GSQAS: Graph Self-supervised Quantum Architecture Search
Zhimin He, Maijie Deng, Shenggen Zheng, Lvzhou Li, Haozhen Situ

TL;DR
GSQAS introduces a self-supervised graph-based approach to quantum architecture search, significantly reducing the need for labeled data and improving efficiency in designing quantum circuits for variational algorithms.
Contribution
The paper presents a novel self-supervised learning framework for quantum architecture search that leverages graph encoding of circuits, outperforming existing predictor-based methods with less labeled data.
Findings
Outperforms state-of-the-art predictor-based QAS methods.
Requires fewer labeled circuits to achieve high performance.
Effective in designing circuits for VQE and quantum state classification.
Abstract
Quantum Architecture Search (QAS) is a promising approach to designing quantum circuits for variational quantum algorithms (VQAs). However, existing QAS algorithms require to evaluate a large number of quantum circuits during the search process, which makes them computationally demanding and limits their applications to large-scale quantum circuits. Recently, predictor-based QAS has been proposed to alleviate this problem by directly estimating the performances of circuits according to their structures with a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is very time-consuming to obtain a large number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing · Quantum Information and Cryptography
