Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Wang, Youle"'
Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground state preparation scheme with shallow variational warm-start to tackle
Externí odkaz:
http://arxiv.org/abs/2303.11204
Publikováno v:
Physical Review A 108 (6), 062413, 2023
Quantum computing can provide speedups in solving many problems as the evolution of a quantum system is described by a unitary operator in an exponentially large Hilbert space. Such unitary operators change the phase of their eigenstates and make qua
Externí odkaz:
http://arxiv.org/abs/2209.14278
Publikováno v:
Physical Review Applied 19, 044041, 2023
The von Neumann and quantum R\'enyi entropies characterize fundamental properties of quantum systems and lead to theoretical and practical applications in many fields. Quantum algorithms for estimating quantum entropies, using a quantum query model t
Externí odkaz:
http://arxiv.org/abs/2203.02386
Publikováno v:
In Computational and Structural Biotechnology Reports December 2024 1
Publikováno v:
Science China Information Sciences volume 66, Article number: 129502 (2023)
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators. In this paper, we propose a hybrid quantum-classical Hamiltonian learning algorithm to find the coefficients of the Pauli operator components of the Hamil
Externí odkaz:
http://arxiv.org/abs/2103.01061
Publikováno v:
Quantum 5, 483 (2021)
Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algo
Externí odkaz:
http://arxiv.org/abs/2006.02336
Publikováno v:
Phys. Rev. Applied 16, 054035, 2021
The preparation of quantum Gibbs state is an essential part of quantum computation and has wide-ranging applications in various areas, including quantum simulation, quantum optimization, and quantum machine learning. In this paper, we propose variati
Externí odkaz:
http://arxiv.org/abs/2005.08797
We propose a quantum data fitting algorithm for non-sparse matrices, which is based on the Quantum Singular Value Estimation (QSVE) subroutine and a novel efficient method for recovering the signs of eigenvalues. Our algorithm generalizes the quantum
Externí odkaz:
http://arxiv.org/abs/1907.06949
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Publikováno v:
In Applied Thermal Engineering October 2022 215