\'Eliv\'agar: Efficient Quantum Circuit Search for Classification

TL;DR

Eliv'agar is a resource-efficient quantum circuit search framework that improves noise robustness and classification accuracy in quantum machine learning by innovating in search space, algorithm, and evaluation strategies.

Contribution

It introduces a novel, noise-guided quantum circuit search method that addresses limitations of classical-inspired approaches, reducing costs and improving performance.

Findings

Achieves 5.3% higher accuracy on QML tasks

Provides 271x faster search compared to existing methods

Demonstrates effectiveness on 12 real quantum devices

Abstract

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for performant QML circuits that are also robust to hardware noise, they directly adopt designs from classical Neural Architecture Search (NAS) that are misaligned with the unique constraints of quantum hardware, resulting in high search overheads and severe performance bottlenecks. We present \'Eliv\'agar, a novel resource-efficient, noise-guided QCS framework. \'Eliv\'agar innovates in all three major aspects of QCS -- search space, search algorithm and candidate evaluation strategy -- to address the design flaws in current classically-inspired QCS methods. \'Eliv\'agar achieves hardware-efficiency and avoids an expensive circuit-mapping co-search via noise- and device topology-aware candidate generation. By introducing two cheap-to-compute predictors, Clifford noise resilience and Representational capacity, \'Eliv\'agar decouples the evaluation of noise robustness and performance, enabling early rejection of low-fidelity circuits and reducing circuit evaluation costs. Due to its resource-efficiency, \'Eliv\'agar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of \'Eliv\'agar on 12 real quantum devices and 9 QML applications, \'Eliv\'agar achieves 5.3% higher accuracy and a 271$\times$ speedup compared to state-of-the-art QCS methods.