Creating and concentrating quantum resource states in noisy environments using a quantum neural network

• Main Authors: Krisnanda, Tanjung, Ghosh, Sanjib, Paterek, Tomasz, Liew, Timothy Chi Hin
• 其他作者: School of Physical and Mathematical Sciences
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Science::Physics; Neural Network Applications; Quantum Neural Network
• 在線閱讀: https://hdl.handle.net/10356/157029; https://doi.org/10.21979/N9/LAKC6C
• 機構: Nanyang Technological University

Quantum information processing tasks require exotic quantum states as a prerequisite. They are usually prepared with many different methods tailored to the specific resource state. Here we provide a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes. The output of such a system is then superposed with the help of linear mixing where weights and phases are trained in order to obtain desired output quantum states. We explicitly show that our method is robust and can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states. Furthermore, the treatment includes energy decay in the system as well as dephasing and depolarization. Under these noisy conditions we show that the target states are achieved with high fidelity by tuning controllable parameters and providing sufficient strength to the driving of the quantum network. Finally, in very noisy systems, where noise is comparable to the driving strength, we show how to concentrate entanglement by mixing more states in a larger network.