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
Scalable Quantum Simulation of Molecular Energies
O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059
Self-citationA two-qubit logic gate in silicon
Veldhorst, M., Yang, C. H., Hwang, J. C. C., Huang, W., Dehollain, J. P., Muhonen, J. T., Simmons, S., Laucht, A., Hudson, F. E., Itoh, K. M., et al. (2015) · Nature · doi:10.1038/nature15263 · Cited by 943
Synthesis of quantum-logic circuits
Shende, V., Bullock, S. S., & Markov, I. L. (2006) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2005.855930 · Cited by 786
Progress towards practical quantum variational algorithms
Wecker, D., Hastings, M. B., & Troyer, M. (2015) · Physical Review A · doi:10.1103/physreva.92.042303 · Cited by 716
Efficient Variational Quantum Simulator Incorporating Active Error Minimization
Li, Y., & Benjamin, S. C. (2017) · Physical Review X · doi:10.1103/physrevx.7.021050 · Cited by 686
The Bravyi-Kitaev transformation for quantum computation of electronic structure
Seeley, J. T., Richard, M. J., & Love, P. J. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4768229 · Cited by 585
Liou, C., Cheng, W., Liou, J.-W., & Liou, D.-R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.055 · Cited by 492
Peterson, K. A., Feller, D. F., & Dixon, D. A. (2012) · Theoretical Chemistry Accounts · doi:10.1007/s00214-011-1079-5 · Cited by 482
Optimal creation of entanglement using a two-qubit gate
Kraus, B., & Cirac, J. I. (2001) · Physical Review A · doi:10.1103/physreva.63.062309 · Cited by 432
Sequential Generation of Entangled Multiqubit States
Schön, C., Solano, E., Verstraete, F., Cirac, J. I., & Wolf, M. M. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.110503 · Cited by 373
The mother of all protocols: restructuring quantum information’s family tree
Abeyesinghe, A., Devetak, I., Hayden, P. M., & Winter, A. (2009) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2009.0202 · Cited by 247
Dupuis, F., Berta, M., Wullschleger, J., & Renner, R. (2014) · Communications in Mathematical Physics · doi:10.1007/s00220-014-1990-4 · Cited by 173
Realization of a programmable two-qubit quantum processor
Hanneke, D., Home, J., Jost, J. D., Amini, J., Leibfried, D. G., & Wineland, D. J. (2009) · Nature Physics · doi:10.1038/nphys1453 · Cited by 150
Sequential generation of matrix-product states in cavity QED
Schön, C., Hammerer, K., Wolf, M. M., Cirac, J. I., & Solano, E. (2007) · Physical Review A · doi:10.1103/physreva.75.032311 · Cited by 141
Modeling word perception using the Elman network
Liou, C., Huang, J.-C., & Yang, W.-C. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.04.030 · Cited by 138
The B ravyi– K itaev transformation: Properties and applications
Tranter, A., Sofia, S. E., Seeley, J. T., Kaicher, M., McClean, J. R., Babbush, R., Coveney, P. V., Mintert, F., Wilhelm, F. K., & Love, P. J. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24969 · Cited by 133
Preparing Projected Entangled Pair States on a Quantum Computer
Schwarz, M., Temme, K., & Verstraete, F. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.110502 · Cited by 55
One-Shot Lossy Quantum Data Compression
Datta, N., Renes, J. M., Renner, R., & Wilde, M. M. (2013) · IEEE Transactions on Information Theory · doi:10.1109/tit.2013.2283723 · Cited by 42
A strategy for quantum algorithm design assisted by machine learning
Bang, J., Ryu, J., Yoo, S., Pawłowski, M., & Lee, J. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/7/073017 · Cited by 33
Universal programmable quantum circuit schemes to emulate an operator
Daskin, A., Grama, A. Y., Kollias, G., & Kais, S. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4772185 · Cited by 30
Quantum learning by measurement and feedback
Gammelmark, S., & Mølmer, K. (2009) · New Journal of Physics · doi:10.1088/1367-2630/11/3/033017 · Cited by 30
Quantum expanders and the quantum entropy difference problemPreprint
Ben-Aroya, A., & Ta‐Shma, A. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.quant-ph/0702129 · Cited by 30
Research works printed but not linked: 1
- Sousa, 2007, Quantum Info. Comput., 7, 228
Cited by
Sorted by citations
Variational quantum algorithms
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00348-9 · Cited by 3,385
“[138] introduced a VQA for quantum autoencoding, with the goal of compressing quantum data.”
BackgroundInfluentialSupervised 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
Barren plateaus in quantum neural network training landscapes
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2018) · Nature Communications · doi:10.1038/s41467-018-07090-4 · Cited by 2,329
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 2,231
Self-citationQuantum 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
Self-citation
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
