Classical quantum optimization with neural network quantum states
Published in 33rd Conference on Neural Information Processing Systems (NeurIPS), Workshop of Machine Learning and the Physical Sciences, 2019
Recommended citation: Joseph Gomes, Keri M. McKiernan, Peter Eastman, Vijay S. Pande. "Classical Quantum Optimization with Neural Network Quantum States". In Neural Information Processing Systems, Workshop on Machine Learning for Physical Sciences (2019). https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_144.pdf
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.
Recommended citation: Joseph Gomes, Keri M. McKiernan, Peter Eastman, Vijay S. Pande. “Classical Quantum Optimization with Neural Network Quantum States”. In Neural Information Processing Systems, Workshop on Machine Learning for Physical Sciences (2019).