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Found 5 papers, 3 papers with code
Gradient-descent quantum process tomography by learning Kraus operators
The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods.
Ultimate quantum limit for amplification: a single atom in front of a mirror
We investigate three types of amplification processes for light fields coupling to an atom near the end of a one-dimensional semi-infinite waveguide.
Quantum Physics
Classification and reconstruction of optical quantum states with deep neural networks
For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state.
Quantum State Tomography with Conditional Generative Adversarial Networks
We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix.
Deep Q-learning decoder for depolarizing noise on the toric code
We present an AI-based decoding agent for quantum error correction of depolarizing noise on the toric code.
Quantum Physics Disordered Systems and Neural Networks
