4 papers
Learning quantum many-body data locally: A provably scalable framework
Koki Chinzei, Quoc Hoan Tran, Norifumi Matsumoto +2
Machine learning (ML) holds great promise for extracting insights from complex quantum many-body data obtained in quantum experiments. This approach can efficiently solve certain q…
Iterative Quantum Feature Maps
Nasa Matsumoto, Quoc Hoan Tran, Koki Chinzei +2
Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have…
Molecular Quantum Transformer
Yuichi Kamata, Quoc Hoan Tran, Yasuhiro Endo +1
The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges such a…
Resource-efficient equivariant quantum convolutional neural networks
Koki Chinzei, Quoc Hoan Tran, Yasuhiro Endo +1
Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities. Despite theoretical developments in eq…