Quantum variational algorithms are swamped with traps
Implementations of shallow quantum machine learning models are a promising application of near-term quantum computers, but rigorous results on their trainability are sparse. Here, the authors demonstrate settings where such models are untrainable.
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-35364-5 |