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Found 9 papers, 4 papers with code
Hardware-efficient learning of quantum many-body states
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science.
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.
Enhancing Generative Models via Quantum Correlations
Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning.
Quantum Sampling Algorithms for Near-Term Devices
Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning.
Quantum Physics Statistical Mechanics
Experimental demonstration of memory-enhanced quantum communication
The ability to communicate quantum information over long distances is of central importance in quantum science and engineering.
Quantum Physics
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
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
We provide an in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations.
Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics
Quantum Convolutional Neural Networks
We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks (CNN).
Quantum Physics Strongly Correlated Electrons
