paper-with-me

홈 › Papers

Quantum Dynamics with Time-Dependent Neural Quantum States

2025-09-29 · Alejandro Romero-Ros, Javier Rozalén Sarmiento, Arnau Rios arxiv

We present proof-of-principle time-dependent neural quantum state (NQS) simulations to illustrate the ability of this approach to effectively capture key aspects of quantum dynamics in the continuum. NQS leverage the parameterization of the wave function with neural-network architectures. Here, we put NQS to the test by solving the quantum harmonic oscillator. We obtain the ground state and perform coherent state and breathing mode dynamics. Our results are benchmarked against analytical solutions, showcasing an excellent agreement.

📄 PDF Abstract BibTeX arXiv:2509.24865

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Neural Quantum Propagators for Driven-Dissipative Quantum Dynamics

2024-10-21 · Jiaji Zhang, Carlos L. Benavides-Riveros, Lipeng Chen

Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with s…

Scaling of neural-network quantum states for time evolution

2021-04-21 · Sheng-Hsuan Lin, Frank Pollmann

Simulating quantum many-body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space. Artificial neural networks have recently been introduced as a new tool to approxim…

Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model

2026-02-25 · Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima arxiv

Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probabilistic models (QuDDPMs) enable efficien…

Scalable quantum dynamics compilation via quantum machine learning

2024-09-24 · Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla, Lukasz Cincio 외

Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared …

Out-of-Distribution GeneralizationQuantum Machine Learning

Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics

2023-08-28 · Apimuk Sornsaeng, Ninnat Dangniam, Thiparat Chotibut

Next Generation Reservoir Computing (NG-RC) is a modern class of model-free machine learning that enables an accurate forecasting of time series data generated by dynamical systems. We demonstrate that NG-RC can accurate…

Time Series