We apply a variational ansatz based on neural networks to the problem of spin-$\frac{1}{2}$ fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during “training”.

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• Fermions
• Few-body systems
• 1-dimensional systems
• Quantum many-body systems
• Approximation methods for many-body systems
• Artificial neural networks
• Machine learning