Learning Quantum States and Unitaries of Bounded Gate Complexity

Journal Article · 16 October 2024 · PRX Quantum

DOI: https://doi.org/10.1103/PRXQuantum.5.040306 · OSTI ID:2472453

Zhao, Haimeng ; Lewis, Laura ; Kannan, Ishaan ; Quek, Yihui ; Huang, Hsin-Yuan ; Caro, Matthias C.

While quantum state tomography is notoriously hard, most states hold little interest to practically minded tomographers. Given that states and unitaries appearing in nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with $G$ two-qubit gates to a small trace distance, a sample complexity scaling linearly in $G$ is necessary and sufficient. We also prove that the optimal query complexity to learn a unitary generated by $G$ gates to a small average-case error scales linearly in $G$ . While sample-efficient learning can be achieved, we show that under reasonable cryptographic conjectures, the computational complexity for learning states and unitaries of gate complexity $G$ must scale exponentially in $G$ . We illustrate how these results establish fundamental limitations on the expressivity of quantum machine-learning models and provide new perspectives on no-free-lunch theorems in unitary learning. Together, our results answer how the complexity of learning quantum states and unitaries relate to the complexity of creating these states and unitaries. Published by the American Physical Society 2024

• Sponsoring Organization: USDOE
• OSTI ID: 2472453
• Journal Information: PRX Quantum, Journal Name: PRX Quantum Journal Issue: 4 Vol. 5; ISSN 2691-3399; ISSN PQRUAG
• Publisher: American Physical SocietyCopyright Statement
• Country of Publication: United States
• Language: English