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Integrating Neural Networks with a Quantum Simulator for State Reconstruction
Giacomo Torlai1,2,3, Brian Timar4, Evert P L van Nieuwenburg4
1Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA.
Physical Review Letters
|December 24, 2019
Summary
We reconstructed quantum many-body states from experimental data using a neural network and error mitigation. This approach extracts complex quantum information from programmable quantum simulators, enhancing future quantum hardware integration.
Area of Science:
- Quantum Information Science
- Machine Learning in Physics
- Quantum Simulation
Background:
- Quantum many-body state reconstruction is crucial for understanding complex quantum systems.
- Experimental data from quantum simulators often contain errors that obscure true quantum states.
Purpose of the Study:
- To develop a method for reconstructing quantum many-body states from experimental data.
- To integrate machine learning with quantum hardware for enhanced state reconstruction.
Main Methods:
- Utilized a neural-network model, specifically restricted Boltzmann machine wave functions.
- Applied a novel regularization technique to mitigate measurement errors in training data.
- Extracted wave functions from a Rydberg quantum simulator with 8-9 atoms.
Main Results:
- Successfully reconstructed quantum many-body states from experimental data.
- Captured one- and two-body observables beyond experimental accessibility.
- Computed sophisticated observables like Rényi mutual information.
Conclusions:
- Demonstrated the efficacy of neural networks for quantum state reconstruction.
- Showcased a method to mitigate experimental errors in quantum simulation data.
- Paved the way for integrating machine learning with intermediate-scale quantum hardware.
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