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ShadowNet for Data-Centric Quantum System Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 14, 2026
Summary
This study introduces a data-centric approach for quantum system learning (QSL), combining classical shadows and neural networks. ShadowNet efficiently tackles quantum state tomography and fidelity estimation for large quantum systems.
Area of Science:
- Quantum Information Science
- Machine Learning for Quantum Systems
Background:
- The curse of dimensionality hinders understanding large quantum systems.
- Statistical learning methods like neural networks and classical shadows offer potential but have limitations.
- Neural networks face computational demands for data collection, while classical shadows cannot leverage prior data.
Purpose of the Study:
- To propose a data-centric learning paradigm to overcome limitations in quantum system learning (QSL).
- To develop a unified dataset construction rule for efficient QSL.
- To introduce ShadowNet, a model for quantum state tomography (QST) and direct fidelity estimation (DFE).
Main Methods:
- A data-centric learning paradigm integrating classical shadows and neural networks.
- A unified dataset construction rule using classical shadows and auxiliary quantum system information.
- ShadowNet implementation with convolutional and attention mechanisms for QSL tasks.
Main Results:
- ShadowNet efficiently and faithfully performs QST and DFE on quantum systems up to 60 qubits.
- The proposed method enhances classical shadows with limited state copies.
- Varied neural network architectures demonstrated impact on performance.
Conclusions:
- The data-centric approach shows immense potential for comprehending large quantum systems.
- ShadowNet offers an efficient and effective solution for pivotal QSL tasks.
- This paradigm advances the field of quantum system learning by combining data efficiency and learning capabilities.
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