Home  | Publications | SLW+24

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

MCML Authors

Link to Profile Thomas Seidl PI Matchmaking

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Link to Profile Volker Tresp

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

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, 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.

inproceedings

Differentiable Almost Everything @ICML 2024

Workshop Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024.

Authors

Y. Sun • J. Liu • Z. Wu • Z. Ding • Y. Ma • T. Seidl • V. Tresp

Links

PDF

Research Area

 A3 | Computational Models

BibTeXKey: SLW+24

Back to Top