Neural-network quantum state tomography
Dominik Koutný, L. Motka, Z. Hradil, J. Řeháček, L. L. Sánchez-Soto
Abstract
We revisit the application of neural networks to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.
Topics & Concepts
Artificial neural networkComputer scienceQuantum stateQuantumQuantum tomographyState (computer science)Artificial intelligenceConstraint (computer-aided design)TomographyNoise (video)Topology (electrical circuits)AlgorithmMathematicsImage (mathematics)PhysicsQuantum mechanicsEngineeringOpticsElectrical engineeringGeometryQuantum Information and CryptographyQuantum Computing Algorithms and ArchitectureNeural Networks and Reservoir Computing