# Short-depth circuits for efficient expectation value estimation

## TL;DR

This paper introduces a method combining a single quantum phase estimation step with classical post-processing to estimate expectation values more efficiently on near-term quantum devices, reducing circuit depth compared to traditional techniques.

## Contribution

It proposes a novel hybrid approach that lowers circuit depth while maintaining accuracy, improving expectation value estimation for quantum algorithms.

## Key findings

- Reduces circuit depth to rac{ ext{target accuracy}}{ ext{power}}rac{ ext{target accuracy}}{ ext{power}} with a single QPE step.
- Achieves fewer measurements than standard operator averaging for certain problem instances.
- Provides conditions under which the new method outperforms existing strategies.

## Abstract

The evaluation of expectation values $Tr\left[\rho O\right]$ for some pure state $\rho$ and Hermitian operator $O$ is of central importance in a variety of quantum algorithms. Near optimal techniques developed in the past require a number of measurements $N$ approaching the Heisenberg limit $N=\mathcal{O}\left(1/\epsilon\right)$ as a function of target accuracy $\epsilon$. The use of Quantum Phase Estimation requires however long circuit depths $C=\mathcal{O}\left(1/\epsilon\right)$ making their implementation difficult on near term noisy devices. The more direct strategy of Operator Averaging is usually preferred as it can be performed using $N=\mathcal{O}\left(1/\epsilon^2\right)$ measurements and no additional gates besides those needed for the state preparation. In this work we use a simple but realistic model to describe the bound state of a neutron and a proton (the deuteron) and show that the latter strategy can require an overly large number of measurement in order to achieve a reasonably small relative target accuracy $\epsilon_r$. We propose to overcome this problem using a single step of QPE and classical post-processing. This approach leads to a circuit depth $C=\mathcal{O}\left(\epsilon^\mu\right)$ (with $\mu\geq0$) and to a number of measurements $N=\mathcal{O}\left(1/\epsilon^{2+\nu}\right)$ for $0<\nu\leq1$. We provide detailed descriptions of two implementations of our strategy for $\nu=1$ and $\nu\approx0.5$ and derive appropriate conditions that a particular problem instance has to satisfy in order for our method to provide an advantage.