Scalable Quantum Architecture Search via Landscape Analysis

TL;DR

This paper presents a scalable, training-free quantum architecture search method that uses landscape fluctuation analysis to efficiently identify high-performance quantum circuits, reducing resource use and enabling large-scale quantum simulations.

Contribution

The authors introduce a novel landscape analysis-based framework for quantum architecture search that predicts circuit learnability without training, improving scalability and efficiency.

Findings

Successfully applied to 50-qubit quantum many-body simulation.

Achieves higher accuracy with fewer gates.

Consumes significantly less classical resources than previous methods.

Abstract

Balancing trainability and expressibility is a central challenge in variational quantum computing, and quantum architecture search (QAS) plays a pivotal role by automatically designing problem-specific parameterized circuits that address this trade-off. In this work, we introduce a scalable, training-free QAS framework that efficiently explores and evaluates quantum circuits through landscape fluctuation analysis. This analysis captures key characteristics of the cost function landscape, enabling accurate prediction of circuit learnability without costly training. By combining this metric with a streamlined two-level search strategy, our approach identifies high-performance, large-scale circuits with higher accuracy and fewer gates. We further demonstrate the practicality and scalability of our method, achieving significantly lower classical resource consumption compared to prior work. Notably, our framework attains robust performance on a challenging 50-qubit quantum many-body simulation, highlighting its potential for addressing complex quantum problems.