This groundbreaking study reveals a fundamental trade-off in quantum machine learning between a model’s trainability and its privacy protection, demonstrating that quantum circuits with polynomial-sized dynamical Lie algebras (necessary for efficient training) inherently allow extraction of input data snapshots from gradients, while full input recovery depends on encoding circuit properties like high-frequency components and resistance to classical simulation.