Entanglement detection with classical deep neural networks.

Area of Science:

• Quantum mechanics
• Quantum information science

Background:

• Quantum entanglement is a fundamental phenomenon in quantum mechanics.
• Understanding and detecting entanglement is crucial for quantum technologies.

Purpose of the Study:

• To develop an autonomous method for detecting and classifying quantum entanglement.
• To assess the performance of this method in two- and three-qubit systems.

Main Methods:

• Utilized a multi-layer perceptron for entanglement detection.
• Applied the method to analyze two- and three-qubit quantum systems.

Main Results:

• Achieved nearly perfect accuracy in detecting entanglement in two-qubit systems.
• Obtained over 90% accuracy for three-qubit system detection.
• Successfully categorized three-qubit entangled states with up to 95% accuracy.

Conclusions:

• The developed autonomous method is effective for quantum entanglement detection and classification.
• The approach shows potential for scalability to larger quantum systems.
• This work contributes to advancements in quantum information processing applications.