New machine learning framework in quantum information processing

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. Credit: U.S. Army image

A new machine learning framework could pave the way for small, mobile quantum networks.

Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and Tulane University combined machine learning with Quantum Information Science (QIS), using photon measurements to reconstruct the quantum state of an unknown system.

QIS is a rapidly advancing field that exploits 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.

To characterize an unknown quantum system, the research team used Quantum State Tomography (QST). They prepared and measured identical unknown quantum system, and used a complicated computational process to determine the quantum system most consistent with the measurement results; however, researchers will need to develop alternative methods to process the classical information associated with quantum information protocols.

The team simulated familiar sources of error in measurement, such as misaligned optical elements, and used these to train the machine learning system. The researchers further tested their system when measurements were not just noisy but completely missing. Notably, the team outperformed conventional state reconstruction methods in each situation while requiring fewer computational resources. (Techxplore)

Read more.