Rethink the Role of Deep Learning towards Large-scale Quantum Systems
This work questions the necessity of deep learning for large-scale quantum system characterization, providing insights for more efficient resource use in quantum computing research.
The study systematically benchmarked deep learning (DL) models against traditional machine learning (ML) approaches for ground-state learning tasks in quantum systems up to 127 qubits, finding that ML models often perform comparably or better than DL models, with measurement features having minimal impact on DL predictions.
Characterizing the ground state properties of quantum systems is fundamental to capturing their behavior but computationally challenging. Recent advances in AI have introduced novel approaches, with diverse machine learning (ML) and deep learning (DL) models proposed for this purpose. However, the necessity and specific role of DL models in these tasks remain unclear, as prior studies often employ varied or impractical quantum resources to construct datasets, resulting in unfair comparisons. To address this, we systematically benchmark DL models against traditional ML approaches across three families of Hamiltonian, scaling up to 127 qubits in three crucial ground-state learning tasks while enforcing equivalent quantum resource usage. Our results reveal that ML models often achieve performance comparable to or even exceeding that of DL approaches across all tasks. Furthermore, a randomization test demonstrates that measurement input features have minimal impact on DL models' prediction performance. These findings challenge the necessity of current DL models in many quantum system learning scenarios and provide valuable insights into their effective utilization.