ML-assisted QIS promises portable quantum systems

July 14, 2020 //By Jean-Pierre Joosting
ML-assisted QIS promises portable quantum systems
Researchers from the U.S. Army Combat Capabilities Development Command's Army Research Laboratory and Tulane University combined machine learning (ML) with quantum information science (QIS) using photon measurements to reconstruct the quantum state of an unknown system.

QIS uses the unique properties of microscopic quantum systems, such as single particles of light or individual atoms, to achieve powerful applications in communication, computing and sensing, which are either impossible or less efficient under conventional means.

"We wanted to apply machine learning to problems in QIS, as machine learning systems are capable of making predictions based on example data sets without explicit programming for the given task," said Dr. Brian Kirby, a scientist at the Army's corporate research laboratory. "Machine learning has excelled in recent years in fields such as computer vision, where a machine learning algorithm trained on large sets of pre-classified images can then correctly classify new images that it has never seen before.”

For example, banks often employ machine learning systems to read the handwriting on checks, despite the program never having seen that particular handwriting before. This image classification is similar to reconstructing quantum states from measurement data, researchers said.

"In image recognition, the machine learning algorithms try to decide if something is a car or a bike," said Tulane University researcher Dr. Sanjaya Lohani. "Machine learning systems can be just as effective at looking for particular features in measurement data that imply what sort of state it came from. In both cases, the input data can be considered as a two-dimensional array, and the ML system attempts to pick out particular features in the array."


In a robust tomography scheme with machine learning, noisy tomography measurements are fed to the convolutional neural network, which makes predictions of intermediate t-matrices as the outputs. At the end, the predicted matrices are inverted to reconstruct the pure density matrices for the given noisy measurements. Image courtesy of the U.S. Army.


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