5 citations · 8 across the 7 of their papers we have counts for
9 papers
Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo +1
Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogenei…
Universality of Many-body Projected Ensemble for Learning Quantum Data Distribution
Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo +1
Generating quantum data by learning the underlying quantum distribution poses challenges in both theoretical and practical scenarios, yet it is a critical task for understanding qu…
Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo +1
Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probab…
Provably scalable learning from quantum many-body data via locality
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, potentially solving certain classically intra…
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…
Quantum Curriculum Learning
Quoc Hoan Tran, Yasuhiro Endo, Hirotaka Oshima
Quantum machine learning (QML) requires significant quantum resources to address practical real-world problems. When the underlying quantum information exhibits hierarchical struct…
Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
Koki Chinzei, Shinichiro Yamano, Quoc Hoan Tran +2
Quantum neural networks (QNNs) require an efficient training algorithm to achieve practical quantum advantages. A promising approach is gradient-based optimization, where gradients…