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Quantum autoencoders for efficient compression of quantum data
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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.
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“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]
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“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]
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“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
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“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]
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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
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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
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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.
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