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Physical Sciences › Computer Science › Artificial Intelligence
Power of data in quantum machine learning
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Paper Summary
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Quantum Machine Learning: Data is King, Even for Quantum Data!
This paper demonstrates that classical machine learning algorithms, when provided with sufficient data, can effectively predict the output of quantum models, even those based on classically hard-to-compute quantum circuits. It introduces projected quantum kernels, which demonstrate significant prediction advantage over classical models on engineered datasets in numerical experiments up to 30 qubits.
Possible Conflicts of Interest
The authors are affiliated with Google Quantum AI, which has a vested interest in the advancement of quantum machine learning.
Identified Weaknesses
Limited generalizability of engineered datasets
The engineered datasets, while useful for demonstrating theoretical separations, may not accurately reflect real-world data distributions, limiting the generalizability of the findings.
Limited qubit number in numerical studies
While the largest scale quantum machine learning simulations performed to date, the largest number of qubits considered (30) is still relatively small compared to the scale required for true quantum advantage.
Specific encoding and model dependence of POK
The reliance on specific encodings and quantum models could restrict the broader applicability of the projected quantum kernel method to diverse machine learning tasks.
Rating Explanation
This paper provides valuable theoretical and numerical insights into the role of data in quantum machine learning, demonstrating that classical methods can be surprisingly powerful even on quantum datasets. The introduction of projected quantum kernels and large-scale numerical studies are significant contributions. However, the reliance on engineered datasets and limitations in qubit number slightly lowers the rating.
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Original Title:
Power of data in quantum machine learning
File Name:
s41467-021-22539-9.pdf
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File Size:
1.84 MB
Uploaded:
July 14, 2025 at 05:25 PM
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