Classical Quantum Optimization with Neural Network Quantum States

Understand

The classical simulation of quantum systems typically requires exponential resources.

• Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previously been intractable for existing exact numerical methods.
• Here, we demonstrate the utility of the variational representation of quantum states based on artificial neural networks for performing quantum optimization.
• We show empirically that this methodology achieves high approximation ratio solutions with polynomial classical computing resources for a range of instances of the Maximum Cut (MaxCut) problem whose solutions have been encoded into the ground state of quantum many-body systems up to and including 256 qubits.