Machine learning for enhancing quantum state estimation

Ryan T. Glasser; Sanjaya Lohani; Brian T. Kirby; Michael Brodsky; Onur Danaci; Thomas A Searles

doi:10.1117/12.2586865

5 March 2021 Machine learning for enhancing quantum state estimation

Ryan T. Glasser, Sanjaya Lohani, Brian T. Kirby, Michael Brodsky, Onur Danaci, Thomas A Searles

Proceedings Volume 11700, Optical and Quantum Sensing and Precision Metrology; 117001D (2021) https://doi.org/10.1117/12.2586865
Event: SPIE OPTO, 2021, Online Only

Abstract

In this presentation, we show the efficacy of neural networks in reducing classical resources required for quantum state estimation. The developed methods achieve near-unity fidelities in reconstructed density matrices, and outperform Stokes reconstruction in a wide variety of scenarios.

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Ryan T. Glasser, Sanjaya Lohani, Brian T. Kirby, Michael Brodsky, Onur Danaci, and Thomas A Searles "Machine learning for enhancing quantum state estimation", Proc. SPIE 11700, Optical and Quantum Sensing and Precision Metrology, 117001D (5 March 2021); https://doi.org/10.1117/12.2586865

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Machine learning

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