We propose a series of quantum algorithms for computing a wide range of quantum
entropies and distances, including the von Neumann entropy, quantum Rényi entropy, trace …

The performance of a quantum information processing protocol is ultimately judged by
distinguishability measures that quantify how distinguishable the actual result of the protocol …

Measuring the distinguishability between quantum states is a basic problem in quantum
information theory. In this paper, we develop optimal quantum algorithms that estimate both …

We propose a versatile privacy framework for quantum systems, termed quantum pufferfish
privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and …

Estimating statistical properties is fundamental in statistics and computer science. In this
paper, we propose a unified quantum algorithm framework for estimating properties of …

We investigate the computational complexity of estimating the trace of quantum state powers
tr (ρq) for an n-qubit mixed quantum state ρ, given its state-preparation circuit of size poly (n) …

Driven by exploring the power of quantum computation with a limited number of qubits, we
present a novel complete characterization for space-bounded quantum computation, which …

The estimation of quantum entropies and distance measures, such as von Neumann
entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such …

We study the estimation of relative entropy $ D (\rho\|\sigma) $ when $\sigma $ is known. We
show that the Cram\'{e} r-Rao type bound equals the relative varentropy. Our estimator …

Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum
state is a basic problem in quantum information. In this paper, we introduce a time-efficient …