Concentration of Data Encoding in Parameterized Quantum Circuits

Understand

Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization.

• When applied to tasks involving classical data, such algorithms generally begin with quantum circuits for data encoding and then train quantum neural networks (QNNs) to minimize target functions.
• Although QNNs have been widely studied to improve these algorithms' performance on practical tasks, there is a gap in systematically understanding the influence of data encoding on the eventual performance.
• In this paper, we make progress in filling this gap by considering the common data encoding strategies based on parameterized quantum circuits.