Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation

Michael H. Goerz; Sebastián C. Carrasco; Vladimir S. Malinovsky

doi:10.1364/QUANTUM.2022.QW2A.12

Quantum 2.0 Conference and Exhibition
Technical Digest Series (Optica Publishing Group, 2022),
paper QW2A.12
•https://doi.org/10.1364/QUANTUM.2022.QW2A.12

Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation

Michael H. Goerz, Sebastián C. Carrasco, and Vladimir S. Malinovsky

Quantum 2.0 2022

Boston, MA United States
13–16 June 2022
ISBN: 978-1-957171-11-1

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Poster Session II: Virtual (QW2A)

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M. H. Goerz, S. C. Carrasco, and V. S. Malinovsky, "Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation," in Quantum 2.0 Conference and Exhibition, Technical Digest Series (Optica Publishing Group, 2022), paper QW2A.12.

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Abstract

We present a computational framework for the direct optimization of measures of metrological gain without analytic gradients, such as the quantum Fisher information. The method is enabled by a new memory-efficient formulation of automatic differentiation.

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