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Found 5 papers, 3 papers with code
A photonic chip-based machine learning approach for the prediction of molecular properties
Machine learning methods have revolutionized the discovery process of new molecules and materials.
Noisy intermediate-scale quantum algorithm for semidefinite programming
Unlike variational quantum eigensolvers, the classical optimization program of our eigensolver is convex, can be solved in polynomial time with the number of ansatz parameters and every local minimum is a global minimum.
Noisy intermediate-scale quantum (NISQ) algorithms
We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.
Engineering quantum current states with machine learning
With our approach, quantum current states characterized by a single winding number or entangled currents with two winding numbers can be prepared superseding the existing protocols.
Quantum Physics Mesoscale and Nanoscale Physics Quantum Gases
Andreev-reflection and Aharonov-Bohm dynamics in atomtronic circuits
We find that the steady-state is independent of the flux, however the actual time-dynamics depends on the flux.
Quantum Gases Quantum Physics
