An online Riemannian gradient descent method for MPO-based quantum state tomography achieves linear convergence with quadratically scaling sample complexity and connects the problem to low TT-rank tensor completion.
The bloch vector for n-level systems
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An integrated neural compression and Cholesky quantum encoding method achieves robust reconstruction and classification performance in noisy quantum channels while bypassing full density matrix reconstruction.
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Online Riemannian Gradient Descent for Quantum State Tomography with Matrix Product Operators
An online Riemannian gradient descent method for MPO-based quantum state tomography achieves linear convergence with quadratically scaling sample complexity and connects the problem to low TT-rank tensor completion.
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End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise
An integrated neural compression and Cholesky quantum encoding method achieves robust reconstruction and classification performance in noisy quantum channels while bypassing full density matrix reconstruction.