73 citations · 196 across the 11 of their papers we have counts for
13 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…
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…
Criticality and Rigidity of Dissipative Discrete Time Crystals in Solids
Koki Chinzei, Tatsuhiko N. Ikeda
We consider a dissipative quantum Ising model periodically driven by a train of -pulses and investigate dissipative discrete time crystals (DTCs) in solids. In this model, the i…
Nonequilibrium steady states in the Floquet-Lindblad systems: van Vleck's high-frequency expansion approach
Tatsuhiko N. Ikeda, Koki Chinzei, Masahiro Sato
Nonequilibrium steady states (NESSs) in periodically driven dissipative quantum systems are vital in Floquet engineering. We develop a general theory for high-frequency drives with…
Time Crystals Protected by Floquet Dynamical Symmetry in Hubbard Models
Koki Chinzei, Tatsuhiko N. Ikeda
We investigate an unconventional symmetry in time-periodically driven systems, the Floquet dynamical symmetry (FDS). Unlike the usual symmetries, the FDS gives symmetry sectors tha…
Disorder Effects on the Origin of High-Order Harmonic Generation in Solids
Koki Chinzei, Tatsuhiko N. Ikeda
We consider noninteracting electrons coupled to laser fields, and study perturbatively the effects of the lattice potential involving disorder on the harmonic components of the ele…
Floquet-Theoretical Formulation and Analysis of High-Harmonic Generation in Solids
Tatsuhiko N. Ikeda, Koki Chinzei, Hirokazu Tsunetsugu
By using the Floquet eigenstates, we derive a formula to calculate the high-harmonic components of the electric current (HHC) in the setup where a monochromatic laser field is turn…