Papers › L2O-g^†: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor
L2O-g^†: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor
Yu-Chao Huang, Hsi-Sheng Goan
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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^†, a quantum-aware learned optimizer that leverages the Fubini-Study metric tensor (g^†) 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^† 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^† on just a single generic PQC instance. Our novel quantum-aware learned optimizer, L2O-g^†, presents an advancement in addressing the challenges of VQAs, making it a valuable tool in the NISQ era.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections