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Cited by 559Quantum Computing Algorithms and ArchitectureQuantum many-body systemsQuantum Information and Cryptography

Quantum autoencoders for efficient compression of quantum data

Open access
Romero, Jonathan, Olson, Jonathan P, Aspuru-Guzik, Alan·2017·Quantum Science and Technology
DOI 10.1088/2058-9565/aa8072
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Abstract

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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Registry

Crossref · 10.1088/2058-9565/aa8072

Cites

32 of 32 references verified0 citation errors · 0 not checkable

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  • This classifier approach has been used in generalization and classification tasks, with different ways of embedding classical data into quantum states and an experimental demonstration of variational classification.

    “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]

    Section 4, “Application to quantum simulation” (page unavailable), reports testing a trained quantum autoencoder on 44 ground states outside its training set, supporting generalization to new states in a compression task. The paper does not establish that this autoencoder is a classifier or that it performs classification. The citing sentence assigns its embedding and experimental-classification statements to other references, not specifically to this source. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • In QML, state preparation is critical for pre-processing and encoding classical data into a quantum state.

    “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]

    Section 3 describes state preparation as part of a quantum–classical machine-learning procedure, and Section 5 describes using a quantum autoencoder to prepare quantum states. Neither passage says that state preparation pre-processes and encodes classical data into quantum states; the paper discusses preparing and compressing quantum-state inputs. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The quantum autoencoder (QAE) is a VQA for the compression of data on a quantum computer.

    “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

  • Canonical transformation theory and perturbative explicit correlated corrections are other techniques that could be implemented using the strategy of combining VQE with classical algorithms.

    “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

Flagged first, then by citations

Research works printed but not linked: 8

  1. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2016 arXiv:1611.09347 · arXiv
  2. Gómez-Bombarelli R, Duvenaud D, Hernández-Lobato J M, Aguilera-Iparraguirre J, Hirzel T D, Adams R P and Aspuru-Guzik A 2016 arXiv:1610.02415 · arXiv
  3. Wan K H, Dahlsten O, Kristjánsson H, Gardner R and Kim M 2016 arXiv:1612.01045 · arXiv
  4. Wilde M M 2013 Quantum Information Theory 1st edn (New York: Cambridge University Press)
  5. Nielsen M A and Chuang I L 2011 Quantum Computation and Quantum Information: 10th Anniversary Edition (New York: Cambridge University Press)
  6. Santagati R et al 2016 arXiv:1611.03511 · arXiv
  7. Bishop C M 2006 Pattern Recognition and Machine Learning (Information Science and Statistics) (Secaucus, NJ, USA: Springer-Verlag New York, Inc.)
  8. Helgaker T, Olsen J and Jorgensen P 2013 Molecular Electronic Structure Theory (Chichester: Wiley)

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Related

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)
DOI
10.1088/2058-9565/aa8072
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
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 › Computer Science › Artificial Intelligence › 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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