| Autor: |
Kholili, M. Jauhar, Muslim, Roni, Nugraha, Ahmad R. T. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2906 Issue 1, p1-10, 10p |
| Abstrakt: |
We investigate the implementation of a classical algorithm inspired by the quantum neural networks to solve a Bose-Hubbard-like system within the epistemically-restricted phase-space representation. The quantum states are expressed with the continuous variable model that intuitively represents the wavelike properties of a quantum system. We use the first quantization formalism to describe bosons in a double well potential. We utilize a fully-connected feedforward neural network that enacts several layers of Gaussian transformations and nonlinear activation functions to find the minimum energy of the system. A stochastic Monte Carlo method with Metropolis sampling generates a set of phase-space variables transformed iteratively by the neural network to reach the lowest possible energy. A gradient descent algorithm attempts to optimize the parameters of the Gaussian gates to reach the ground state energy of the system. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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