Entropy measures quantify the amount of information and correlation present in a quantum
system. In practice, when the quantum state is unknown and only copies thereof are …

For two unknown mixed quantum states and in an-dimensional Hilbert space, computing
their fidelity is a basic problem with many important applications in quantum computing and …

Fidelity is a fundamental measure for the closeness of two quantum states, which is
important both from a theoretical and a practical point of view. Yet, in general, it is difficult to …

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

We propose a method of quantum machine learning called quantum mutual information
neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual …

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

In quantum information, trace distance is a basic metric of distinguishability between
quantum states. However, there is no known efficient approach to estimate the value of trace …

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

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 …

Quantum computing can provide speedups in solving many problems as the evolution of a
quantum system is described by a unitary operator in an exponentially large Hilbert space …