Machine learning for characterization of quantum states and processes (Conference Presentation)

Aleksey Fedorov

doi:10.1117/12.2555915

30 March 2020 Machine learning for characterization of quantum states and processes (Conference Presentation)

Aleksey Fedorov

Proceedings Volume 11347, Quantum Technologies 2020; 113470B (2020) https://doi.org/10.1117/12.2555915
Event: SPIE Photonics Europe, 2020, Online Only

Abstract

Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for large and complex quantum systems. Here we realize and experimentally demonstrate a method for complete characterization of a quantum harmonic oscillator based on an artificial neural network known as the restricted Boltzmann machine. We apply the method to optical homodyne tomography and show it to allow full estimation of quantum states based on a smaller amount of experimental data compared to state-of-the-art methods. We link this advantage to reduced overfitting. Although our experiment is in the optical domain, our method provides a way of exploring quantum resources in a broad class of large-scale physical systems, such as superconducting circuits, atomic and molecular ensembles, and optomechanical systems.

Conference Presentation

Citation Download Citation

Aleksey Fedorov "Machine learning for characterization of quantum states and processes (Conference Presentation)", Proc. SPIE 11347, Quantum Technologies 2020, 113470B (30 March 2020); https://doi.org/10.1117/12.2555915

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