu, Y., Hou, Y., Huang, Y., Xu, J., & Shan, Z. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ada0a4 · Cited by 2
COMPARISON OF QUANTUM DEEP LEARNING METHODS FOR IMAGE CLASSIFICATION
Katı, B. E., Küçüksille, E. U., & Sarıman, G. (2025) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.1553326 · Cited by 1
Research on Quantum Circuit Learning Models for Molecular Dynamics Simulation
Nishida, Y. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5c03624 · Cited by 1
On a Possible Quantum Variational Autoencoder Circuit
Pramanik, S., & Chandra, M. G. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533801 · Cited by 1
Implementing Quantum-Kernel-Based Classifiers in the NISQ Era
Pillay, S. M., Sinayskiy, I., Jembere, E., & Petruccione, F. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-95070-5_17
On quantum ensembles of quantum classifiersPreprint
Abbas, A., Schuld, M., & Petruccione, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10833 · Cited by 1
Quantum transfer component analysis for domain adaptationPreprint
He, X., Lyu, C., Hsieh, M.-H., & Wang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.09113 · Cited by 3
Cascade Decoders-Based Autoencoders for Image ReconstructionPreprint
Li, H., Galayko, D., Trocan, M., & Sawan, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00002 · Cited by 1
Clustering and enhanced classification using a hybrid quantum autoencoder
Srikumar, M., Hill, C. D., & Hollenberg, L. C. L. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac3c53 · Cited by 32
Quantum Computing for Inference
Schuld, M., & Petruccione, F. (2018) · Quantum science and technology · doi:10.1007/978-3-319-96424-9_6
Quantum generative models for data generation
Sun, T., Wu, Y.-C., & Guo, G. (2021) · Acta Physica Sinica · doi:10.7498/aps.70.20210930 · Cited by 1
Fast suppression of classification error in variational quantum circuitsPreprint
Zhang, B., & Zhuang, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08026
Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning
Barzili, S. L. (2021) · Chapman University Digital Commons (Chapman University) · doi:10.36837/chapman.000317
A METHODOLOGY FOR ENCODER USING DATA COMPRESSION AND OBFUSCATION TECHNIQUES IN QUANTUM COMPUTING
Lokesh, B. S., Hariharan, Chaitanya, Karthik, U. G., Rakshith, P., & MIT Mysore, K. (2021) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2021.v06i06.046
Unravelling physics beyond the standard model with classical and quantum anomaly detectionPreprint
Schuhmacher, J., Boggia, L., Belis, V., Puljak, E., Grossi, M., Maurizio Pierini, B., Vallecorsa, S., Tacchino, F., Barkoutsos, P. K., & Tavernelli, I. (2023) · arXiv (Cornell University) · doi:10.3929/ethz-b-000643789
Cao, Z. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300033
Quantum kernel subspace alignment for unsupervised domain adaptation
He, X., & Du, F. (2023) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1145/3590003.3590054
大久保, 亮., UEHARA, R., Kumoi, G., Goto, M., & Yoshikai, T. (2023) · Total Quality Science · doi:10.17929/tqs.8.113
A radar data compression method based on autoencoder neural network and range encoding
Hu, Z., Yang, F., Qiao, X., & Li, F. (2023) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1117/12.2689784
Investigating quantum many-body systems with tensor networks, machine learning and quantum computers
Kottmann, K. (2022) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-379461
MVITA, M. J., Zulu, N. G., Thethwayo, B., & Makhamisa, S. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/eu6.20230207
Quantum Joint Distribution Adaptation on the Universal Quantum Computer
Du, F., & He, X. (2023) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai59366.2023.10332071
Asymptotic compression rate of quantum autoencoders
Kaltchenko, A. (2024) · SPIE Quantum Information Science, Sensing, and Computation · doi:10.1117/12.3013415
Design Considerations for Denoising Quantum Time Series Autoencoder
Cybulski, J. L., & Zając, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63778-0_18 · Cited by 1
Optimization Techniques of Quantum Neural Network for Image Classification
