73 citations · 196 across the 11 of their papers we have counts for
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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…
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…
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…
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
Koki Chinzei, Quoc Hoan Tran, Kazunori Maruyama +2
The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable proble…