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

Area of Science:

• Quantum Information Science
• Quantum Computing
• Condensed Matter Physics

Background:

• Quantum information processing relies on preparing specific, often complex, quantum states.
• Current methods for preparing quantum states are typically specialized and resource-intensive.

Purpose of the Study:

• To develop a versatile and unified scheme for preparing diverse quantum states.
• To demonstrate the robustness of the proposed method under realistic noisy conditions.

Main Methods:

• Utilizing a driven quantum network of randomly-coupled fermionic nodes.
• Employing linear mixing with trainable weights and phases to superpose network outputs.
• Investigating the impact of energy decay, dephasing, and depolarization on state preparation.

Main Results:

• Successfully generated highly accurate maximally entangled, NOON, W, cluster, and discorded states.
• Achieved high fidelity state preparation even in the presence of significant energy decay, dephasing, and depolarization.
• Demonstrated entanglement concentration in highly noisy systems by increasing network size.

Conclusions:

• The proposed driven quantum network scheme offers a robust and unified approach to quantum state preparation.
• The method's adaptability and resilience to noise make it a promising tool for advancing quantum information processing.
• Entanglement concentration offers a strategy for overcoming noise limitations in future quantum technologies.