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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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Gujju, Y., Matsuo, A., & Raymond, R. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.067001 · Cited by 104
Towards quantum enhanced adversarial robustness in machine learning
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Minimizing Estimation Runtime on Noisy Quantum Computers
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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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