Search Results for author:
Found 5 papers, 0 papers with code
Learning quantum properties from short-range correlations using multi-task networks
Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks.
Quantum Similarity Testing with Convolutional Neural Networks
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous-variable quantum systems.
Flexible learning of quantum states with generative query neural networks
Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized.
Efficient verification of continuous-variable quantum states and devices without assuming identical and independent operations
To successfully demonstrate these protocols, an essential step is the certification of multimode continuous-variable quantum states and quantum devices.
Quantum Physics
Multiple uncertainty relation for accelerated quantum information
The uncertainty principle, first introduced by Heisenberg in inertial frames, clearly distinguishes quantum theories from classical mechanics.
Quantum Physics General Relativity and Quantum Cosmology
