(Phys.org) Scientists at Skoltech have applied deep learning to reconstruct quantum properties of optical systems.
Through a collaboration between the quantum optics research laboratories at Moscow State University, led by Sergey Kulik, and members of Skoltech’s Deep Quantum Laboratory of CPQM, led by Jacob Biamonte, the scientists have successfully applied machine learning to the state reconstruction problem.
The findings as an important first step toward the practical use of neural network architecture in a lab for improving quantum tomography with available quantum setups of noisy experimental data. Such quantum information processing is used ubiquitously in paradigmatic quantum devices for quantum computation and optimization. In the future, the researchers plan to address further challenges of upscaling quantum information devices.
Machine Learning Implemented for Quantum Optics
