Zobrazeno 1 - 10
of 210
pro vyhledávání: '"STOKES, JAMES"'
Autor:
Knitter, Oliver, Zhao, Dan, Stokes, James, Ganahl, Martin, Leichenauer, Stefan, Veerapaneni, Shravan
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum proble
Externí odkaz:
http://arxiv.org/abs/2411.03900
Although quantum computing holds promise to accelerate a wide range of computational tasks, the quantum simulation of quantum dynamics as originally envisaged by Feynman remains the most promising candidate for achieving quantum advantage. A less exp
Externí odkaz:
http://arxiv.org/abs/2407.08006
Neural networks are increasingly recognized as a powerful numerical solution technique for partial differential equations (PDEs) arising in diverse scientific computing domains, including quantum many-body physics. In the context of time-dependent PD
Externí odkaz:
http://arxiv.org/abs/2311.12239
Digital quantum simulation (DQS) of continuous-variable quantum systems in the position basis requires efficient implementation of diagonal unitaries approximating the time evolution operator generated by the potential energy function. In this work,
Externí odkaz:
http://arxiv.org/abs/2212.04942
Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function tha
Externí odkaz:
http://arxiv.org/abs/2211.03198
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resou
Externí odkaz:
http://arxiv.org/abs/2211.02929
Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of lar
Externí odkaz:
http://arxiv.org/abs/2208.05637
Variational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of partial differential equations (PDEs); namely, the real- and imaginar
Externí odkaz:
http://arxiv.org/abs/2207.10838
This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (
Externí odkaz:
http://arxiv.org/abs/2203.14824
Publikováno v:
Proceedings of the National Academy of Sciences, 119, e2122059119 (2022)
We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert space involving "hidden" additional fermion
Externí odkaz:
http://arxiv.org/abs/2111.10420