Qisheng Wang

Uhlmann's theorem is a central result in quantum information theory, associating the closeness of two quantum states with that of their purifications. This theorem well characterizes the fundamental task of transforming a quantum state into another state via local operations on its subsystem. The optimal transformation for this task is called the Uhlmann transformation, which has broad applications in various fields; however, its quantum circuit implementation and computational cost have remained unclear. In this work, we fill this gap by proposing quantum query and sample algorithms that realize the Uhlmann transformation in the form of quantum circuits. These algorithms achieve exponential improvements in computational costs, including query and sample complexities, over naive approaches based on state measurements such as quantum state tomography, under certain computational models. We apply our algorithms to the square root fidelity estimation task and particularly show that our approach attains a better query complexity than the prior state-of-the-art. Furthermore, we discuss applications to several information-theoretic tasks, specifically, entanglement transmission, quantum state merging, and algorithmic implementation of the Petz recovery map, providing a comprehensive evaluation of the computational costs.