Resource-Optimized Grouping Shadow for Efficient Energy Estimation

TL;DR

The paper presents ROGS, a resource-optimized grouping shadow method that reduces measurement costs for quantum energy estimation by using a novel grouping strategy and convex optimization.

Contribution

Introduction of ROGS, a new algorithm that minimizes measurement resources for quantum Hamiltonian energy estimation through optimized grouping and resource allocation.

Findings

ROGS requires fewer quantum circuits for accurate estimation.

ROGS outperforms existing methods in measurement efficiency.

Numerical experiments validate the effectiveness of ROGS.

Abstract

The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing. We introduce a Resource-Optimized Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer unique quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers.