Sample-Optimal Quantum Estimators for Pure-State Trace Distance and Fidelity via Samplizer

TL;DR

This paper introduces a quantum algorithm that estimates the trace distance and fidelity between pure quantum states with optimal sample complexity, significantly improving previous methods and utilizing a novel pure state samplizer.

Contribution

The paper presents the first optimal sample complexity quantum algorithms for estimating trace distance and fidelity between pure states, using a new pure state samplizer.

Findings

Achieves the optimal sample complexity of Θ(1/ε²) for estimating trace distance and fidelity.

Introduces a new pure state samplizer that is optimal and converts quantum query algorithms into sample algorithms.

Improves previous folklore bounds from O(1/ε⁴) to Θ(1/ε²).

Abstract

Trace distance and infidelity (induced by square root fidelity), as basic measures of the closeness of quantum states, are commonly used in quantum state discrimination, certification, and tomography. However, the sample complexity for their estimation still remains open. In this paper, we solve this problem for pure states. We present a quantum algorithm that estimates the trace distance and square root fidelity between pure states to within additive error $\varepsilon$, given sample access to their identical copies. Our algorithm achieves the optimal sample complexity $\Theta(1/\varepsilon^2)$, improving the long-standing folklore $O(1/\varepsilon^4)$. Our algorithm is composed of a samplized phase estimation of the product of two Householder reflections. Notably, an improved (multi-)samplizer for pure states is used as an algorithmic tool in our construction, through which any quantum query algorithm using $Q$ queries to the reflection operator about a pure state $|\psi\rangle$ can be converted to a $\delta$-close (in the diamond norm) quantum sample algorithm using $\Theta(Q^2/\delta)$ samples of $|\psi\rangle$. This samplizer for pure states is shown to be optimal.