The Transverse Field Ising Model (TFIM) is an important quantum many-body model used to study the competition between spin-spin interactions and quantum fluctuations generated by a transverse field. Due to the exponential growth of the Hilbert space with increasing system size, numerical methods quickly become computationally infeasible motivating the use of approximate approaches. In this work Neural Quantum States were used as a variational method to approximate the ground-state properties of the one-dimensional TFIM. The NQS was represented using a feedforward Neural Network and optimized through Monte Carlo sampling together with stochastic reconfiguration. The implementation was done through the NetKet framework. The obtained results showed stable convergence of the variational energy and good agreement with exact diagonalization for the smaller system. The quantum phase transition was observed through the order parameter  and spin-spin correlations. Finite-size effects were also observed when comparing the two system sizes. The smaller system more closely approaches the symmetric cat-like ground-state. In contrast, the larger system tends to remain near one symmetry-broken configuration. As a result, the smaller system showed stronger spin expectation fluctuations and a smoother behavior near the phase transition.

The result demonstrates that the NQS approach is a good approximate method in studying the ground-state physics of the one-dimensional TFIM.