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Found 7 papers, 2 papers with code
Learning entanglement breakdown as a phase transition by confusion
Then we extend the way of using the 'learning by confusion' scheme for recognizing whether an arbitrary given state is entangled or separable.
Hybrid quantum-classical machine learning for generative chemistry and drug design
Deep generative chemistry models emerge as powerful tools to expedite drug discovery.
Learning phase transitions in ferrimagnetic GdFeCo alloys
The approach for finding phase transitions in the system is based on the `learning by confusion' scheme, which allows one to characterize phase transitions using a universal $W$-shape.
Materials Science Computational Physics Quantum Physics
Tomographic and entropic analysis of modulated signals
We study an application of the quantum tomography framework for the time-frequency analysis of modulated signals.
Experimental quantum homodyne tomography via machine learning
Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing.
Lightweight authentication for quantum key distribution
Reducing the portion of quantum-generated secret keys, that is consumed during the authentication procedure, is of significant importance for improving the performance of QKD systems.
Quantum Physics Cryptography and Security Information Theory Information Theory
Revealing quantum chaos with machine learning
Understanding properties of quantum matter is an outstanding challenge in science.
