Quantum state estimation

Sample-efficient estimation of nonlinear quantum state functions
Estimating nonlinear functions of quantum states is essential for quantum information processing tasks such as entropy quantification, …
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Sample-efficient estimation of nonlinear quantum state functions
Quantum Fidelity Estimation in the Resource Theory of Nonstabilizerness
Quantum fidelity estimation is essential for benchmarking quantum states and processes on noisy quantum devices. While stabilizer …
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Quantum Fidelity Estimation in the Resource Theory of Nonstabilizerness
Near-Optimal Simultaneous Estimation of Quantum State Moments
Estimating nonlinear properties such as Rényi entropies and observable-weighted moments serves as a central strategy for spectrum …
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Near-Optimal Simultaneous Estimation of Quantum State Moments
Retrieving Non-Linear Features from Noisy Quantum States
Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, …
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Retrieving Non-Linear Features from Noisy Quantum States
Quantum Phase Processing and Its Applications in Estimating Phase and Entropies
Quantum computing can provide speedups in solving many problems as the evolution of a quantum system is described by a unitary operator …
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Quantum Phase Processing and Its Applications in Estimating Phase and Entropies
Efficient information recovery from Pauli noise via classical shadow
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information …
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Efficient information recovery from Pauli noise via classical shadow
Quantum algorithms for estimating quantum entropies
The von Neumann and quantum Rényi entropies characterize fundamental properties of quantum systems and lead to theoretical and …
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Variational quantum algorithms for trace distance and fidelity estimation
Estimating the difference between quantum data is crucial in quantum computing. However, as typical characterizations of quantum data …
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More Practical and Adaptive Algorithms for Online Quantum State Learning
Online quantum state learning is a recently proposed problem by Aaronson et al. (2018), where the learner sequentially predicts …
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