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Found 7 papers, 2 papers with code
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms.
Digital-analog quantum learning on Rydberg atom arrays
We propose hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the rapidly scaling architectures of neutral atoms.
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning
Lastly, we comment on how this RL framework can be used in conjunction with physical quantum devices to tailor a code without explicit characterization of the noise model.
Differentiable Programming of Isometric Tensor Networks
Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation.
Hamiltonian-Driven Shadow Tomography of Quantum States
It relies on a unitary channel that efficiently scrambles the quantum information of the state to the measurement basis.
High-order Time-Reversal Symmetry Breaking Normal State
The phase locking, i. e., ordering of $\theta_-$, can take place in the phase fluctuation regime before the onset of superconductivity, i. e. when $\theta_+$ is disordered.
Superconductivity Strongly Correlated Electrons
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale.
