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…
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…
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…
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 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…