Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
Open accessAbstract
With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.
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A local version of the autoencoder cost function was proposed and shown to train well for large-scale problems.
“Recently, a local version of this cost function was also proposed and was shown to train well for large-scale problems [166].”
From Variational quantum algorithms (2021) · cites this paper as [166]
The supplied passage labeled Abstract describes a different autoencoder cost function based on junk-mode occupation, and another reports training a device with that function. The experiment compressed qutrits to qubits; our copy does not show that this cost function was proposed as a local version or trained well on large-scale problems. (LLM, conf=0.85)
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Self-citation
- Authors
- Pepper, A., Tischler, N., & Pryde, G. J.
- Affiliations
- Griffith University (AU)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2019-02-11
- Volume
- 122
- Issue
- 6
- Pages
- 060501-060501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2952384119
- Cited by
- 97
- References
- 26
- Citations by year
- 2026: 1 · 2025: 15 · 2024: 16 · 2023: 16 · 2022: 17 · 2021: 20 · 2020: 8 · 2019: 4
- FWCI
- 5.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (DP160101911)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum autoencoders, quantum data compression, lossless compression, robustness to disturbances
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