Quantum landscape tomography for efficient single-gate optimization on quantum computers

TL;DR

This paper introduces quantum landscape tomography, a method leveraging tensor network theory to efficiently optimize quantum circuits by fully characterizing gate influences, improving optimization strategies for near-term quantum computers.

Contribution

It presents a novel quantum landscape tomography technique with two implementations, advancing circuit optimization through tensor network analysis and efficient measurement strategies.

Findings

Quantum landscape tomography effectively characterizes gate influence.

The Clifford tableaux implementation balances shot count and circuit number.

Numerical simulations show improved optimization over existing methods.

Abstract

Circuit optimization is a fundamental task for practical applications of near-term quantum computers. In this work we address this challenge through the powerful lenses of tensor network theory. Our approach involves the full characterization of the influence of individual gates on the entire circuit, a process we call quantum landscape tomography. We derive the necessary and sufficient requirements of this process and propose two implementations, respectively based on 2-unitary design and Clifford tableaux. The latter implementation strikes a convenient balance between the number of shots and the number of circuits needed for the tomography. Numerical simulations based on a realistic noise model demonstrate the advantage of our approach with respect to both gradient-free and gradient-based methods. Overall, our findings highlight the potential of quantum landscape tomography to enhance circuit optimization in near-term quantum computing applications.