{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/l2o-g-dagger-learning-to-optimize","title":"L2O-$g^{\\dagger}$: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor","arxiv_id":"2407.14761","date":"2024-07-20","proceeding":null,"authors":["Yu-Chao Huang","Hsi-Sheng Goan"],"abstract":"Before the advent of fault-tolerant quantum computers, variational quantum algorithms (VQAs) play a crucial role in noisy intermediate-scale quantum (NISQ) machines. Conventionally, the optimization of VQAs predominantly relies on manually designed optimizers. However, learning to optimize (L2O) demonstrates impressive performance by training small neural networks to replace handcrafted optimizers. In our work, we propose L2O-$g^{\\dagger}$, a $\\textit{quantum-aware}$ learned optimizer that leverages the Fubini-Study metric tensor ($g^{\\dagger}$) and long short-term memory networks. We theoretically derive the update equation inspired by the lookahead optimizer and incorporate the quantum geometry of the optimization landscape in the learned optimizer to balance fast convergence and generalization. Empirically, we conduct comprehensive experiments across a range of VQA problems. Our results demonstrate that L2O-$g^{\\dagger}$ not only outperforms the current SOTA hand-designed optimizer without any hyperparameter tuning but also shows strong out-of-distribution generalization compared to previous L2O optimizers. We achieve this by training L2O-$g^{\\dagger}$ on just a single generic PQC instance. Our novel $\\textit{quantum-aware}$ learned optimizer, L2O-$g^{\\dagger}$, presents an advancement in addressing the challenges of VQAs, making it a valuable tool in the NISQ era.","url_abs":"https://arxiv.org/abs/2407.14761v2","url_pdf":"https://arxiv.org/pdf/2407.14761v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"l2o-g-dagger-learning-to-optimize","repo_url":"https://github.com/physics-morris/l2o-g","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}