The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Towards quantum machine learning with tensor networks
Open accessAbstract
Abstract Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks, that could already have benefits with such near-term devices. The result is a unified framework in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes in which, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. We demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a hybrid quantum-classical optimization procedure that could be carried out on quantum hardware today, and testing the noise resilience of the trained model.
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Cited by
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517
Quantum Algorithms for Quantum Chemistry and Quantum Materials Science
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Matrix product states and projected entangled pair states: Concepts, symmetries, theorems
Cirac, J. I., Pérez-García, D., Schuch, N., & Verstraete, F. (2021) · Reviews of Modern Physics · doi:10.1103/revmodphys.93.045003 · Cited by 769
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M. S., Alonso-Linaje, G., AkashNarayanan, B., Asadi, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1811.04968 · Cited by 732
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658
Metadata
- Authors
- Huggins, W., Patil, P., Mitchell, B., Whaley, K. B., & Stoudenmire, E. M.
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2019-01-09
- Volume
- 4
- Issue
- 2
- Pages
- 024001
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W2794602324
- Cited by
- 366
- References
- 28
- FWCI
- 17.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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