Zobrazeno 1 - 10
of 149
pro vyhledávání: '"LIU Jin-peng"'
Publikováno v:
Shipin yu jixie, Vol 39, Iss 1, Pp 85-90 (2023)
Objective: The fatigue strength of a new type of vertical retort under the cyclic load of heat engine was studied, especially the residual fatigue life of the pot body after the crack appeared and the factors affecting the fatigue crack propagation o
Externí odkaz:
https://doaj.org/article/3a48e21b44254b978c7cf65d4a3a2ded
Quantum-classical hybrid dynamics is crucial for accurately simulating complex systems where both quantum and classical behaviors need to be considered. However, coupling between classical and quantum degrees of freedom and the exponential growth of
Externí odkaz:
http://arxiv.org/abs/2408.00276
Simulation of physical systems is one of the most promising use cases of future digital quantum computers. In this work we systematically analyze the quantum circuit complexities of block encoding the discretized elliptic operators that arise extensi
Externí odkaz:
http://arxiv.org/abs/2407.18347
Autor:
Liu, Jin-Peng, Lin, Lin
The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics using quantum computers. This problem arises frequently in applications such as quantum control and spectroscopic computat
Externí odkaz:
http://arxiv.org/abs/2307.14441
Autor:
Liu, Junyu, Liu, Minzhao, Liu, Jin-Peng, Ye, Ziyu, Wang, Yunfei, Alexeev, Yuri, Eisert, Jens, Jiang, Liang
Publikováno v:
Nature Comm. 15, 434 (2024)
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tol
Externí odkaz:
http://arxiv.org/abs/2303.03428
Publikováno v:
Phys. Rev. Lett. 131, 150603 (2023)
We propose a simple method for simulating a general class of non-unitary dynamics as a linear combination of Hamiltonian simulation (LCHS) problems. LCHS does not rely on converting the problem into a dilated linear system problem, or on the spectral
Externí odkaz:
http://arxiv.org/abs/2303.01029
Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchronous-parallel (memory efficient) algorithms are necessary to minimize
Externí odkaz:
http://arxiv.org/abs/2211.09908
We study the limitations and fast-forwarding of quantum algorithms for linear ordinary differential equation (ODE) systems with a particular focus on non-quantum dynamics, where the coefficient matrix in the ODE is not anti-Hermitian or the ODE is in
Externí odkaz:
http://arxiv.org/abs/2211.05246
Publikováno v:
Advances in Neural Information Processing Systems (NeurIPS 2022) 35, 23205 (2022)
Given a convex function $f\colon\mathbb{R}^{d}\to\mathbb{R}$, the problem of sampling from a distribution $\propto e^{-f(x)}$ is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work,
Externí odkaz:
http://arxiv.org/abs/2210.06539
Nonlinear differential equations exhibit rich phenomena in many fields but are notoriously challenging to solve. Recently, Liu et al. [1] demonstrated the first efficient quantum algorithm for dissipative quadratic differential equations under the co
Externí odkaz:
http://arxiv.org/abs/2205.01141