Karkera, P., & Srivaramangai, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-64836-6_41
Solving complex problems of mixed-binary bounded optimization based on quantum algorithms
Muhamediyeva, D. T., Vasiyeva, D., & Ganieva, N. A. (2024) · International Conference on Digital Technologies, Optics, and Materials Science (DTIEE) · doi:10.1117/12.3036914
Symmetry-guided gradient descent for quantum neural networks
Bian, K., Zhang, S., Meng, F., Zhang, W., & Dahlsten, O. (2024) · Physical Review A · doi:10.1103/physreva.110.022406
Applying Quantum Autoencoders for Time Series Anomaly DetectionPreprint
Frehner, R., & Stockinger, K. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.04154 · Cited by 1
Efficient Layer Optimizations for Deep Neural Networks
Anik, S. M., Kelso, K., & Lee, B. K. (2024) · International Journal of Soft Computing and Engineering · doi:10.35940/ijsce.e3650.14051124
A Black-Box Attack Methods of Machine Learning Algorithms Based on Quantum AutoencodersPreprint
Tan, D., Yan, L., Zhao, J., Chang, Y., & Zhang, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5043506
Generator and training technique in quantum generative adversarial networks
Ma, Q., Hao, C., Liu, Y., Si, N., Zhang, J., Chen, G., & Qu, D. (2024) · International Conference on Image Processing, Machine Learning and Pattern Recognition (IPMLPR) · doi:10.1145/3700906.3701003
Araujo, I. F., Oh, H., Rodríguez-Briones, N. A., & Park, D. K. (2025) · Quantum Science and Technology · doi:10.1088/2058-9565/adcd99
Quantum Transfer Learning: The Impact of Classical Preprocessing
Kölle, M., Maurer, J., Altmann, P. J., Sünkel, L., Stein, J., Hager, J., Zielinski, S., & Linnhoff–Popien, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87327-0_18 · Cited by 3
Haque, M. E., Tohidi, F., & Ulhaq, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0020-7.ch009
Safarova, L., Muhamediyeva, D., & Vasiyeva, D. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-90546-9_2
Quantum-Circuit Inspired Hybrid QCNN for UAV Based Crop Classification
Singh, I., Baidya, A., Farooq, U., & Singh, P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93694-4_32
Quantum Autoencoder-Driven Hamiltonian Reduction for VQE Applications
Khandelwal, A., Nirmal, M. R., & Chandra, M. G. (2025) · Quantum 2.0 Conference and Exhibition · doi:10.1364/quantum.2025.qtu3a.22
40516 Quantum Machine Learning for Sensor Fusion
Anoop, M. A., & Karthikeyan, P. (2025) · De Gruyter eBooks · doi:10.1515/9783111619323-016
Quantum Machine Learning (QML)
Natarajan, V. (2025) · Springer eBooks · doi:10.1007/978-981-96-5051-4_5
Quantum Machine Learning for AGI: Redefining Intelligence Through Quantum Algorithms
Poornima, G., Pallavi, R., & Natarajan, R. (2025) · Springer eBooks · doi:10.1007/978-3-031-87931-9_13
Transforming machine learning: An exploration of quantum algorithms and their applications
Pawan, Y. V. R. N., & Kolla, B. P. (2025) · Advances in computers · doi:10.1016/bs.adcom.2025.06.005 · Cited by 2
Quantum Modelling of Time Series: Expressivity vs. TrainabilityPreprint
Cybulski, J. L., & Zając, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7253212/v1
Metadata
- Authors
- Romero, J., Olson, J. P., & Aspuru-Guzik, A.
- Affiliations
- Harvard University (US)
- Harvard University
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2017-08-18
- Volume
- 2
- Issue
- 4
- Pages
- 045001
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W2562234976
- Cited by
- 559
- References
- 31
- Citations by year
- 2026: 58 · 2025: 81 · 2024: 81 · 2023: 82 · 2022: 73 · 2021: 82 · 2020: 46 · 2019: 29 · 2018: 23 · 2017: 4
- FWCI
- 16.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Quantum Science and Technology (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (N00014-16-1-2008)
- Air Force Office of Scientific Research (FA9550-12-1- 0046)
- Army Research Office (W911NF-15-1-0256)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- quantum autoencoders, quantum data compression, quantum state compression, variational quantum circuits, quantum simulation, molecular Hamiltonian, quantum machine learning
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
