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.
Neural-network quantum state tomography
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Augments quantum state tomography with anytime-valid confidence sequences so that confidence sets contain the true state with user-specified probability at any stopping time.
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.
citing papers explorer
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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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Anytime-Valid Quantum State Tomography via Confidence Sequences
Augments quantum state tomography with anytime-valid confidence sequences so that confidence sets contain the true state with user-specified probability at any stopping time.
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Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.