Enhanced physics-informed neural networks with Augmented Lagrangian relaxation method (AL-PINNs)

Son, Hwijae; Cho, Sung Woong; Hwang, Hyung Ju

doi:10.1016/j.neucom.2023.126424

Cited by 18 publications

(1 citation statement)

References 23 publications

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“…This technique necessitates incorporating several constraints, but it entails establishing a specific dependence on the problem at hand. Other researchers achieve a certain level of independence concerning the set of weights in (5) through the application of the Augmented Lagrangian method [75] which involves the update of the multipliers during training in accordance with the degree of violation for each respective condition.…”

Section: Data Availability Statementmentioning

confidence: 99%

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Ferrer-Sánchez,

Flores-Garrigos,

Hernani-Morales

et al. 2024

Mach. Learn.: Sci. Technol.

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A novel methodology that leverages Physics-Informed Neural Networks to optimize quantum circuits in systems with NQ qubits by addressing the counterdiabatic protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate counterdiabatic terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing H2 molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.

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Section: Data Availability Statementmentioning

confidence: 99%