5 citations · 8 across the 8 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…
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…
Reducing QPU Calls in Variational Quantum Algorithms via Classical Offloading of Gradient Computation
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh +3
Variational quantum algorithms (VQAs) are a promising approach for leveraging near-term quantum computers, but trainability and quantum-resource costs remain major challenges. Here…
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…
Variational Denoising for Variational Quantum Eigensolver
Quoc Hoan Tran, Shinji Kikuchi, Hirotaka Oshima
The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable…