SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search

Yize Sun, Jiarui Liu, Zixin Wu, Zifeng Ding, Yunpu Ma, Thomas Seidl, Volker Tresp

Keywords: QAS, DQAS, differentiable quantum architecture search

TL;DR: Our method, SA-DQAS, improves DQAS by using the self-attention mechanism. We show the effectiveness of this method through various experiments for JSSP and max-cut problems.

Abstract: We introduce SA-DQAS in this paper, a novel framework that enhances the gradient-based Differentiable Quantum Architecture Search (DQAS) with a self-attention mechanism, aimed at optimizing circuit design for Quantum Machine Learning (QML) challenges. Analogous to a sequence of words in a sentence, a quantum circuit can be viewed as a sequence of placeholders containing quantum gates. Unlike DQAS, each placeholder is independent, while the self-attention mechanism in SA-DQAS helps to capture relation and dependency information among each operation candidate placed on placeholders in a circuit. To evaluate and verify, we conduct experiments on job-shop scheduling problems (JSSP), Max-cut problems, quantum chemistry and quantum fidelity. Incorporating self-attention improves the stability and performance of the resulting quantum circuits and refines their structural design with higher noise resilience and fidelity. Our research demonstrates the first successful integration of self-attention with DQAS.

Submission Number: 20