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Found 5 papers, 3 papers with code
Machine learning discovery of new phases in programmable quantum simulator snapshots
Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions.
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
Parallel implementation of high-fidelity multi-qubit gates with neutral atoms
We report the implementation of universal two- and three-qubit entangling gates on neutral atom qubits encoded in long-lived hyperfine ground states.
Quantum Physics Quantum Gases
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known experimental errors.
Quantum Physics Quantum Gases
Probing entanglement in a many-body-localized system
The key to our understanding of this phenomenon lies in the system's entanglement, which is experimentally challenging to measure.
Quantum Gases Disordered Systems and Neural Networks Statistical Mechanics Atomic Physics
